With the continuous expansion of drone application scenarios, small object detection in aerial images has become a research hotspot in the field of computer vision. In view of the problems that small object features are not obvious, complex backgrounds lead to false detection and missed detection, and the existing algorithms are difficult to balance detection accuracy and real-time performance, this paper proposes an aerial image small object detection algorithm FST-RTDETR based on RT-DETR to solve these problems. First, FasterNet is combined with the EMA attention mechanism, and the structure of the Basic Block module of the original module is redesigned to improve the network operation speed and the accuracy of visual tasks. Secondly, in order to solve the problems of excessive calculation and more time-consuming post-processing after adding the traditional P2 detection layer, this study propose to use the P2 feature layer based on the original CCFM architecture to obtain features rich in small object information through SPDConv and give them to P3 for fusion, and then use the CSP idea and Omni-Kernel to improve CSP-OmniKernel for feature integration, effectively learn the feature performance from global to local, and finally reduce the missed detection rate, false detection rate and improve the detection performance of small objects. Finally, in order to simplify the loss function calculation process, improve regression efficiency and accuracy, and have a more comprehensive loss consideration, this study use inner-MPDIoU to replace the original GIoU. Experiments on the improved algorithm on the VisDrone2019 dataset show that the FST-RTDETR model achieves a detection accuracy of 49.6%, which is 2.1% higher than the original RT-DETR model. The FST-RTDETR model significantly improves the object detection performance of drone images, improves model efficiency, and shows good performance compared to other algorithms.

In recent years, the application of 3D Gaussian splatting technology in simultaneous localization and mapping systems has made it possible to perform high-quality image rendering using explicit 3D Gaussian models, significantly improving the fidelity of environmental reconstruction. However, the existing methods based on 3DGS have problems such as limited tracking accuracy and lack of global consistency in the 3D reconstruction of complex indoor environments. For this purpose, this paper proposes a dense SLAM algorithm based on 3D Gaussian splatting—SNGO-SLAM. This algorithm combines the advantages of both frame-to-model and frame-to-frame tracking methods, and uses surface normal perception to obtain richer geometric information, significantly improving the tracking accuracy. To address the tracking error that occurs over time, the algorithm introduces a loop closure process and optimizes the 3D Gaussian point representation problem, further enhancing the tracking accuracy. In addition, this algorithm also introduces a dual Gaussian pruning strategy, optimizing memory usage and ensuring precise camera tracking. Experiments on the Replica, ScanNet and TUM RGBD datasets show that while maintaining high rendering quality, the absolute root mean square error of the trajectory of this algorithm on the Replica dataset reaches 0.27 cm. Compared with NICE SLAM, Vox-Fusion, Gaussian SLAM and SplaTAM, the tracking accuracy has increased by 74.53%, 91.26%, 12.90% and 28.95% respectively, providing new ideas for SLAM technology.

To address the low search efficiency, slow convergence speed, and limited path expansion diversity of the RRT family of algorithms, an adaptive multi-strategy dynamic step-size algorithm, AMDS-Bi-RRT^*, is proposed. Based on the Bi-RRT^* framework, the algorithm enhances convergence efficiency through a dynamic goal-directed extension strategy and an adaptive step-size evaluation function. A multi-directional emergency maneuver strategy is designed to improve adaptability in complex environments. Meanwhile, node sampling is optimized using an improved artificial potential field method, and a three-stage path smoothing approach is introduced to ensure path feasibility. Comparative experiments conducted in four simulation environments of varying complexity against five benchmark algorithms—Bi-APF-RRT^*, Bi-RRT^*, APF-RRT^*, RRT^*, and goal-biased RRT^*—demonstrate that AMDS-Bi-RRT^* reduces average planning time by 12.22%~23.45%, shortens average path length by 0.88%~1.89%, and decreases the average number of nodes by 6.69%~22.85%. The results verify that AMDS-Bi-RRT^* outperforms the comparison algorithms in planning efficiency, path quality, and convergence speed, confirming its superior performance across diverse environments.

In order to meet the requirements of large-scale infrastructure and supporting equipment for corrosion monitoring in atmospheric environment, a multi-channel atmospheric corrosion monitoring system based on STM32F407ZGT6 is designed and implemented, aiming at the shortcomings of poor real-time performance and low monitoring accuracy in the existing corrosion monitoring system. The system combines the electrochemical impedance spectroscopy measurement technology and the theory of the equivalent circuit model of the galvanic probe of the double electrode primary battery, uses the time division multiplexing method to control the multi-channel excitation signal generation module to generate the excitation signal to act on the electrode system of each channel, uses the multi-channel response signal acquisition module to collect the response voltage data generated by the electrode system of each channel in real time, and transmits the calculated and processed electrochemical impedance spectroscopy data to the upper computer software deployed in the cloud server through the wireless communication module, and finally analyzes and processes the electrochemical impedance spectroscopy data to obtain the corrosion state information. The experimental results show that the system can realize multi-point corrosion status analysis and monitoring in atmospheric environment, the accuracy of corrosion rate is more than 90%, and the monitoring data can be accurately transmitted to the monitoring platform in real time.

Accurate assessment of pilot cognitive states is critical for ensuring flight safety, yet existing methods exhibit limitations in fusing multimodal physiological signals. To address this, this paper proposes a dual-stream deep learning network based on bidirectional cross-modal attention. The model adopts a parallel dual-branch architecture: The electroencephalography (EEG) branch quantifies brain functional connectivity through phase locking value (PLV) features and employs a densely connected network enhanced with squeeze-and-excitation (SE) modules for deep feature extraction; the electrocardiogram (ECG) branch extracts heart rate variability (HRV) and waveform features, processed by a residual-connected multilayer perceptron to characterize autonomic nervous system activity. Building upon this, an innovatively designed bidirectional cross-modal attention module dynamically weights and fuses the dual-path deep features to achieve precise classification of three states—concentrated attention, distracted attention, and startle/surprise. Experimental results on the NASA public dataset demonstrate an overall recognition accuracy of 97.44%. Ablation and comparative analyses confirm that the fusion strategy significantly outperforms single-modality analysis and simple feature concatenation methods. The study reveals that deep integration of EEG functional connectivity and ECG physiological information via attention mechanisms effectively enhances cognitive state recognition performance. This approach provides reliable technical support for developing objective and efficient pilot state monitoring systems, holding significant application value for improving flight safety.

Medium and long-term power load forecasting is a core link to ensure the stability and economy of power system planning and operation.Some studies convert the input data to the frequency domain through Fourier transform to obtain different signal components, thereby reducing the interference of noise. However, existing studies often indiscriminately handle all frequency-domain signals, causing the key frequency-domain components and irrelevant frequency-domain components to mix, which makes it difficult for the model to fully capture the features contained in the frequency-domain signals. Therefore, a multivariable long-term prediction model FTAformer that integrates frequency-domain analysis and attention mechanism is proposed. This model integrates time-domain and frequency-domain information and conducts collaborative modeling to enhance the model's ability to capture global features. Firstly, the input sequence is transformed into a frequency-domain signal by using the fast Fourier transform. A hierarchical filtering and isolation strategy is adopted to isolate the key frequency-domain components and suppress the noise. Then, the correlations among different variables are captured in the time domain through the multi-head attention mechanism, and the global representation of the sequence is modeled by using layer normalization and the feedforward network module. The experimental results show that on two public power load datasets, the predictive performance of this model is significantly higher than that of other benchmark models. Compared with the existing optimal model iTransformer, the mean square error and mean absolute error of the proposed method are reduced by 15.26% and 8.76% respectively in the multi-step prediction scenario, fully verifying the effectiveness and superiority of the collaborative modeling of frequency domain analysis and multi-head attention mechanism in medium and long-term power load forecasting.

To address the limitations of existing weakly supervised semantic segmentation models for point clouds, which struggle to balance local feature correlation, generalization, and feature utilization. This paper proposes WS-MLF, a weakly supervised point cloud semantic segmentation model via multi-scale local feature fusion, based on the RAC-Net baseline. Firstly, the raw point cloud data is taken as input, and a multi-scale spherical sampling methods (MSSM) is employed to capture hierarchical features across varying spatial radii. Secondly, a multi-local feature aggregation enhancement module (MFA) is designed to refine geometric context within neighborhoods. Thirdly, a spatial-channel-fused hybrid attention module (SCH-Att) is proposed to prioritize discriminative channels and key points. Finally, a decoder is utilized for upsampling to generate point-level semantic labels, thereby completing the semantic segmentation task. The proposed model is evaluated on large-scale indoor scene datasets, S3DIS and ScanNet-v2. Experimental results demonstrate that on the S3DIS dataset, when the label ratios are 0.02% and 0.06%, the mIoU surpasses RAC-Net by 2.71% and 0.54%, respectively. On the ScanNet-v2 dataset, with a label ratio of 20 pt, the mIoU increases by 1.55% compared with RAC-Net. These results validate WS-MLF's effectiveness in extracting key features under weak supervision, enhancing segmentation accuracy.

Small object detection in UAV aerial imagery encounters critical challenges including extremely small target sizes, complex background interference, and insufficient feature representation. Addressing the limitations of existing RT-DETR models in small object feature extraction and multi-scale fusion, this paper proposes an adaptive multi-scale gated enhancement fusion DETR (MGEF-DETR). A multi-order cross-stage gated aggregation (MCGA) module is designed to achieve selective enhancement of small object texture features through adaptive gating mechanisms. A Micro-OmniPyramid feature pyramid is constructed by integrating space-to-depth (SPD) convolution sparse encoding and cross-stage enhanced spectral kernel (CESK) modules, establishing lossless transmission pathways for small object features. An enhanced feature correlation (EFC) module is introduced to optimize cross-scale feature fusion through grouped attention and multi-level reconstruction strategies. An inner-modified penalty distance IoU (IMIoU) loss function is designed to enhance boundary regression sensitivity for small objects. Experimental results on the VisDrone2019 dataset demonstrate that MGEF-DETR achieves improvements of 3.9% and 3.1% in mAP@0.5 and mAP@0.5:0.95 metrics respectively compared to the baseline RT-DETR, while reducing parameters by 13.6%. Validation on TinyPerson and CODrone datasets further confirms the generalization capability of the algorithm, indicating significant improvements in both accuracy and efficiency for small object detection in aerial scenarios while maintaining lightweight characteristics.

Underwater litter detection is a crucial technology for maintaining the balance of underwater ecosystems. To address the challenge of significant variations in target scales encountered in underwater litter detection, we propose the YOLO11-MDA based on YOLO11 is proposed. Firstly, a multidomain feature extraction module MFEM is proposed, which is capable of extracting different scales of features from the input feature map by extracting the target features in both spatial and frequency domains, and enhances the ability of expression of the global features and local information. Second, the lightweight dynamic up-sampling DySample module is introduced to integrate contextual information and improve the quality and efficiency of up-sampling. Finally, the adaptive threshold focused classification loss ATFL is introduced to reduce the impact of the uneven distribution of multi-scale samples on the detection results and improve the detection accuracy of multi-scale targets. The experimental results show that compared with the baseline model, the mAP of YOLO11-MDA in TrashCan dataset and Trash_ICRA19 dataset reaches 91.4% and 97% respectively, which is an enhancement of 3.1% and 10.7%, and the FPS reaches the detection speed of 354.3 fps, which fully demonstrates that the overall performance of the improved model outperforms that of other algorithms, and it can provide an effective method for the automated monitoring of underwater environments.

Steel defect detection is critical for industrial quality control, yet performance is constrained by multi-scale variations, small targets, and background interference. To enhance the accuracy and efficiency of the detection model, this paper proposes a defect detection network based on an improved version of YOLO11, named LiteSteel-YOLO. First, a Lightweight Multi-Scale Fusion module (C3k2-LMSF) is designed to enhance multi-scale defect perception through fused convolutional kernels and feature guidance mechanisms. Second, a spatial-channel aware upsampling module (SCAM) is proposed, which improves the robustness of small target detection and suppresses noise through channel reorganization and spatial offset operations. Finally, an Efficient-Head detector optimized via structural reconfiguration is introduced to maximize computational efficiency. Experimental results show that the LiteSteel-YOLO receives mAP@50 of 81.7% and 70.7% with inference speed of 338 and 530 FPS on the NEU-DET and GC10-DET datasets (surpassing YOLO11 by 4.0% and 2.3%). The proposed framework enhances the accuracy and efficiency of steel defect detection, providing a solution for industrial inspection scenarios.