Changes in retinal vascular morphology serve as crucial indicators for diagnosing various diseases, such as diabetic retinopathy, hypertension, and cataracts. Traditional diagnosis of retinal diseases primarily relies on manual assessment by clinicians, typically involving the analysis of retinal vascular curvature, width, and branching patterns based on imaging results from fundus examinations, optical coherence tomography, and fluorescein angiography. However, these imaging outcomes are susceptible to interference from factors such as illumination and noise, which increase the difficulty for clinicians in distinguishing retinal vessels from the background. Furthermore, inherent ocular structures like the optic cup and optic disc regions, as well as pathological alterations such as hard exudates, soft exudates, and the macular area, can also impede clinicians' judgment. Diagnostic results often vary subjectively among different physicians, potentially leading to misdiagnosis due to insufficient experience or fatigue. Automated retinal vessel segmentation technology can not only significantly reduce the time and effort required for manual annotation by clinicians and improve diagnostic efficiency but also minimize the impact of human factors to the greatest extent, providing more objective results to assist physicians in decision-making. Therefore, developing an accurate and automated retinal vessel segmentation technique holds substantial practical significance.

In the field of retinal vessel segmentation, existing methods can be broadly categorized into unsupervised and supervised approaches. Unsupervised methods do not rely on manually annotated data and primarily achieve segmentation by analyzing the morphological features of vessels. Common techniques include matched filtering-based, edge detection-based, and model-based methods. Matched filtering methods mainly utilize Gaussian kernel functions or their variants, which resemble the cross-sectional shape of blood vessels, to construct two-dimensional linear filter banks. These methods enhance tubular structures in retinal images through multi-scale and multi-directional filter responses, combined with thresholding for preliminary detection of vessel pixels. Edge detection-based methods capture boundary features between vessels and the background by calculating gradient changes in pixel intensity or using differential operators such as Canny and Sobel, often integrated with local or global thresholding strategies to extract vascular contours with continuous topological structures. Model-based methods rely on geometric or statistical models, such as active contour models, level set methods, or vascular tubular models, to mathematically describe vessel structure and distribution. They iteratively optimize energy functionals or probabilistic fitting processes to gradually evolve fine segmentation boundaries that adaptively conform to vessel morphology. However, such methods often depend on manually designed feature extraction pipelines, making it difficult to fully capture the complex topological structures of vessels, thereby resulting in limited segmentation performance. In contrast, supervised methods require expert-annotated data as training samples to achieve higher accuracy in vessel segmentation tasks. These methods can be further divided into traditional machine learning methods and deep learning methods. Traditional machine learning methods typically involve two steps: manual feature extraction and classification. Handcrafted features mainly include edge responses, wavelet responses, principal component information, and color intensity information. Based on the choice of classifier, they can be roughly categorized into k-nearest neighbors, random forests, support vector machines, and Gaussian mixture models. Traditional machine learning methods extract segmentation targets based on vascular features and fundus image characteristics. Compared to manual screening, they offer advantages such as shorter processing time and faster segmentation. However, their feature extraction process still requires manual design and cannot adequately represent the multi-scale structural features of vessels. With the continuous advancement and development of deep learning, it has gradually been adopted across various tasks in computer vision. In the field of medical image analysis, segmentation algorithms have been continuously updated and iterated. The introduction of various deep learning models and modules has further improved the accuracy and efficiency of fundus vessel segmentation algorithms. Compared to traditional machine learning methods, deep learning methods can automatically learn optimal features and achieve predictive modeling without human intervention, offering higher segmentation efficiency. Nevertheless, challenges such as limited representation of fine vessels, interference from complex backgrounds, and discontinuities in vessel structures remain. To address the aforementioned issues, this paper proposes a retinal vessel segmentation algorithm based on Grouped Channel Transformer and Modulated Fusion. First, a dual-dimensional cooperative attention module is designed in the encoder to enhance the distinguishability of vessel features and improve the model's accuracy in segmenting microvessels. Second, a Grouped Channel Transformer module is applied to construct a dual-encoder architecture, establishing global dependencies to effectively capture detailed vessel information. Finally, a Modulated Fusion module is introduced at the decoder to enhance feature fusion between the encoder and decoder, thereby improving the model's robustness against noise interference.

Experiments conducted on the DRIVE, CHASE_DB1, and STARE datasets demonstrate that the proposed algorithm achieves accuracy rates of 0.9711, 0.9762, and 0.9758, sensitivities of 0.8026, 0.8219, and 0.8042, and specificities of 0.9873, 0.9866, and 0.9899, respectively. The proposed method outperforms other algorithms in overall segmentation performance, demonstrating superior segmentation capability.

GTMF-Net demonstrates robust vessel segmentation capabilities on the DRIVE, CHASE_DB1, and STARE datasets. It achieves a favorable balance between model complexity and segmentation performance, offering both relatively low computational cost and high segmentation accuracy. However, the algorithm currently exhibits limitations in feature extraction within low-contrast regions, which can lead to mis-segmentation. Future work will focus on optimizing the algorithm's design to improve the accuracy of vessel identification in pathological areas.

Remote sensing image target detection plays a crucial role in a wide range of practical applications, including resource exploration, urban planning and management, environmental monitoring, and disaster emergency response. With the rapid development of high-resolution sensors and large-scale data acquisition technologies, the demand for accurate and efficient object detection methods has increased significantly. However, targets in remote sensing images are typically characterized by small size, large scale variations, complex and cluttered backgrounds, blurred boundaries, and insufficient texture information, which significantly increases the difficulty of effective feature extraction and reliable detection. In addition, factors such as imaging altitude, atmospheric interference, and illumination variations further degrade image quality, resulting in low contrast between targets and the surrounding environment. Despite substantial progress in recent years, small target detection in remote sensing imagery still faces several critical challenges. From a localization perspective, commonly used metrics such as Intersection over Union are overly sensitive to slight variations in bounding boxes when applied to small targets, leading to unstable regression optimization and reduced localization accuracy. From a data perspective, the limited number of small target samples and the imbalance in category distribution often cause models to underrepresent small object features, resulting in missed detections. Moreover, in complex environments, small targets are often mixed with background regions that share similar textures or visual patterns, making it more difficult to accurately separate foreground from background and achieve precise localization.Therefore, improving the accuracy, robustness, and generalization capability of small target detection in remote sensing images, while enhancing training stability and localization precision under complex conditions, remains a critical and meaningful research problem.

Addressing the above challenges, a remote sensing small target detection algorithm integrating attention mechanisms and feature enhancement is proposed. First, a multi-level feature collaboration strategy is constructed by combining channel attention and spatial attention with a gating mechanism. This design enables adaptive weighting of features across different levels, strengthens cross-scale information interaction, and effectively suppresses background interference. By dynamically emphasizing salient regions and reducing redundant responses, the model is able to capture subtle and discriminative features of small targets more reliably. Second, a multi-scale feature enhancement module is designed by integrating three-dimensional convolution with multi-scale feature encoding to form an enhanced neck structure. This module facilitates the fusion of semantic and spatial information across different scales, allowing the network to preserve global contextual information while retaining local details. The use of three-dimensional convolution further enhances the modeling of inter-scale dependencies, improving the representation of small targets under complex backgrounds and varying resolutions. Finally, SIoU and NWD are jointly employed as regression loss functions to optimize bounding box prediction. This combination improves geometric alignment between predicted and ground-truth boxes, reduces sensitivity to scale differences, and stabilizes the optimization process. As a result, the model achieves faster convergence and more accurate localization, especially for small and densely distributed targets.

Extensive experiments are conducted on three widely used remote sensing datasets, including NWPU VHR-10, DIOR, and RSOD, to evaluate the effectiveness and generalization capability of the proposed method. The results show that, compared with the baseline model, the improved algorithm achieves consistent performance gains across all datasets, with mAP@0.5 increasing by 9.9%, 3.1%, and 3.2%, respectively. These improvements demonstrate that the proposed method effectively enhances feature representation and detection performance for small targets. Visualization results further indicate that the model can successfully identify targets that are easily missed by conventional approaches, particularly in scenarios with dense distributions and complex backgrounds. In addition, the proposed method shows improved robustness in distinguishing targets from visually similar background regions. Ablation studies confirm the effectiveness of each component, showing that the multi-level feature collaboration strategy, the multi-scale feature enhancement module, and the optimized loss function contribute synergistically to performance improvement. The results also suggest that the proposed framework maintains stable detection performance under different dataset distributions, indicating good generalization ability.

In this study, an attention- and feature-enhancement-based small target detection algorithm for remote sensing images is proposed. By introducing a multi-level feature collaboration strategy and a multi-scale feature enhancement module, the method significantly improves cross-level feature interaction and strengthens the representation of small targets. Meanwhile, the joint optimization of SIoU and NWD loss functions further enhances localization accuracy and training efficiency. The proposed approach effectively addresses key challenges in remote sensing small target detection, including complex backgrounds, scale variations, and insufficient feature information. Experimental results on multiple benchmark datasets confirm the superiority, robustness, and generalization capability of the proposed method over the baseline model. These findings demonstrate its strong potential for practical applications in real-world remote sensing tasks, particularly in scenarios that require accurate detection of small and densely distributed targets. Furthermore, the proposed framework provides a reliable technical reference for future research and can serve as a foundation for further improvements in small target detection methods.

Salient Object Detection (SOD) is difficult in low-light conditions. The images often suffer from poor quality, heavy noise in dark areas, and low contrast. These issues make it hard for models to detect objects accurately and reliably. Current methods mostly rely on single-mode features. These methods cannot capture complex information in low-light images. This is especially true when light is uneven or local details are lost. Therefore, this study proposes an efficient and robust hybrid model. We combine the strengths of Convolutional Neural Networks (CNNs) and Transformers. Our goal is to improve detection performance and overcome the limits of traditional methods in the dark.

This study proposes a Dual-Stream Hybrid Network (DSHNet) based on a hybrid CNN-Transformer architecture, which adopts an asymmetric dual-branch encoder-decoder lightweight structure to make up for the defects of the existing hybrid models in low-light image processing, such as inflexible multi-scale image processing and low feature fusion efficiency. DSHNet combines two main strengths: first, CNNs extract local features at multiple scales with shallow convolution suppressing noise interference caused by uneven illumination and deep convolution capturing fine-grained texture features; second, Transformers provide powerful global modeling with self-attention mechanism breaking the bottleneck of modeling long-distance dependence between target pixels in low-light images. The model uses a multi-level strategy to extract features from both the CNN and Transformer modules, then adopts a global-local synergy method to realize cross-hierarchical bidirectional fusion of features from different branches at the same scale, solving the problem of information loss in multi-scale feature fusion. We also designed a Channel Spatial Attention Enhancement Module (CSAEM), which dynamically fuses channel-spatial attention and channel recalibration, and uses a 1×1 convolution layer to learn the weighted coefficient to adaptively balance channel and spatial attention, realizing explicit perception of edge structures and effectively suppressing background noise in low-light images. In the encoding stage, the Efficient Spatial Reduction Attention (ESRA) module is applied step by step to Transformer features, compressing the keys and values in multi-head self-attention through convolution operations and introducing the DropKey mechanism to reduce the number of model parameters and computational complexity while avoiding over-focus on local areas. We also adopt an overlapping patch embedding strategy and embed depth convolution in the feedforward network to enhance the continuity of local features and avoid the dilution effect of fixed position encoding on dark area features. In the decoding stage, we use a two-stage hierarchical design with a coarse-grained decoding branch restoring the spatial resolution of global semantic features and a fine-grained decoding branch refining the dark texture and local contrast, and deploy saliency prediction heads at six feature stages with a deep supervision strategy to realize gradient backpropagation from coarse to fine and strengthen the boundary constraints of dark area targets. Finally, we use a combined loss function of Focal Loss and Dice Loss. This helps the model focus on difficult samples and improves area overlap, effectively solving the issue of unclear boundaries caused by severe imbalance between foreground and background in low-light scenes.

To verify the effectiveness of DSHNet, we constructed a complex low-light scene mixed dataset LlSOD, which includes 3960 images and corresponding binary masks by screening and mixing LLVIP and YLLSOD datasets with NIQE greater than 6.78 and labeling with Labelme. Experimental validation was conducted on LlSOD and standard datasets including RGB252, RGBD-385, RGBT-621 and VI-789, with experimental configuration set as 50 training epochs, initial learning rate of 5e-5, poly learning rate strategy with power=0.9, weight decay of 1e-4 and batch size of 4. Ablation experiments on the LlSOD dataset show that the introduction of the lightweight CNN branch only increases 0.3M parameters, while the F-measure is increased by 5.2% and MAE is reduced by 2.4% compared with the single Transformer branch; the CSAEM dual-branch enhancement module further reduces MAE to 0.093 and increases F-measure to 0.901, which verifies that the synergy of channel and spatial attention is superior to a single attention mechanism. Comparative experiments show that DSHNet is much lighter than the similar hybrid model ABiUNet, reducing the number of parameters by 23% (only 26.1MB). On the LlSOD dataset, it achieved high scores: 91.2% for F-measure, 83.7% for S-measure, and 87.2% for weighted F-measure, which are 5.4%, 5.7% and 4.8% higher than the YLLSOD model dedicated to low-light image SOD respectively. In the generalization performance test on standard datasets, DSHNet achieved 0.021 MAE and 0.852 F-measure on RGB252, 0.037 MAE and 0.907 F-measure on VI-789, and even showed competitive performance on dual-modal datasets RGBD-385 and RGBT-621 without introducing additional modal data, with 0.850 F-measure on RGBD-385 and 0.854 F-measure on RGBT-621, which proves that the global-local feature fusion of DSHNet can simulate multi-modal information interaction and has strong robustness. Visual comparison results show that DSHNet can stably realize object positioning and segmentation in various low-light scenes such as backlight, indoor night and outdoor night, effectively solving the problems of foreground and background segmentation difficulty and inaccurate object positioning faced by most methods in low-light environments.

In conclusion, DSHNet successfully solves key problems in low-light object detection such as difficult balance between global modeling ability and computational efficiency, difficulty in considering global information and local detail features, and overlap between multi-scale feature fusion and low-light noise. It combines the advantages of both CNNs and Transformers, and realizes the lightweight design of the model on the premise of improving detection performance. The results prove that our model is both lightweight and powerful, and provides a new effective approach for single-modal low-light image saliency detection. In the future, we will continue to optimize the model structure, further reduce the computational complexity of the model while maintaining detection accuracy, and plan to explore more real-world applications such as autonomous driving and security monitoring to promote the engineering transformation and application of this technology.

Phase measurement profilometry (PMP) has emerged as a key optical technique for acquiring three-dimensional (3D) surface information. It has been successfully adopted in fields such as precision manufacturing, biomedical imaging, robotics, security inspection, and digital entertainment. Recently, the demand for high-resolution optical 3D measurement has increased rapidly. In the high-resolution PMP system with digital projector, the measurement error caused by pixel matching between projector and camera becomes obvious. In ultra-high-resolution PMP systems with a high projector-to-camera pixel size ratio (PPSR), fringe patterns projected by diamond pixel digital light processing (DLP) projectors exhibit prominent zigzag edges. Furthermore, the sampling frequency of vertical fringes is only half that of horizontal fringes, resulting in a significant decline in phase measurement accuracy and becoming a key bottleneck restricting system performance. Addressing this critical issue requires a comprehensive solution that mitigates both pixel dimension induced and discrete sampling errors.

A high-precision measurement method combining pixel remapping and deep learning was proposed to overcome these challenges. Firstly, by analyzing the transmission characteristics of fringes in the PMP system and the pixel mapping law of diamond pixel projectors, a pixel remapping scheme for column-interlaced diamond linear fringes (CIDLF) was designed. This scheme rearranges diamond pixels into column-interleaved straight-line arrangements, doubling the sampling frequency of vertical fringes while effectively alleviating the zigzag phenomenon on fringe edges. Unlike the traditional diamond zigzagged fringe (DZF) and rectangle linear fringe (RLF), CIDLF maintains consistent fringe width between design and projection, ensuring better sinusoidality of projected fringes. Secondly, a ResUNet deep neural network integrating residual network and UNet architectures was constructed to smooth the captured fringes. The UNet component enables spatial downsampling and upsampling for high-level feature extraction and detail recovery through skip connections, which fuse low-level pixel information with high-level semantic features. Residual modules incorporated into both encoder and decoder paths alleviate gradient vanishing and overfitting during deep network training, enhancing the network’s ability to preserve fine object details while smoothing fringes. The network’s basic convolution block (BasicConv) consists of 3×3 convolution, batch normalization, dropout regularization, and rectified linear unit activation, ensuring efficient feature learning and noise suppression. To ensure reliable network training, a high-quality dataset construction method combining defocused projection and the 32-step phase-shifting algorithm was developed. The focused fringes and defocused fringes corresponding to the focused one are captured. Then, parameters of fringes including background, amplitude and phase are obtained with 32-step phase-shifting algorithm and the averages of the background and amplitude of the two types of fringes are worked out. A high contrast smooth fringe is generated using the above parameters as the ground truth. The dataset included 19200 fringe images for training, 400 for validation, and 400 for testing, covering fringe patterns with periods of 8, 12, and 16 pixels. The ResUNet was trained using stochastic gradient descent (SGD) optimization with a mean squared error (MSE) loss function. Training spanned 100 epochs with a batch size of 8; the initial learning rate of 5×10⁻¹ was updated to 5×10⁻² after 20 epochs, resulting in a final training MSE of 1.0128×10⁻⁵ and a validation MSE of 5.4261×10⁻⁵.

An experimental system composed of a LightCrafter 4500 projector (1140 pixel×912 pixel) and a Bonito CL-400C camera (2320 pixel×1736 pixel) with a Nikon zoom lens was established to verify the method. PPSR was adjusted by modifying the camera lens focal length, covering values from 0.5 to 85. Experimental results demonstrated that as PPSR increased, the pixel structure of the projector in fringe patterns became more distinct, leading to more prominent regular distributions of phase measurement errors. For traditional DZF, the phase error reached 0.3013 rad in the focused state, while CIDLF achieved a significantly lower error of 0.0898 rad, outperforming both DZF and RLF across all focusing conditions. When combined with ResUNet processing, the phase measurement error of CIDLF under the 4-step phase-shifting algorithm was reduced to 0.0225 rad, approaching the 0.0223 rad error of the 32-step algorithm. This performance is particularly valuable for high-speed 3D measurement applications that require fewer phase shifts. In measurements of step-shaped objects and objects with step reflectivity, CIDLF processed by ResUNet showed phase curves nearly identical to those of defocused CIDLF, with steeper edge transitions and smoother reflectivity boundary regions compared to processed DZF. For complex objects, the proposed method maintained low phase measurement errors even in intricate regions, outperforming defocused projection and DZF processing. The method effectively balances fringe smoothness and detail preservation, resolving the trade-off between error reduction and information retention in high-PPSR systems.

This method effectively resolves the measurement error issues caused by diamond pixel DLP projectors under high PPSR conditions. By combining pixel remapping to optimize fringe sampling and ResUNet to refine captured images, it achieves both high measurement accuracy and detail preservation. The proposed dataset construction method ensures robust network training, enabling the system to perform well with fewer phase shifts. This work provides a feasible approach for achieving high-precision, high-resolution, and high-speed 3D measurement. Future research will focus on adapting the network to varying system configurations and expanding its applicability to dynamic measurement scenarios.

As semiconductor manufacturing progresses towards sub-Xnm nodes and adopts 3D device architectures, such as gate-all-around field-effect transistors and 3D-NAND, wafer structures now feature extremely high aspect ratios and complex heterogeneous integration. This development imposes strict requirements on optical inspection and metrology, which are crucial for process control. Traditional inspection methods, which regard illumination as a static and passive source and mainly rely on backend algorithmic processing, are becoming increasingly insufficient. Their drawbacks, including instability in dynamic environments, substantial signal-to-noise issues in detecting nanoscale defects, and limited three-dimensional awareness, highlight the necessity for a revolutionary change. Therefore, next-generation optical inspection systems should transform into intelligent, integrated platforms based on the synergy of two key technologies: dynamic light field manipulation and multidimensional information fusion perception. This reconfigures the system from a passive observer to an active probe, where lighting and sensing are dynamically coupled to optimize detection from the physical signal generation stage.

The application of light field shaping in engineering clearly and logically evolved to cope with ever more challenging demands of semiconductor inspection. The first generation was called static light field shaping, addressing the fundamental need of converting an inherently Gaussian profile output from most lasers into useful, uniform profiles required by scanning applications. This was done using passive optical elements such as a diffractive optical element and a microlens array that were able to create a flat-top line illumination or flat-top spot illumination, thus establishing a fundamental hardware framework. With increasing complexity in inspection conditions and the need for flexibility, it evolved into active light field control: active, programmable devices, such as digital micromirror devices or liquid-crystal spatial light modulators. All of these contributed to a vital capability: the ability to shift quickly from one type of light to another, or to generate complex, programmable, structured light patterns in real-time and thus match the light field to particle inspection tasks. However, only achieving dynamic control did not ensure stable, high-fidelity performance for multiple tools and over time. This realization gave rise to step three: adaptive light field correction. We now looked inwards at compensating for the intrinsic flaws of our systems and their variations. With tools such as wavefront correctors, integrated beam pre-processing modules, and computational algorithms that enabled active measurement and correction of the static aberrations due to component tolerances or assembly stresses. This “ pre-standardization” of the input beam ensured that downstream modulation stages received a consistent, high-quality light field, which is critical for tool-to-tool matching and long-term stability. The ultimate stage in this progression is closed-loop light field self-calibration, which looks outward to combat real-time environmental perturbations. To build a high-bandwidth “ sense-analyze-act” feedback loop—using fast sensors and smart processors (usually augmented with artificial intelligence), and fast actuators—whereby the system has the capability of sensing and correcting in real time, transient disturbances such as vibration or thermal drift, transforming into an autonomous adaptive agent. Based on such a four-phase evolutionary process, with the principle of “ multi-dimensional fusion and collaborative control”, we develop a three-layer engineering application architecture for addressing the critical challenges in contemporary examination. It should be noted that this design can be viewed as a natural combination of these three levels: the physical execution layer forms its foundation, including devices such as spatial light modulators or digital micromirror devices, which directly shape the light field’s phase, amplitude, and pattern. The physical principal layer is the strategy layer that turns physical principles into practical strategies for breaking through conventional barriers. It applies techniques like structured illumination microscopy to obtain super-resolution images, active noise immunity and polarization engineering to improve the signal-to-noise ratio, and quantitative phase imaging for 3D topography sensing. Computational algorithm layer is an intelligent decision maker that drives the whole system by solving inverse problems, conducting AI-based defect classification, and allowing for adaptive optimization and closed-loop control, which ties everything together. There is a tight coupling between all three of those layers—with instructions guiding how they are implemented on hardware, and software refining both—that constitute an integrated smart system that is ready to tackle any challenging inspection task.

We have traced the evolution of light field control from a passive element into that which forms the heart of an active intelligent probing system for semiconductor optical inspection. Logical progression of the ideas, starting from static shaping and moving towards closed-loop self-calibration, paves the way forward towards realizing dynamic, resilient optical systems. Our suggested three-tiered application architecture provides an overall systematic approach for designing the next generation of inspection tools, effectively channeling advances in hardware, physics, and algorithms to overcome the intertwined problem of resolution, accuracy, speed, and three-dimensional vision. In the future, further development in this direction depends on closer integration with state-of-the-art computer intelligence and new optical materials. Highlights of the frontier include generative artificial intelligence driven inverse design that promises to discover non-intuitive, high-efficiency photonic architectures beyond human’s intuition; development of deep learning enhanced expert system, which is a hybridization between data driven learning and structured physics knowledge for robust and efficient optimization; and research on large model-based autonomous agent to perceive and make decisions about a whole scene in complicated inspection environment. Meanwhile, with the emergence of new hardware such as dynamic metasurface integrated photonic circuits, we may be able to make even more compact and flexible modulators for controlling light fields. We believe that this combination of smart software (algorithms) and hardware devices would enable autonomous and powerful light field manipulation, consolidating its position as the essential “eyes” for precision manufacturing of the semiconductor industry and beyond.

The manufacturing of printed circuit boards (PCBs) constitutes the foundational infrastructure of the contemporary electronics industry. As the sector evolves in response to consumer demands for increasingly miniaturized, sophisticated, and high-performance devices, the physical characteristics of PCBs have undergone significant transformation. Modern boards are characterized by high-density interconnects, multi-layered architectures, and microscopic component placement. In this context, the role of quality control has shifted from a supplementary process to a critical determinant of product viability and yield. Traditionally, defect detection has relied on manual inspection or standard automated optical inspection (AOI) systems. However, these conventional methods are increasingly challenged by the escalating complexity of modern PCB designs. Manual inspection is labor-intensive, subjective, and prone to fatigue-induced errors, rendering it unsuitable for mass production. Similarly, traditional AOI systems based on pattern matching or simple image processing often struggle with the subtle variations and complex backgrounds found in high-density PCBs. Consequently, there is a growing necessity for more intelligent and adaptive inspection solutions capable of discerning defects with high precision. Current state-of-the-art detection methods, particularly those leveraging deep learning paradigms, encounter a distinct set of challenges when applied to the specific constraints of industrial manufacturing environments. The first challenge is the significant computational burden associated with high-precision models. Accurately detecting minute defects typically necessitates high-resolution imagery; however, processing such data with deep convolutional neural networks (CNNs) or complex Transformer models consumes substantial computational resources and memory. This computational demand often translates to prohibitive hardware costs for widespread deployment on factory floors. The second challenge is scale variation, which presents a persistent hurdle. PCB defects manifest in widely varying sizes, from tiny pinholes barely visible to the naked eye to larger structural errors like open circuits. A detection model must possess the multi-scale capability to identify these diverse anomalies with uniform accuracy. The third challenge is the imperative for real-time performance. In high-velocity production lines, inspection algorithms must operate at speeds commensurate with manufacturing throughput to avoid creating bottlenecks. Unfortunately, many existing high-accuracy models are computationally too intensive for real-time inference, forcing manufacturers to make an undesirable trade-off between inspection speed and detection precision. These limitations highlight the need for an optimized architecture that balances resource efficiency with robust detection capabilities.

To address these pressing challenges effectively, this study proposes LightRT-DETR, a lightweight real-time detection transformer specifically optimized for PCB defect detection. Building upon the architectural principles of the real-time detection Transformer (RT-DETR), our approach introduces a series of strategic modifications designed to mitigate computational costs significantly while maintaining, and in some aspects enhancing, detection accuracy. The design of LightRT-DETR is characterized by three primary technical innovations: the implementation of the EfficientFastGLU-18 backbone, the integration of a cascaded group attention (CGA) mechanism, and the development of an efficient selective feature pyramid network (ES-FPN). These components work in concert to refine the model's ability to extract relevant features from complex backgrounds without incurring the heavy computational penalties associated with standard transformer architectures. The overarching goal is to create a model that is both agile enough for edge deployment and accurate enough to meet rigorous quality standards. To resolve the inefficiency of traditional backbones like ResNet-18 when deployed on edge computing devices, we propose the integration of the EfficientFastGLU-18 backbone. This novel structure synthesizes partial convolution (PConv) with convolutional gated linear units (GLU). PConv operates by applying convolution to only a subset of input channels while leaving the remainder untouched for the next layer. This strategy effectively reduces memory access and floating-point operations (FLOPs) without compromising the flow of information. By coupling this efficient convolution strategy with GLU, which provides robust non-linear transformation capabilities, the backbone maintains strong feature extraction abilities. This is particularly crucial for identifying defects against the complex, texture-rich backgrounds of PCBs, where differentiating between a valid trace and a defect requires discerning subtle feature variations. Furthermore, to address the computational expense of standard multi-head attention mechanisms, we introduce a cascaded group attention (CGA) mechanism. Instead of processing all feature channels simultaneously, CGA splits feature maps into distinct groups and processes them through a cascaded structure. This approach allows the network to refine features progressively, as the output of one group's attention computation can inform the next, creating a hierarchical refinement process. This mechanism appears particularly beneficial for defects possessing intricate or fine-grained details, such as "mouse bites" or "spurs", which are frequently overlooked by coarser, global attention mechanisms. Finally, we tackle multi-scale defect detection through the efficient selective feature pyramid network (ES-FPN). This redesigned module employs selective feature integration and incorporates RetBlockC3 modules enhanced with manhattan self-attention (MaSA). Unlike standard self-attention, MaSA is specifically designed to capture long-range dependencies in horizontal and vertical directions. Given that PCB layouts consist largely of orthogonal lines, this domain-specific adaptation enables more effective recognition of defects across widely varying scales by aligning the attention mechanism with the geometric priors of the PCB domain.

The efficacy of LightRT-DETR was validated through extensive experiments on a public PCB defect dataset, revealing a significant improvement in both efficiency and accuracy compared to current state-of-the-art models, including the widely utilized YOLO series. In terms of quantitative performance, LightRT-DETR achieved a mean average precision (mAP50) of 98.3% and a stricter metric, mAP50-95, of 52.9%. When benchmarked against the advanced YOLOv9e model, our approach demonstrated an improvement of 3.0% in mAP50 and 1.8% in mAP50-95. These metrics suggest that the model is not only capable of detecting defects but also does so with high localization accuracy, minimizing false positives and false negatives—a critical requirement for industrial quality control. The experimental setup ensured fair comparisons by utilizing consistent hyperparameters and training environments, further reinforcing the validity of the observed performance gains.

Perhaps the most distinct advantage of LightRT-DETR lies in its computational efficiency. The model requires approximately 37.2 GFLOPs, representing an 80.3% reduction in computational cost compared to the baseline RT-DETR model. Similarly, the parameter count is reduced to 12.8 million, a 77.7% decrease. This substantial reduction has practical implications: it facilitates deployment on cost-effective hardware with limited computing power or allows the system to operate at significantly higher frame rates on standard hardware.

UAV aerial images have been widely applied in military surveillance, emergency rescue, agricultural monitoring, and other fields due to their advantages of flexible shooting angles and rich spatial information acquisition. However, target detection in such images faces prominent challenges: extreme scale variations of targets, dense spatial distribution of small targets, and frequent false or missed detections caused by complex backgrounds. Additionally, real-time application scenarios impose strict requirements on the inference speed of detection models, which makes it difficult for existing algorithms to balance detection accuracy and computational efficiency. To address these issues, this study aims to propose a high-precision and efficient UAV aerial image target detection method, which enhances the model's ability to perceive multi-scale targets, especially small targets, reduces false and missed detections, and ensures real-time performance to meet practical application needs.

Based on the YOLOv11s baseline model, this study designs a multi-module collaborative optimization framework, and the specific technical methods are as follows: First, an Adaptive Multi-dimensional Feature Sampling (AMFS) unit is embedded in the backbone network. The input feature maps are grouped along the channel dimension, and three parallel paths are used for multi-scale feature extraction. Adaptive average pooling, depthwise convolution, and spatial attention mechanisms are integrated to realize the adaptive synergy fusion of local details and global contextual information, which makes up for the limitation of fixed convolution receptive fields and enhances the feature extraction ability for small targets. Second, a Spatial Pyramid Adaptive Pooling Fusion (SPAPF) layer is constructed to replace the traditional SPPF module. This layer combines smoothed approximate average pooling and smoothed approximate maximum pooling through learnable weight masks. It not only retains structural details and texture information of targets but also reduces redundant computations, thus accelerating the model's inference speed while maintaining lightweight characteristics. Third, a Cross-Spatial Contextual Parallel Self-Attention (CS_C2PSA) module is proposed. It introduces cross-spatial multi-head self-attention (CSL-MHSA) and phased residual connection structures. By associating local and global receptive fields through 1×1 and 3×3 convolutions, and integrating vertical and horizontal spatial position information, the module strengthens the model's global contextual perception ability, alleviates the confusion between small targets and backgrounds or adjacent targets, and improves the false detection problem. Finally, a Semantic-guided Feature Fusion (SGFF) module and a dedicated Small Object Detection (SOD_Detect) head are designed. The SGFF module adopts a cross-layer feature alignment strategy and sub-pixel convolution technology. It uses the SE channel attention mechanism to enhance the expression of deep semantic information in shallow features, realizes the efficient fusion of shallow detail features and deep semantic features, and generates high-resolution feature maps with rich semantics for the SOD_Detect head, which significantly improves the positioning and classification accuracy of small targets. The model is trained and tested on VisDrone2019 and VisDrone2021 datasets, with batch_size set to 16 and epochs to 200. The experimental environment is Windows 10 operating system, NVIDIA GeForce RTX 3090 GPU, PyTorch 1.10.1 + CUDA 11.1 deep learning framework, and Python 3.8 programming language.

Ablation experiments on the VisDrone2019 dataset show that each improved module plays a positive role in performance enhancement: The AMFS unit increases the mAP@0.5 by 3.2%, the SPAPF layer reduces the GFLOPs by 4.4 while improving the Recall by 2.2%, the CS_C2PSA module increases the Precision by 2.3% and the mAP@0.5 by 2.0%, and the SGFF module improves the Precision by 3.9% and the mAP@0.5 by 3.4%. When all four modules are integrated, the model's Precision reaches 58.3%, Recall 43.1%, and mAP@0.5 46.8%, which are 8.1%, 4.7%, and 7.2% higher than those of the baseline YOLOv11s, respectively, and the GFLOPs are maintained at 30.8, ensuring computational efficiency. Comparative experiments with 11 mainstream algorithms (including SSD, Faster-RCNN, YOLOv5s, YOLOv11s, etc.) on VisDrone2019 and VisDrone2021 datasets show that the proposed method achieves mAP@0.5 of 46.8% and 44.7% on the two datasets, respectively, which are 7.2% and 6.5% higher than the baseline model. Compared with the latest YOLOv10s and YOLOv8s, the mAP@0.5 is increased by 8.0% and 8.6% respectively on VisDrone2019 dataset. Compared with the UAV-specific detection algorithm ESO-DETR, the mAP@0.5 is improved by 2.1%, and the FPS is increased by 14. Even compared with the lightweight model PP-YOLOE, the proposed method still has advantages in both accuracy and speed, with Precision increased by 4.3% and FPS increased by 21. Visualization results of challenging scenarios such as dense small targets, aerial multi-scale, and low-light occlusion show that the proposed method effectively reduces false and missed detections, and has better detection performance for dense small targets than algorithms such as MIS-YOLOv8. However, there is still room for improvement in the distinction between Pedestrian and People categories in extremely dense scenarios, which is a direction for subsequent optimization.

This study proposes a UAV aerial image target detection method based on semantic guidance and multi-dimensional feature perception. Through the collaborative optimization of AMFS, SPAPF, CS_C2PSA, and SGFF modules, the method effectively solves the key problems of large target scale variation, dense small target distribution, and high false and missed detection rates in complex backgrounds in UAV aerial image target detection. Experimental results on public datasets verify that the proposed method significantly improves the detection accuracy while maintaining efficient computational performance, and has excellent generalization ability and robustness. It provides a reliable technical solution for high-precision and real-time target detection in UAV aerial imagery. In future research, we will further optimize the module structure to enhance the model's ability to distinguish similar target categories in extremely dense scenarios, and explore the application of the method in more complex environments such as severe weather and heavy occlusion to expand its practical application scope.

Small object detection remains a significant and persistent challenge within the computer vision domain. This challenge is particularly acute in the context of drone aerial imagery, where objects captured from high altitudes appear extremely small, densely packed, and often exhibit blurred features due to low resolution and environmental factors. Traditional object detection algorithms frequently suffer from performance degradation in these scenarios, manifesting as high rates of missed detections and false positives. To address these critical limitations, this paper proposes a novel object detection algorithm named FFC-YOLO (FASFF-FreqFusion-CAA YOLO), which is built upon a frequency-aware feature fusion framework.

The core of the FFC-YOLO algorithm lies in three systematic and synergistic innovations designed to enhance feature representation, fusion, and contextual understanding specifically for small and dense objects. First, to bolster multi-scale feature extraction, we redesign the detection head by incorporating a Four Adaptively Spatial Feature Fusion (Detect-FASFF) structure. This is augmented with an additional P2 detection layer dedicated to capturing finer-grained spatial information crucial for identifying minuscule targets. The FASFF mechanism leverages four adaptive branches to dynamically aggregate features across different scales, thereby significantly improving the model's sensitivity and discriminative power for small objects across varying sizes.Second, we comprehensively optimize the feature fusion pathway to counteract the information loss common in conventional sampling methods (e.g., up-sampling and down-sampling). We introduce a Frequency-aware Feature Fusion (FreqFusion) module. This module operates by decomposing features into different frequency components within the frequency domain, allowing for more deliberate and effective integration of high-frequency details (essential for edge and texture information of small objects) and low-frequency semantics. This FreqFusion module is then elegantly coupled with a Bidirectional Feature Pyramid Network (BiFPN) structure. The resulting fusion architecture enables efficient, multi-level bidirectional cross-scale information flow, effectively mitigating the problems of feature obliteration, category misclassification, and bounding box localization drift in scenes crowded with small objects.

Third, to enhance the model's ability to leverage surrounding semantic context—a vital clue when object appearance is ambiguous—we integrate a Context Anchor Attention (CAA) mechanism into the C2PSA module. This attention mechanism functions by establishing dynamic associations between anchor points and their contextual surroundings, enabling the feature extraction process to selectively focus on and amplify informative regions around potential targets. This contextual awareness strengthens feature representation and aids in distinguishing small objects from complex backgrounds.

The efficacy of the proposed FFC-YOLO algorithm is rigorously validated through extensive experiments on two benchmark datasets. On the public VisDrone2019 dataset, a standard benchmark for drone vision, FFC-YOLO achieves a mean Average Precision (mAP@0.5) of 40.0%. This represents a substantial performance gain of 8.0%, 7.6%, and 7.8% over the widely-recognized YOLOv8n, YOLOv10n, and YOLOv11n baselines, respectively. To further verify its generalization capability and prowess in detecting extremely small targets, we conducted additional experiments on a custom-built, challenging dataset named "tiny-data", which contains three categories of progressively smaller persons ("sperson", "lperson", "wperson"). On this dataset, FFC-YOLO demonstrates superior results compared to the strong YOLOv11n baseline, with improvements of 9.2% in mAP@0.5, 8.7% in Precision (P), and 5.9% in Recall (R). These consistent gains across both public and private datasets robustly confirm that the FFC-YOLO algorithm possesses strong generalization ability and excels in the demanding task of drone-based aerial image analysis, particularly for long-range and dense small object detection.

In conclusion, this paper presents a comprehensive and effective solution to the small object detection problem in aerial imagery. By innovating at the levels of feature extraction (FASFF head), feature fusion (FreqFusion-BiFPN), and contextual modeling (CAA attention), the FFC-YOLO framework successfully addresses key shortcomings of prior methods. The experimental evidence underscores its potential for practical application in UAV-based surveillance, traffic monitoring, and search-and-rescue operations. For future work, we plan to explore model compression techniques such as pruning and knowledge distillation to streamline the model for efficient deployment on resource-constrained edge devices, thereby bridging the gap between high accuracy and real-time performance.

In complex real scenes (such as low illumination, haze, occlusion or strong light interference), single mode target recognition and localization methods face significant performance bottlenecks. Although the visible image is rich in texture and color details, it is highly sensitive to light conditions; Infrared image has all-weather working ability and strong penetrability by virtue of thermal radiation imaging, but it is limited by low resolution and lack of structural information. The inherent limitations of the two make it difficult for the single-mode system to meet the requirements of all-weather perception with high robustness and high accuracy. Therefore, how to deeply integrate the complementary advantages of infrared and visible modes, that is, combining the local detail expression ability of visible light with the global thermal semantic saliency of infrared, has become a key challenge to improve the performance of target detection in complex environments. This paper aims to build a new neural network architecture that can deeply mine, adaptively fuse and efficiently use multimodal features, in order to achieve higher accuracy, stronger robustness and better scale invariance of target recognition and localization.

This paper proposes an infrared visible multimodal target recognition and positioning network based on CNN transformer heterogeneous fusion framework. The network is composed of four core modules: visible light oriented CNN feature extraction branch, infrared oriented transformer feature extraction branch, cross modal adaptive fusion module and multi-scale dynamic adjustment structure. In the visible light branching, the "feature criticality grouping convolution" mechanism is designed: the feature importance is evaluated by weighting the maximum and average values at the channel level, and the normalized criticality weight is generated after full connection layer optimization. Based on this, the number of convolution output channels is dynamically allocated - the key region gets richer characterization, and the background region is compressed, so as to guide the network to focus on the essential information of the target; At the same time, the compression extraction module composed of 1 × 1 point convolution and Gelu activation is introduced, and the hybrid down sampling strategy is adopted to reduce the computational overhead and the loss of micro target information. In the infrared branch, the feature aggregation shift window self-attention structure is constructed: the computational complexity is controlled by using the window shifting mechanism of swing transformer for reference, and 2 × 2 average pooling is applied to the key (k) and value (V) matrices in the self-attention for local aggregation, which not only significantly reduces the redundant calculation, but also strengthens the ability of global context modeling for low contrast heat source targets. Then, a cross modal fusion module is designed: firstly, the local features of CNN and the global features of transformer at the same scale are normalized, and then channel attention (highlighting semantic importance) and spatial attention (accurately locating the target area) are applied respectively, and shallow features are fused to enhance detail retention; Finally, the adaptive stitching of the three features is realized through the learnable weight parameters, and the complementary enhancement between modes is achieved. Finally, in order to alleviate the feature conflict in multi-scale fusion, a multi-scale dynamic adjustment mechanism is proposed: Based on the feature pyramid structure, the fusion proportion is dynamically adjusted according to the difference in the number of adjacent scale channels (the larger the scale difference, the smaller the fusion weight), and the channel spatial attention mechanism is combined to strengthen the target response, suppress the background interference, and significantly improve the unified detection ability of small, medium and large targets.

Experiments is systematically validated on three public datasets—KAIST, FLIR, RGB-T—and a self-collected power grid equipment dataset (covering 5 types of electrical equipment). The results show that: 1) the proposed key-grouped convolution achieves 83.1% mAP on the visible light modality, with parameters and FLOPs reduced by 26% and 21% compared to standard convolutions, and inference speed increased by 29%, outperforming mainstream lightweight networks and demonstrating an excellent efficiency-accuracy balance. 2) After introducing feature aggregation in the infrared branch, FLOPs decreased from 173.2 G to 116.5 G, FPS increased from 69 to 88, and mAP only slightly dropped by 0.9%, verifying its efficiency. 3) The modality-network adaptability experiment clearly shows that CNN performs better on visible light (83.1% mAP) than Transformer (81.2%), while Transformer outperforms CNN on infrared (78.9% vs. 78.2%), strongly supporting the rationality of the heterogeneous dual-branch design. 4) In the comparison of multimodal fusion strategies, the proposed attention-guided adaptive fusion achieved 88.4% mAP on KAIST, significantly surpassing addition (86.8%) and concatenation (87.5%) methods; feature visualization confirms its effectiveness in suppressing background noise, particularly benefiting small object edge preservation. 5) The multi-scale fusion module, built on FPN and ASFF, introduces dynamic ratio adjustment and dual attention, achieving an overall mAP of 89.3%, with small object mAPs improved to 79.4%, validating its effectiveness in alleviating scale conflicts. 6) Comprehensive comparisons with five advanced methods (including dual CNNs, dual Transformers, CNN-Transformer cascades, etc.) across the four datasets show that our method achieved the best or second-best results on KAIST (89.3%), FLIR (88.9%), RGB-T (87.1%), and the power grid dataset (89.1%), with controlled computational cost (FLOPs ≈169 G, FPS ≈69), balancing accuracy, robustness, and practicality. In summary, this method systematically addresses the issues of insufficient multimodal feature complementarity and scale sensitivity through the deep synergy of modality characteristics, network architecture, and fusion strategies.

Aiming at the problems of insufficient feature mining, weak modal complementarity and poor multi-scale adaptability in infrared visible multimodal target recognition and localization, this paper innovatively proposes a heterogeneous network architecture that deeply integrates the advantages of CNN and transformer. Through visible light oriented feature criticality grouping CNN, infrared oriented feature aggregation lightweight transformer, attention driven adaptive cross modal fusion and dynamic multi-scale adjustment mechanism, the extraction efficiency, fusion quality and scale invariance of multimodal features are systematically improved. A large number of experiments have verified the superior performance of this method on multiple real scene data sets, which not only leads significantly in detection accuracy, but also maintains good computational efficiency and environmental robustness. This study provides an effective technical path for highly reliable target perception in complex environments. Future work will focus on model compression (such as knowledge distillation, neural architecture search) to adapt edge devices, and explore strategies to enhance the generalization ability in extreme weather (such as rainstorm and smoke), so as to further promote the application of this technology in key fields such as power patrol, automatic driving, emergency rescue and so on.

Residential gas leaks, vehicle exhaust, and industrial emissions all contain carbon monoxide (CO), a colorless, odorless, and extremely poisonous gas produced by the incomplete combustion of fuels containing carbon. Public safety, industrial production, and environmental quality are all seriously threatened by CO, which can cause acute poisoning or even death in humans at very low amounts. Therefore, it is crucial to create ultra-sensitive CO detection technology to protect human health, industrial operations, and environmental monitoring. Researchers have recently focused a lot of attention on anti-resonant hollow-core fiber (AR-HCF) because of its special benefits, which include low transmission loss, large core diameter, small optical stripe amplitude, compact structure, and the ability to enable long-distance continuous interaction between single-mode radiation and the target gas.These features give AR-HCF a lot of potential for use in gas sensing applications. In order to achieve high sensitivity, high stability, and compact trace CO detection, this study proposes an all-fiber gas detection system based on AR-HCF for CO monitoring employing fiber loop ring down spectroscopy.

In this work, an all-fiber CO detection system based on fiber ring cavity decay spectroscopy was built, using a continuous wave (CW) laser with a central wavelength of 1567 nm as the light source and AR-HCF as the gas chamber sensing element. The AR-HCF' transmission characteristics were first examined using the finite element approach, with an emphasis on its limiting loss at the operating wavelength of 1567 nm.The ideal length of the AR-HCF gas chamber was then determined by methodically examining the link between the sensitivity of the hollow-core gas chamber and the optical path length. Erbium-doped fiber amplifier (EDFA) with varying gain values were incorporated into the fiber ring cavity to boost the effective number of laser cycles and offset the intrinsic cavity loss in order to further improve the detection sensitivity. In order to thoroughly assess the detection performance of the developed system, a number of performance tests were carried out, including stability testing through repeated measurements and concentration detection tests in the range of 0-0.02%.

The research using the finite element approach showed that the AR-HCF' limiting loss at 1567 nm was roughly 0.92 dB/km.A 1-meter-long AR-HCF was chosen as the gas chamber structure after research into the relationship between optical path length and sensitivity revealed that the detection sensitivity increased with the length of the optical path. This was done while taking system compactness and practical applicability into full consideration. The findings of the experiment showed that increasing the EDFA gain greatly increased the system's sensitivity: the system's sensitivity with a 2 dB gain ring cavity was 16.7 times greater than that of the system without EDFA. With a correlation value (R²) of 0.99989, concentration detection experiments verified a strong linear relationship between the decay time and CO concentration in the range of 0%-0.02%. The greatest average relative error was only 0.4%, according to stability tests with ten repeated measurements for various CO concentration gradients. In the end, the developed system demonstrated outstanding detection performance and reliability with a minimum detection limit of 3.0757×10⁻¹¹, a sensitivity of 0.795 s, and a stability of 0.82%.

This work effectively creates an all-fiber CO detection system using fiber loop ring down spectroscopy in conjunction with AR-HCF. The system achieves ultra-high sensitivity and stability in trace CO detection by utilizing the superior performance of AR-HCF and the gain compensating effect of EDFA. The system offers a new and efficient technical method for trace gas detection thanks to its benefits of strong anti-interference ability, compact structure, high detection sensitivity, and dependable performance.It not only improves the technical methods of CO detection but also has great potential for use in areas including public health protection, environmental quality monitoring, and industrial safety early warning. In order to increase the detection limit and broaden the system's application scope to more trace gas detection scenarios, future research can concentrate on improving the fiber cavity construction and EDFA gain control.