Real-time and accurate detection of rockfalls on mining roads is critical for ensuring transportation safety, preventing geological hazards, and maintaining operational continuity in open-pit mining environments. Detecting rockfalls in such scenarios presents substantial challenges due to the small size of targets, irregular shapes, low contrast against the background, drastic illumination variations, and interference from adverse weather conditions such as fog, dust, and rain. Conventional single-modality visual detection methods, particularly those based solely on visible light imagery, often fail under low-visibility conditions, while existing dual-modality fusion approaches typically face limitations including complex network structures, high computational costs, and difficulties in simultaneously achieving real-time performance and high detection accuracy for small targets.

To address these challenges, this study proposes a novel progressive infrared–visible light fusion detection framework based on the Mamba state space model (SSM), which is specifically designed to efficiently integrate complementary information from infrared and visible light images while preserving fine-grained details of small targets, even under challenging environmental conditions such as low illumination, strong backlighting, partial occlusion, and adverse weather. The framework consists of three key components: 1) optimized feature extraction using MobileMamba and Wavelet Transform-based downsampling, which reduces feature loss while maintaining high-resolution details, 2) cross-modal feature fusion via the MambaFusion module, enabling adaptive integration of thermal and texture cues across multiple scales, and 3) small-object detection and localization enhancement through a Scale and Location Sensitive Loss (SLS Loss), which explicitly models scale variations and center-point offsets to improve both classification confidence and spatial precision for micro-scale rockfall targets. This multi-component design ensures robust detection in complex, unstructured mining environments and provides a balanced trade-off between computational efficiency and detection accuracy.

First, a Wavelet Transform-based WTSterm downsampling module is introduced to mitigate the loss of fine-grained features during feature extraction. Unlike conventional stride-based convolutional downsampling, the WTSterm module decomposes input images into multiple frequency components, including low-frequency approximations and directional high-frequency details. These components are concatenated along the channel dimension and projected through a 1×1 convolution, effectively preserving edge and texture information of small rock targets while reducing computational overhead. This module is integrated into an optimized MobileMamba backbone network, a lightweight visual SSM architecture that combines multi-kernel convolutions, multi-receptive field feature interaction, and wavelet-enhanced Mamba blocks. The MobileMamba backbone enables efficient extraction of thermal radiation cues from infrared images and rich spatial-texture features from visible light images, while selectively maintaining both shallow fine-grained details and deep high-level semantic features. To reduce computational redundancy, the network removes high-dimensional, small-scale feature branches that contribute little to small-object representation, thereby balancing feature richness and efficiency.

Second, a MambaFusion module is designed to progressively fuse multi-scale features from infrared and visible light modalities. The fusion process leverages Mamba’s dynamic state transition and gating mechanisms, which enable adaptive selection of discriminative information while suppressing modality-specific noise. MambaFusion combines the global dependency modeling ability of the SSM with local feature retention through convolution, facilitating cross-modal correspondence at semantic and spatial levels. Multi-scale fusion is performed across large, medium, and small feature maps, with adaptive weighting applied to emphasize salient information according to scene context. This mechanism allows the network to maintain robustness under challenging conditions such as strong backlighting, low illumination, partial occlusion, and airborne dust or smoke, enhancing both target discrimination and representation for small rockfall instances.

Third, to improve small-object detection and localization, the framework incorporates a Scale and Location Sensitive Loss (SLS Loss). This loss function explicitly models variations in target scale and center-point offsets, addressing the insensitivity of conventional losses (e.g., IoU, Dice) to size and position discrepancies. The scale-sensitive component reweights loss contributions according to the predicted and true target sizes, focusing training on underrepresented or mispredicted small targets. The location-sensitive component employs polar-coordinate quantification of center-point offsets, providing a strong penalty for positional errors and refining bounding box localization. Together, SLS Loss ensures that the network maintains high sensitivity to micro-scale rockfalls and achieves precise positioning in cluttered or unstructured road environments.

Extensive experiments were conducted on a self-collected open-pit mine rockfall dataset, containing diverse scenes with varying illumination, weather conditions, and target sizes (typically 10–50 cm in diameter). Results demonstrate that the proposed framework achieves precision of 0.856, recall of 0.715, and mAP@0.5 of 0.721, with an inference speed of 16 ms per frame. Compared with conventional single-modality detection and existing dual-modality fusion approaches, the proposed method significantly improves small-target detection performance and robustness while maintaining real-time computational efficiency. In conclusion, the progressive infrared–visible light fusion framework based on MobileMamba and MambaFusion modules effectively integrates complementary thermal and textural cues, preserves fine-grained details of micro-scale rockfall targets, and enhances small-object localization and discrimination in complex visual scenarios. By combining efficient feature extraction, adaptive cross-modal fusion, and a scale- and location-aware loss function, the proposed approach achieves high robustness under diverse environmental conditions, including low visibility, dynamic lighting, dust, and partial occlusion.

The method offers a reliable, high-performance solution for real-time rockfall detection and monitoring in open-pit mining environments, significantly contributing to safer road operation, reduced operational risk, and improved transportation efficiency. These results demonstrate the potential of lightweight state-space models combined with progressive multi-modal fusion and targeted loss functions for deployment in practical industrial safety and autonomous monitoring systems.

Photonics integrated circuits (PICs) have emerged as a foundational platform for next-generation optical information transmission and processing, owing to their high speed, broad bandwidth, excellent scalability, and low propagation loss. These advantages stem from the unique capability of PICs to densely integrate diverse and complex optical functionalities onto micro- and nanoscale chips. However, the efficient conversion of guided optical modes into free-space radiation propagating into the far field, together with precise and flexible manipulation of multiple optical degrees of freedom (DoFs)—including amplitude, phase, state of polarization (SoP), and orbital angular momentum (OAM)—remains a critical challenge in the field. Addressing this challenge is essential for unlocking the full potential of integrated photonic systems. In this context, the present review provides significant academic and technological value by systematically summarizing recent advances in on-chip metasurfaces for guided-mode radiation control. It fills an important gap in the existing literature by consolidating both theoretical frameworks and experimental demonstrations, while offering a comprehensive roadmap for overcoming key technical bottlenecks. By doing so, this review accelerates the translation of innovative metasurface concepts into practical PIC applications and supports the development of intelligent, high-performance optical systems.

Over the past decade, integration strategies for metasurface–waveguide architectures have evolved substantially. Early implementations typically involved placing a metasurface layer atop a conventional grating coupler. As demands for miniaturization and functional density increased, more advanced approaches emerged, most notably the direct patterning of high-aspect-ratio, high-refractive-index artificial nanostructures (meta-atoms) on waveguide surfaces. This strategy enables direct modulation of the evanescent field of guided modes, thereby facilitating comprehensive control over multiple DoFs of the radiated optical field. Recent progress in PICs and nanophotonics has further inspired innovative design paradigms, among which two representative approaches have attracted particular attention: co-designed metasurface–grating coupler hybrids and leaky-wave metasurfaces. The former offers a compact and fabrication-friendly solution, often realizable via a single etching step, in which the metasurface–grating coupler simultaneously enables efficient radiation and wavefront shaping. The latter exploits intentional symmetry breaking in photonic crystal slab waveguides to induce controlled leaky-wave radiation. Notably, both strategies can realize large surface-emitting apertures without requiring the integration of high-aspect-ratio, high-index nanostructures, making them especially attractive for scalable implementations. As a result, these two approaches are expected to play a central role in advancing the field.

Substantial progress has been achieved in manipulating guided-mode radiation using on-chip metasurfaces. Several well-established mechanisms have been proposed to control the phase of radiated wavefronts, including: 1) detour phase, determined by the lateral displacement of a meta-atom within a unit cell; 2) geometric (Pancharatnam–Berry) phase, governed by the in-plane rotation of anisotropic meta-atoms; 3) propagation phase, set by the geometric dimensions of the meta-atoms; and 4) resonant phase, arising from engineered resonances that provide strong and dispersive phase responses. Beyond phase-only modulation, interference among multiple artificial nanostructures within a unit cell enables simultaneous control of amplitude and SoP, thereby allowing full multidimensional manipulation of optical DoFs. By leveraging these mechanisms, on-chip metasurfaces have demonstrated a broad range of functionalities, including beam steering, focusing, vortex beam generation, and holographic projection. Furthermore, the inherent advantages of the on-chip platform—such as multi-port, multi-mode, and multi-wavelength excitation—enable decoupled and independent control of optical DoFs with minimal crosstalk, paving the way for advanced functional multiplexing in integrated photonic systems.

Despite these advances, several key challenges remain. First, the radiation efficiency of guided modes must be further improved. Future studies are expected to explore novel radiation-enhancement mechanisms, such as resonant architectures and multilayer composite structures, to increase energy conversion efficiency. Second, modulation precision is still constrained by current micro- and nanofabrication limitations. Advances in stacked multilayer metasurfaces, three-dimensional (3D) nanostructures, and high-precision, multi-material co-fabrication could significantly expand the available design space and improve device fidelity. Third, dynamic modulation capabilities remain limited. Most existing on-chip metasurfaces are passive and rely on static geometries, preventing adaptive responses to external stimuli such as temperature, optical intensity, or applied voltage. Future efforts may enable non-volatile reconfigurability through phase-change materials, achieve ultrafast modulation using the optoelectronic properties of two-dimensional (2D) materials, or expand modulation dimensionality via photochromic effects. Finally, current on-chip metasurfaces often lack sufficient system-level integration. Future research should therefore focus on the co-integration of metasurfaces with multi-functional photonic modules to realize intelligent, system-level optical field manipulation.

In conclusion, pioneering work on on-chip metasurfaces has significantly advanced the understanding of guided-mode radiation control and driven innovation in PICs and nanophotonics. With continued progress in nanofabrication technologies and electromagnetic modeling, on-chip metasurfaces are expected to achieve further performance improvements. As a result, metasurface-enabled control of guided-mode radiation is poised to become a cornerstone technology for next-generation PICs, with far-reaching impact across high-capacity optical communications, imaging systems, optical security, and immersive AR/VR applications.

As a core technology in the field of information security, image steganography holds irreplaceable strategic value in areas such as covert communication and digital copyright protection. Current adversarial image steganography methods commonly suffer from insufficient cross-model generalization capability and low quality of stego images. This is primarily due to the over-reliance of these methods on the decision boundaries of the target steganalysis model, which results in adversarial perturbations being highly coupled with the decision boundaries of the target model, making it difficult to generalize effectively to unknown models. Moreover, when generating adversarial perturbations, the fusion of secret information with the texture of the cover image is often insufficiently natural, especially in low-texture regions where statistical anomalies are prone to emerge, thereby compromising the visual quality and statistical naturalness of the stego images.

To address the aforementioned issues, this paper proposes a dual-strategy image steganography model that integrates passive defense and active attack. Through the synergistic end-to-end optimization training of cover image enhancement and adversarial embedding, the model significantly improves cross-model generalization capability and the quality of stego images. At the passive defense level, cover image optimization is performed: A dual-stream U-Net structure is introduced into the generator of the generative adversarial network (GAN). This dual-branch network simultaneously processes the original image and edge information derived from conformal monogenic phase congruency (CMPC). Combined with the SE-Net attention mechanism to optimize feature distribution, texture enhancement and detail preservation are achieved during adversarial training. By jointly optimizing the discriminator loss and the pixel-space mean squared error loss, an optimized cover image with high visual quality and statistical naturalness is generated, constructing a secure and robust cover environment for secret information embedding. At the active attack level, adversarial embedding is conducted: integrated gradients are utilized for neuron attribution to locate the critical discriminative features commonly relied upon by various models, followed by dynamic perturbation. The neuron attribution loss, together with the discriminator loss and pixel-space mean squared error loss, jointly optimizes the embedding strategy. This ensures the indistinguishability between the stego image and the original cover image while significantly reducing the detection accuracy of various steganalysis models. Considering the phased differences in the optimization objectives of the proposed model, the cover image optimization network dominates the training in the initial stage, as a high-quality cover image is the foundation of steganography. In the later stage, the adversarial embedding network takes the lead, continuously adjusting the embedding distortion by interfering with the discriminative logic of steganalysis models to enhance the concealment of the stego image. Therefore, a linear dynamic adjustment strategy is adopted, allowing the weights of the two components to transition smoothly and uniformly during the first 100 training epochs. This matches the requirement for phased dominance switching between the two networks, driving the model's performance to advance progressively by stages and achieving a global optimization of concealment, usability, and security.

In the loss function weight configuration experiment, considering the phased differences in the model's optimization objectives, a linear dynamic adjustment strategy was adopted to change the weight coefficients k₁ and k₂, while systematically investigating the effects of different configurations of λ₁ and the ratio λ₂:λ₃:λ₄. By comparing the quality of stego images and the detection accuracy of non-targeted steganalysis models, the optimal weight configuration was determined as: k₁ decreasing from 0.8 to 0.2, k₂ increasing from 0.2 to 0.8, λ₁=0.2, and λ₂:λ₃:λ₄=8:3:1. The ablation study, by comparing the ACC against non-targeted steganalysis models as well as the average PSNR and SSIM of stego-images under different architectures, demonstrates that the configuration of the proposed method achieves the best overall performance. Furthermore, comparing the effects of adding the SE-Net attention mechanism at different positions within the dual-stream U-Net network showed that adding it only during the encoding stage yielded the best results. In the experiment analyzing generated image quality and efficiency, the average PSNR and SSIM obtained from 1000 randomly selected stego images were 40.89 dB and 0.9783, respectively. Three sets of cover images and their corresponding stego images were randomly selected for comparison. The results indicated that the two types of images are not only visually indistinguishable but also exhibit highly similar histogram distributions. To compare generation efficiency, the average generation time for a single stego image was tested. Due to its complex network architecture, the proposed method is slightly less efficient in generation speed compared to other schemes, but it significantly enhances the quality of cover images. This demonstrates that the proposed method provides an effective guarantee for improving security and image quality at an acceptable efficiency cost. The cross-dataset generalization capability analysis experiment showed that the proposed method achieved the lowest average ACC, indicating good cross-dataset generalization ability. This is because the proposed dual-strategy model generates optimized covers with consistent structural properties and perturbs common discriminative features, effectively reducing the model's dependence on statistical features specific to a particular training dataset. Consequently, it exhibits stable and good anti-detection performance across different datasets. In the experiment on anti-steganalysis capability, a comparison was made with two state-of-the-art adversarial embedding methods (CR-AIS and Natias). The experimental results showed that, in terms of resilience against non-targeted steganalysis models, the proposed method achieved average detection accuracy (ACC) reduction margins of 3.69% and 1.91% compared to CR-AIS and Natias, respectively. This indicates that the proposed method possesses strong resilience against non-targeted steganalysis models and exhibits good cross-model generalization capability.

Through the deep integration of passive defense (dual-stream U-Net-guided cover optimization) and active attack (neuron attribution-driven adversarial embedding), the proposed image steganography model significantly reduces the detection accuracy of various steganalysis models, enhances cross-model generalization capability, and maintains high image quality, thereby achieving synergistic improvement in steganographic security and visual fidelity.

Forged parts are widely used as key load-bearing components in automotive, aerospace, shipbuilding, and heavy equipment industries. Their surface quality directly affects structural reliability and service safety. During forging and subsequent processing, surface defects such as cracks, inclusions, pores, oxide layers, and spot-like imperfections are frequently generated due to material heterogeneity, mold wear, and process instability. Magnetic particle inspection is commonly adopted for ferromagnetic forged parts because of its high sensitivity to surface and near-surface cracks. However, magnetic particle inspection images usually exhibit complex backgrounds, uneven illumination, magnetic trace interference, and weak texture contrast. These characteristics cause surface defects to present blurred boundaries, irregular shapes, and large scale variation. Slender cracks and tiny defects are especially difficult to detect accurately under real-time constraints. Existing convolutional neural network–based and Transformer-based detection models often show insufficient directional perception, limited contextual representation, and inadequate geometric sensitivity in bounding-box regression. As a result, detection accuracy and computational efficiency are difficult to balance in practical industrial inspection scenarios. The objective of this study was to develop a real-time forged-part surface defect detection model that enhances directional feature perception and contextual fusion capability, improves multi-scale feature alignment, and strengthens localization robustness for elongated defects, while maintaining high inference efficiency suitable for industrial deployment.

A real-time surface defect detection framework, named MCF-RTDETR, was constructed based on an improved RT-DETR architecture. Magnetic particle inspection images of heavy-duty truck steering knuckles were collected from an industrial production environment and manually annotated to establish a forged-part surface crack dataset. The dataset contained complex background structures and fine-scale defect patterns, providing realistic samples for model evaluation.

In the backbone network, a multi-gradient fusion convolution module was introduced to enhance directional edge representation. This module integrated four directional difference convolutions with one standard convolution to capture gradient responses along multiple orientations. Such design improved feature sensitivity to slender cracks and weakly textured defects. To avoid increased inference cost, convolutional re-parameterization was adopted during the inference stage, allowing the multi-branch structure to be equivalently transformed into a single convolution layer while preserving feature enhancement capability.

In the neck network, a context-aware recursive feature pyramid network was designed to address insufficient spatial–semantic alignment in conventional multi-scale fusion schemes. A rectangular self-calibration module was first employed to model directional contextual information by expanding receptive fields along horizontal and vertical directions, which strengthened anisotropic defect representation. Subsequently, a FuseBlockMulti module was used to realize semantic-guided alignment between shallow detail features and deep semantic features, enabling adaptive information interaction across different feature levels. Furthermore, a dynamic interpolation fusion mechanism was introduced to assign adaptive fusion weights to multi-scale features according to local contextual semantic strength. Through recursive fusion, feature consistency across scales was progressively refined, improving robustness under complex backgrounds and large scale variation.

For bounding-box regression, the original loss function was replaced with a Focaler-MPDIoU loss. This loss combined difficulty-aware sample weighting with multi-point distance geometric constraints, enhancing localization accuracy and boundary fitting ability for elongated and irregular defects. The entire detection framework was optimized in an end-to-end manner and evaluated under unified training and inference configurations.

Experimental results demonstrated that the proposed MCF-RTDETR achieved superior performance in forged-part surface defect detection. On the magnetic particle inspection dataset of heavy-duty truck steering knuckles, the proposed model obtained a mean average precision of 88.5%, which exceeded the RTDETR-r18 baseline by 2.3%. Meanwhile, computational complexity was reduced from 58.3 G to 33.5 G, and inference speed reached 137.2 f/s, indicating an effective balance between detection accuracy and computational efficiency.

Performance improvement was particularly evident for slender cracks and small-scale defects. Enhanced directional gradient perception reduced missed detections caused by weak edge responses, while context-aware recursive fusion suppressed background interference and improved defect-background discrimination. Ablation experiments confirmed the effectiveness of each proposed component. The multi-gradient fusion Convolution strengthened edge sensitivity without increasing inference overhead. The Context-Aware Recursive Feature Pyramid Network improved multi-scale feature consistency and spatial-semantic alignment. The Focaler-MPDIoU loss further enhanced bounding-box regression accuracy for defects with large aspect ratios.

Generalization capability was validated on the public GC10-DET dataset. The proposed model achieved a 2.8% increase in mean average precision compared with the baseline detector, demonstrating that the proposed feature enhancement and fusion strategies are transferable to different industrial surface defect detection scenarios. These results indicate that the proposed framework effectively integrates accuracy improvement with computational reduction, which is essential for real-time industrial inspection systems.

A context-aware multi-scale fusion real-time detection model for forged-part surface defects is presented. Directional gradient fusion, adaptive contextual feature integration, and geometry-sensitive regression jointly enhance detection robustness under complex backgrounds and severe scale variation. Experimental results confirm that the proposed approach improves detection accuracy while reducing computational complexity and maintaining high inference speed. The model shows strong generalization capability and practical applicability, providing an effective solution for automated forged-part surface defect inspection in industrial environments.

Weak signal detection under complex backgrounds is a key technology in fields such as medical healthcare, aerospace engineering, and other related areas. The phase-locked amplifier (LIA) can effectively extract the sinusoidal signal of a specified frequency from noise, featuring advantages of a narrow passband and high signal-to-noise ratio (SNR). However, traditional lock-in amplifiers face multiple issues including insufficient hardware scalability and limited software flexibility. Especially in precision measurement tasks requiring real-time adjustment of modulation parameters, the limitations of their fixed hardware architecture and single control logic become increasingly prominent. To address these problems, this paper proposes a programmable lock-in amplifier system based on the LabVIEW FPGA architecture, which realizes the collaborative work of hardware and software.

In terms of hardware, the system takes the XILINX XC7A200T FPGA chip as the hardware core, and constructs a highly reliable hardware platform with the ALINX AX7202 backplane and AC7200 core board. Meanwhile, the ADC module AN9238 (12-bit resolution, 65 MSps sampling rate) and DAC module AD9767 (14-bit resolution, 125 MSps sampling rate) are selected to ensure high fidelity of signal acquisition and output.

In terms of software, with the LabVIEW graphical programming platform as the core, the system can flexibly configure the entire signal processing workflow. For the modulation and demodulation function: first, high-throughput function operations are innovatively adopted to realize waveform generation. This scheme is based on a 120 MHz clock-driven timing cycle structure, implements the accumulator function via a shift register, and dynamically generates waveform data using high-throughput multiplication, division, and sine function operations. This not only reduces FPGA resource consumption but also achieves flexible phase configuration and minimized data delay, laying the foundation for the dynamic optimization of subsequent modulation parameters. Second, in the phase-sensitive detection (PSD) module, weak signal demodulation and extraction are realized based on the mixing principle: the signal under test collected by the ADC is multiplied with the reference signal generated internally in the FPGA, and useful signals are initially separated from noise by leveraging the characteristics of coherent same-frequency signals and non-coherent noise. According to phase-locked amplification theory, when the frequency of the signal under test matches that of the reference signal with a fixed phase difference, multiplication generates a DC component and a frequency-doubled component. The DC component is proportional to the amplitude of the signal under test, providing a theoretical basis for signal extraction. Finally, in the filtering module, a cascaded design of a mean filter and Butterworth low-pass filter is adopted: the mean filter is configured with a 60-sample window, performing signal smoothing within a single-cycle timing loop to initially filter out high-frequency random noise; the subsequent 4th-order Butterworth filter adopts a cutoff frequency configuration strategy with a bandwidth margin of two to three times, further suppressing residual high-frequency components without signal distortion, thus achieving an optimal balance between SNR and response speed.

In the information transmission part, the UDP communication IP core of the host computer is invoked, which is interconnected with the physical pins and FIFO buffer in the FPGA through a four-wire handshaking mechanism. The host computer software is developed based on LabVIEW, featuring a comprehensive human-machine interface (HMI) that supports real-time configuration of core parameters (modulation frequency: 5 Hz–16 kHz adjustable; signal amplitude; phase: 0°–360° continuously adjustable; filter order) and provides a real-time waveform display function, significantly improving the real-time performance and stability of signal processing.

To verify the feasibility and practical value of the system, this paper deeply integrates the programmable lock-in amplifier with TDLAS technology to construct a high-precision trace oxygen detection system. A laser with a central wavelength of 760.59 nm and a Herriott-type multi-pass gas absorption cell are employed. First, a modulation amplitude optimization experiment is conducted, constructing a 0.3–1.8 V modulation coefficient gradient test under the conditions of 5 Hz scanning frequency, 16 kHz modulation frequency, 210° relative phase, and 200 Hz filter bandwidth. The results verify that the relationship between the second harmonic amplitude and modulation amplitude meets expectations, i.e., an optimal modulation amplitude exists. Second, concentration calibration and linearity verification experiments are performed: with a 5 Hz scanning frequency and 16 kHz laser modulation frequency, standard oxygen samples with concentrations ranging from 0.02% to 15% are prepared, and real-time detection and calibration of the second harmonic signals are conducted. The fitting correlation coefficient (R²) between oxygen concentration and the second harmonic signal is approximately 0.999, verifying the system’s feasibility and practicality. Finally, detection limit analysis is carried out, and calculations show that the system’s oxygen detection limit is 2.8×10⁻⁵.

This lock-in amplifier has the advantages of high integration, fast detection speed, and high SNR, making it suitable for on-site detection of trace oxygen. This design can exhibit significant advantages in spectral analysis scenarios requiring multi-parameter continuous adjustability and high real-time performance, promote the application and development of weak signal detection technology, enrich the implementation pathways of digital lock-in amplification technology, and provide a new design idea for the development of high-flexibility weak signal detection systems.

In the production process of printed circuit boards (PCBs), common defects such as mouse_bite, open_circuit, short, spur and spurious_copper are frequently encountered. These defects are characterized by their small size, color similarity to the background, and similar features across different defect types, which make detection challenging and prone to false positives and missed detections. Traditional inspection methods, including manual visual checks and conventional automated optical inspection (AOI), suffer from high error rates, limited adaptability, and dependency on manually selected features. Although deep learning approaches, particularly YOLO-series models, have improved detection robustness, they still face three core limitations in PCB defect inspection: insufficient feature extraction for tiny objects, poor balance between model lightweight and accuracy, and inadequate loss function design for small targets. To address the issues of limited samples, low efficiency, large parameter size, and low accuracy in detecting tiny-defects on PCBs, this paper proposes a lightweight detection model named PCB-YOLO based on the YOLOv8n.

First, the original high-resolution PCB images with diverse background colors are converted to grayscale to minimize color interference. To avoid losing tiny defects during resizing, a sliding-window slicing strategy is applied, generating 640 pixel×480 pixel sub-images with 100 pixels overlaps. Only slices containing defects are retained, effectively increasing the usable dataset while preserving small-object details. Second, considering the significant proportion of small target defects in PCBs, a Conv_SWS (SimAM with slicing) module is designed by integrating the SimAM attention mechanism and merging slicing operations. The input feature map is divided into local patches, each enhanced by SimAM to emphasize small-defect regions, before being recombined. This module replaces standard convolution in downsampling stages, enhancing multi-scale feature extraction without adding parameters. Third, the original C2f module in YOLOv8 is modified by introducing partial convolution (PConv) to comprehensively compute key feature channels, resulting in the design of the FBC2f module. This enhancement improves feature extraction capability while reducing the number of model parameters. Finally, the standard IoU loss is sensitive to minor localization errors and fails when boxes do not overlap. Thus, a combined loss using normalized Wasserstein distance (NWD) and IoU is proposed. NWD models bounding boxes as 2D Gaussian distributions and measures similarity via Wasserstein distance, providing stable gradients even for non-overlapping tiny objects. The final loss is a weighted sum of NWD and IoU, optimized with a balancing factor α=0.5. The model is trained and evaluated on an augmented PCB defect dataset containing 5172 images with 6282 defect instances. Performance is measured using mAP50, mAP50-95, parameters, FLOPs, and frame per second (FPS). Comparative experiments include ablation studies, attention mechanism comparisons, loss function analysis, and benchmarking against state-of-the-art detectors.

Experimental results demonstrate the effectiveness of PCB-YOLO: it achieves 95.6% mAP50 on the PCB defect dataset, outperforming YOLOv8n by 2.9%. It also reduces parameters by 23.6% (from 3.006 M to 2.302 M), FLOPs by 22.2% (from 8.1 G to 6.3 G), and model weight by 1.3 M. Moreover, ablation studies are finished to evaluate each proposed component contributes positively: the Conv_SWS module boosts recall by 5% and mAP50 by 2.4%; the FBC2f module reduces parameters by 23.5% with a moderate mAP50 trade-off. the NWD-IoU loss increases mAP50 by 2.3% and mAP50-95 by 2.8%. The full integration of all three modules achieves the best balance of accuracy and efficiency. Comparative evaluations demonstrate that PCB-YOLO outperforms Faster R-CNN, YOLOv5n, YOLOv10n, YOLOv11n, YOLOv12n, and the recently introduced lightweight PIC2f-YOLO in terms of both accuracy and model size. Additionally, it achieves a real-time inference speed of 160 f/s on GPU and 50 f/s on CPU, meeting the throughput demands of industrial applications. Moreover, PCB-YOLO also demonstrates strong generalization on external datasets, attaining mAP50 scores of 98.1% on the industrial aluminum surface defects dataset and 93.6% on the PKU-Market-PCB dataset, thereby verifying its robustness and transferability. Visual comparisons reveal that PCB-YOLO substantially decreases the rates of missed detections and false alarms relative to YOLOv8n, especially in the case of minor defects like shorts and mouse bites. In summary, the results validate that the Conv_SWS module effectively preserves small-object features during downsampling, the FBC2f module maintains representational capacity while reducing computation, and the NWD-IoU loss stabilizes training and improves localization for miniature targets.

This paper proposes PCB-YOLO, a lightweight and accurate detection model designed for tiny PCB defects. The approach integrates grayscale transformation and adaptive slicing during preprocessing to enhance input data quality. It introduces the Conv_SWS module to improve small-object feature retention and designs the parameter-efficient FBC2f module. Additionally, the NWD-IoU loss is adopted to refine the regression of minor defects. Together, these contributions address key challenges in PCB inspection, including limited sample availability, background variation, small defect size, and the accuracy–complexity trade-off. Experimental results show that PCB-YOLO achieves higher detection accuracy with fewer parameters and faster inference than current leading models. It also demonstrates strong generalization across diverse industrial defect datasets. The framework offers a practical, deployable solution for real-time, high-precision PCB quality inspection in manufacturing. Future work may explore further architectural optimizations and extend the method to other micro-defect detection domains.

In the context of the information age, massive data growth has posed severe challenges to traditional storage technologies such as magnetic storage and hard disks, which are limited by low storage density, high energy consumption, and short service life, failing to meet the long-term and low-cost storage demands of large-scale datasets. Holographic data storage, as a promising optical storage technology with advantages of high density, long lifespan, and high speed, has become a key solution to address this bottleneck. Deep learning can be used to combine the holographic storage system to demodulate the complex amplitude information. This paper focuses on the correlation mechanism between the diffraction distance, diffraction intensity image, and deep learning decoding in the holographic storage system, and explores the differences in the representation of complex amplitude information by intensity images under different diffraction distances and the influence patterns on the deep learning model.

To achieve the research objectives, firstly, a theoretical analysis was conducted on the relationship between the oversampling rate and the diffraction distance. The non-interference complex amplitude decoding holographic storage system was emphasized, and the optimal diffraction distance of 20 for the oversampling rate was explored under this system. A decoding framework based on deep learning was constructed, leveraging the powerful image feature extraction and mapping capabilities of the U-Net neural network. Intensity diffraction images corresponding to different diffraction distances were collected, which contained the implicit phase and amplitude information of the complex amplitude data pages. These intensity diffraction images were used to train the U-Net model to explore the intrinsic correlation between the diffraction distance and the feature description of the complex amplitude. Additionally, by adjusting the oversampling rate parameters, the relationship between the oversampling rate and the diffraction distance was analyzed, and the symbol error rate (SER) decoding performance indicators were statistically compared under different experimental conditions to verify the relationship between the oversampling rate and the diffraction distance.

Firstly, it clarifies the correlation rule between oversampling rate and diffraction distance: the higher the oversampling rate, the longer the diffraction distance required to obtain an intensity diffraction pattern with a specific contrast. This is because a higher oversampling rate leads to a more compact distribution of spectral information, thus requiring a longer diffraction distance to separate the complex amplitude features hidden in the intensity image. Secondly, under specific conditions (oversampling rate of 20 and diffraction distance of 1.4 millimeters), the complex amplitude features carried by the intensity diffraction pattern match highly with the feature extraction mechanism of the U-Net neural network. This matching effect improves the efficiency of feature mapping of phase and amplitude information during model training, and makes the SER approach 0 in the early stages of iteration, which is superior to the decoding performance under other diffraction distance conditions. Finally, the oversampling rates of 10 and 30 correspond to the optimal diffraction distances of 0.4 mm and 3.1 mm respectively, further verifying the correlation rule between oversampling rate and diffraction distance elucidated in this paper.

This paper focuses on the core correlations between the diffraction distance, intensity diffraction pattern, and the specific Unet deep neural network decoding in the holographic storage system. It particularly explores the intrinsic relationship between the oversampling rate and the diffraction distance, and conducts quantitative analysis on the optimal diffraction distance under specific parameter combinations. The correlation rule between the oversampling rate and the diffraction distance is clarified: the higher the oversampling rate, the greater the diffraction distance required to obtain the specific contrast intensity diffraction pattern. This rule and the quantitative results of the optimal diffraction distance provide specific references for the selection of diffraction distance and parameter optimization in similar holographic storage systems, and lay the foundation for subsequent research on different encoding schemes and storage media.

Remote sensing images are formed by capturing the reflection or radiation information of electromagnetic waves from ground objects, reflecting the spatial distribution and semantic features of these objects. Remote sensing image target detection holds significant application value in fields such as environmental monitoring, military defense, and land resource management. However, due to the complex background of remote sensing images, significant variations in target scales, and a large number of small targets, existing target detection algorithms still exhibit deficiencies in feature extraction, multi-scale feature fusion, and model complexity control, making it difficult to simultaneously achieve both detection accuracy and computational efficiency. To address the aforementioned issues, this paper proposes an improved RTDETR remote sensing image target detection method based on an analysis of the structure and performance characteristics of the RTDETR algorithm. The aim is to enhance the model's ability to detect multi-scale targets and reduce model complexity while maintaining detection accuracy.

This article proposes an improved RTDETR remote sensing image object detection method to address the problems of insufficient feature extraction ability, limited multi-scale object modeling ability, and high model complexity in remote sensing image object detection tasks. Firstly, in terms of backbone network design, a lightweight backbone network StarNet is adopted to replace the original ResNet18. StarNet maps input features to a high-dimensional nonlinear feature space through element level multiplication, which can generate richer feature representations and enhance the model's ability to express complex remote sensing scenes. Secondly, to enhance the modeling ability of the model for targets of different scales, a deformable attention mechanism is introduced in the AIFI module of the efficient hybrid encoder to replace the original standard attention mechanism. The deformable attention mechanism combines the idea of sparse attention with dynamic shift strategy. By learning variable sampling positions, attention allocation becomes more flexible, allowing for adaptive attention to target regions of different spatial positions and scales. In addition, a bidirectional feature pyramid network module is introduced in the feature fusion stage of the efficient hybrid encoder. This module achieves full interaction and fusion of feature information at different levels through a bidirectional feature transfer mechanism from top to bottom and from bottom to top, effectively enhancing the model's ability to comprehensively utilize multi-scale features and further improving the detection performance of targets at different scales in remote sensing images. To verify the effectiveness of the proposed method, this paper conducted ablation experiments and comparative experiments on the DIOR remote sensing image dataset. At the same time, in order to evaluate the generalization ability of the algorithm in practical remote sensing application scenarios, further generalization analysis experiments were conducted on the RSOD dataset to reduce the risk of overfitting and verify the robustness of the model.

The experimental results show that the proposed improved algorithm has achieved significant performance improvement on the DIOR dataset. Among them, the accuracy and recall of the algorithm increased by 1.8% and 3.0% respectively, and the mAP50 and mAP50-95 indicators increased by 1.8% and 1.7% respectively. While improving detection performance, the parameter and computational complexity of the model were reduced by 37.7% and 32.5%, respectively, indicating that the proposed method significantly reduces model complexity while ensuring detection accuracy, achieving an effective balance between accuracy and efficiency. To further verify the generalization ability of the algorithm, this paper conducted generalization analysis experiments on the RSOD dataset. The experimental results showed that the improved algorithm improved the mAP50 and mAP50-95 metrics by 1.6% and 0.5%, respectively, while reducing the parameter count by 37.9%. The above results indicate that the method has good robustness and generalization performance on different remote sensing datasets, and can effectively alleviate overfitting problems. In addition, the detection performance of the algorithm proposed in this article was compared with various mainstream object detection algorithms on the DIOR dataset. The experimental results showed that the algorithm proposed in this article achieved the best performance in terms of average detection accuracy, showing significant advantages compared to other comparative algorithms. The main reason for the performance improvement lies in the synergistic effect of the introduced deformable attention mechanism and the weighted bidirectional feature pyramid network. The deformable attention mechanism can adaptively focus on key information regions at different scales through flexible attention allocation strategies, thereby enhancing the modeling ability of the model for local details and multi-scale features; The weighted bidirectional feature pyramid network achieves full integration of low-level detail features and high-level semantic features through a bidirectional information flow mechanism, effectively enhancing the ability to express multi-scale features. In terms of lightweight performance, the parameter and computational complexity of the improved algorithm are 12.4 M and 38.5 G, respectively, and the overall algorithm complexity is lower than other compared algorithms. This is mainly due to the adoption of the StarNet backbone network, which maps input features to a high-dimensional nonlinear feature space through element level multiplication in star operations. While generating rich feature representations, it significantly reduces model parameters and computational overhead, thereby enhancing the practicality and deployment value of the algorithm in remote sensing image object detection tasks.

This paper addresses the challenge of balancing accuracy and efficiency in remote sensing image object detection by improving the RTDETR algorithm and proposing a target detection method that considers both detection performance and model lightweighting. By introducing the StarNet backbone network, deformable attention mechanism, and bidirectional feature pyramid network, the model's ability to model and fuse features of complex scenes and multi-scale targets is enhanced. Experimental results show that this method achieves good detection performance on multiple remote sensing datasets and significantly reduces model complexity, validating its effectiveness and generalization ability. This research provides a reference for the deployment of remote sensing object detection algorithms in practical applications and resource-constrained platforms. In the future, we will further explore more efficient feature modeling methods to enhance the algorithm's applicability in complex remote sensing scenes.

Skin lesions are often accompanied by complex features such as blurred boundaries, variable shapes, and hair occlusion, which makes it difficult to accurately locate and finely characterize the lesion area in the segmentation task. At the same time, existing methods often have problems such as large number of parameters and high computational complexity, making them difficult to deploy on resource-constrained terminal devices. To this end, this paper proposes a novel lightweight skin lesion segmentation network based on dilated gating and adaptive bridging mechanisms.

The proposed method is designed to strike a balance between efficiency and segmentation performance, addressing the need for high-precision medical image analysis under resource-constrained environments.Specifically, a Dilated Gated Perceptual Enhancement (DGPE) module is introduced to improve both local detail modeling and global contextual understanding. This module employs a channel decoupling strategy combined with parallel depthwise separable convolutions of varying dilation rates to extract features at multiple scales. By integrating a gating attention mechanism and a progressive perception structure, the model effectively suppresses redundant background information and enhances edge structure preservation. To further bridge the semantic gap between encoder and decoder features, an Adaptive Feature Bridging (AFB) module is constructed. This module incorporates a dual attention mechanism—including Channel Attention Bridge (CAB) and Spatial Attention Bridge (SAB)—to perform cross-level feature calibration and establish global receptive field associations. In addition, a Cross-Granularity Feature Integration (CGFI) block is introduced to merge multi-resolution semantic features, enhancing the model’s capability to handle complex lesion structures with varying shapes and sizes. Furthermore, a Lightweight Initial Structure-Guided (LISG) module is developed to enhance shallow feature extraction. It integrates standard convolutions with large-kernel depthwise separable convolutions, forming long-range spatial dependencies in the early stages of the network. This is further supported by residual connections and cascaded small-kernel convolutions, ensuring effective gradient flow and feature stability across network layers. Lastly, an Efficient Channel Attention (ECA) mechanism is incorporated to model channel-wise dependencies using lightweight, adaptive one-dimensional convolution, effectively mitigating information loss during upsampling and preserving feature consistency throughout the decoding process.

Taking the ISIC2017 dataset as an example, our proposed method achieves mIoU and DSC metrics of 79.81% and 88.73%, respectively, representing performance improvements of 2.83% and 1.74% compared to the classic U-Net model. Simultaneously, the number of model parameters is significantly reduced by 25 times, and the computational complexity is reduced by 62 times, fully demonstrating its excellent computational efficiency while maintaining accuracy. Experimental results show that TransFuse and MHorUNet achieve mIoU of 79.21% and 79.37%, and DSC metrics of 88.40% and 87.99%, respectively, performing better than other methods compared in our paper. However, this is inseparable from their enormous computational burden. Compared to TransFuse, our proposed method reduces the number of parameters and computational complexity by 87 times and 51 times, respectively, significantly alleviating the hardware burden. Compared to MSS-UNet, another lightweight model, our proposed method reduces the number of parameters by 0.03M, a negligible difference, but achieves a 72-fold reduction in computational complexity. Even with the advancements in parameter compression achieved through dual-space shift MLP modules and external attention, MSS-UNet's computational complexity remains significantly higher than our proposed method, indicating room for improvement in computational efficiency. Furthermore, its high computational load may lead to overfitting, impacting model performance. In contrast, while UNeXt-S boasts a lower computational complexity of only 0.10 GFLOPs, its mIoU and DSC metrics are only 78.26% and 87.80%, respectively, showing a significant gap in segmentation accuracy. Its performance in other metrics is also inferior to our proposed algorithm. Our proposed method, in comparison, improves mIoU by 1.55% and DSC by 0.93%, demonstrating a superior balance between performance and efficiency.Comparative experiments show that our proposed method achieves an advanced balance between parameter count, computational complexity, and segmentation accuracy. Specifically, compared to traditional CNN-based medical segmentation models, our proposed method significantly reduces model redundancy and achieves more efficient feature extraction and fusion through structural design optimization. Compared to Transformer-based hybrid models, our method improves accuracy while significantly reducing computational resource consumption, thus alleviating deployment challenges. Furthermore, compared to similar lightweight models, our method not only ranks among the best in efficiency but also maintains a higher level of segmentation accuracy.

To address the common problems of insufficient segmentation accuracy and redundant model parameters in current skin lesion image segmentation methods, this paper proposes a lightweight skin lesion segmentation network based on dilatational gating and adaptive bridging. A dilatational gating perception enhancement module (DGPE) is designed, combining split dilatation units, gated attention units, and progressive perception enhancement units to fully mine multi-scale features and co-model global-local information, effectively mitigating the loss of feature details. An adaptive feature bridging module (AFB) is constructed, utilizing channel attention, spatial attention, and cross-granularity feature collaboration mechanisms to enhance effective interaction and fusion between cross-layer features, improving the accuracy of semantic information representation. A lightweight initial structure guidance module (LISG) is proposed in the initial stage of the network to improve the spatial representation ability and low-level semantic perception of shallow features, providing more robust guidance information for deep representation. Finally, efficient channel attention (ECA) is embedded in the upsampling stage, capturing the correlation features between key channels without increasing model complexity, and strengthening the localization ability of edge regions. This paper conducts a systematic evaluation on four publicly available datasets. Extensive experiments conducted on the ISIC2016, ISIC2017, and ISIC2018 datasets demonstrate the superior performance of the proposed model. It achieves mIoU scores of 85.78%, 79.81%, and 80.65%, and DSC scores of 91.90%, 88.73%, and 89.37%, respectively. With only 0.30 M parameters and 0.22 GFLOPs, the model significantly outperforms existing methods in terms of accuracy and efficiency, showcasing strong potential for real-world deployment in mobile medical applications and intelligent diagnostic systems, especially under constrained computing environments and limited storage conditions. Experimental results show that the proposed method significantly reduces the number of parameters and computational overhead while ensuring high segmentation accuracy. Its overall performance outperforms existing mainstream comparative models. Ablation experiments further validate the effectiveness and synergistic advantages of each module. This study provides a more reliable solution for the analysis of skin lesion images in clinical settings. Future work will explore integrating more feature enhancement strategies and structural optimization methods to further improve the model's generalization ability and make it applicable to research on other diseases.

In recent years, various devices for monitoring lower limb movement have emerged. Electrical sensors are susceptible to electromagnetic interference, while visual signals are affected by light intensity. Fiber Bragg Grating (FBG) sensors, on the other hand, have been widely adopted due to their excellent linearity, high sensitivity, flexibility, and strong anti-interference characteristics. During lower limb movement, the forces on the toes and heels of the foot continuously change, and the forces on the calf and thigh also fluctuate. However, research on the integrated monitoring of foot, calf, and thigh movements remains limited. Therefore, the development of a lower limb exoskeleton monitoring device is significant.

This study first established a theoretical model based on kinematics and verified its feasibility through theoretical analysis and simulation. Subsequently, a lower limb exoskeleton and its monitoring sensor unit were designed. Calibration experiments were conducted on the lower limb sensor unit using a tensile testing machine. The force sensitivities of all sensing units were obtained above 9.9 pm/N, and the goodness of fit values were all above 0.9906, demonstrating that the sensing units possess excellent monitoring performance. The exoskeleton provides support for the wearer, and the sensor unit integrated within it successfully monitors lower limb movements. After the volunteer wore the exoskeleton, motion monitoring experiments were conducted. The resulting data showed periodic changes, consistent with the conclusions drawn from the theoretical model and simulation analysis. The device features a simple power system that provides a certain level of assistance. Furthermore, the system includes an upper-level computer to analyze the monitored data and provide warning feedback based on the movement state.

In the theoretical model, as the angles of the three segments of the lower limb increase, the wavelength of the corresponding sensor units either increases or decreases. The relationship between step length and step height in the simulation analysis aligns with theoretical predictions. Additionally, dynamic analysis of the simplified structure further verified the accuracy of the step length-step height relationship. Finally, experimental data from the volunteer wearing the exoskeleton exhibited periodic changes, with stable mean values and standard deviations for the lower limb regions. The error between the theoretical and actual step length-step height was approximately 10%. The average value of the lower limb gait rhythm index (J) was 0.9379. These results confirm that the lower limb exoskeleton monitoring device meets monitoring requirements without negatively impacting the monitoring process and can accurately track the volunteer's lower limb movements. From a technical perspective, this study has made significant progress in sensing unit design and system integration. The advantages of FBG sensors lie not only in their high sensitivity and anti-interference capability but also in their suitability for multi-point synchronous monitoring due to their distributable embeddability and wavelength-encoding characteristics. In this system, by arranging sensing units at corresponding positions on the sole, ankle joint, knee joint, and thigh, a monitoring network covering key lower limb movement nodes was constructed. This distributed layout can capture the biomechanical changes during various phases of the gait cycle, providing multidimensional data for gait analysis and motor dysfunction assessment. In terms of system integration, the exoskeleton structure not only serves as a carrier for the sensing units but its mechanical design also fully considers ergonomics and movement adaptability. Lightweight hinge structures are used at the joints to reduce movement constraints while ensuring support stiffness. The power system employs lightweight electric actuators that primarily provide assistive torque during the gait propulsion phase, reducing the wearer's walking energy expenditure. When gait abnormalities (such as foot dragging or excessively high left-right asymmetry) or sudden force changes are detected, the system can provide alerts through sound or interface flashes, possessing preliminary gait anomaly warning capabilities.

Looking at the application prospects, this FBG-based lower limb exoskeleton monitoring device holds potential not only for motion analysis or assistance in healthy populations but also, and more importantly, in the field of rehabilitation medicine. It can provide objective and continuous gait parameter monitoring for patients after lower limb surgery or those with post-stroke sequelae, helping rehabilitation therapists quantitatively assess recovery progress. Furthermore, it could potentially integrate with functional electrical stimulation or robotic-assisted training to form an intelligent rehabilitation training system. Additionally, in specialized operational fields (such as firefighting or loaded marching), this technology could be used to monitor operators' fatigue states and prevent sports injuries. In summary, this study successfully designed and validated a lower limb exoskeleton monitoring device integrated with FBG sensing technology. The device achieves synchronous, anti-interference monitoring of multi-part lower limb movements with reliable data, while the exoskeleton structure provides a certain level of assistance during monitoring. The experimental results confirm their effectiveness, laying a solid foundation for subsequent research. Future work will focus on algorithm optimization, system lightweighting and portability, as well as clinical validation and functional expansion for specific application scenarios, aiming to ultimately transition the device from a laboratory prototype to a practical product.