In order to solve the existing distracted driving behaviour detection algorithms’ problems such as low recognition accuracy, high leakage rate, high false recognition rate, poor real-time performance, etc., and to achieve high-precision real-time detection of common distracted driving behaviours (mobile phone use, smoking, drinking), this paper proposes a driver distracted driving behaviour recognition algorithm based on YOLOv5. Firstly, to address the problem of poor real-time identification, the computational and parametric quantities of the network are reduced by introducing a lightweight network, Ghostnet. Secondly, the use of GSConv reduces the complexity of the algorithm and ensures that there is a balance between the recognition speed and accuracy of the algorithm. Then, for the problem of missed and misidentified cigarettes during the detection process, the Soft-NMS algorithm is used to reduce the problems of missed and false detection of cigarettes without changing the computational complexity. Finally, in order to better detect the target of interest, the CBAM is utilised to enhance the algorithm’s attention to the target of interest. The experiments show that on the homemade distracted driving behaviour dataset, the improved YOLOv5 model improves the mAP@0.5 of the YOLOv5s by 1.5 percentage points, while the computational volume is reduced by 7.6 GFLOPs, which improves the accuracy of distracted driving behaviour recognition and ensures the real-time performance of the detection speed.

Object detection of small and occluded targets in complex scenarios is a vital yet challenging task in computer vision, with applications in intelligent systems (e.g., kitchen safety supervision). To address limitations of existing models, this study proposes an improved YOLOv10 algorithm with three key innovations. We first introduce a Mosaic-9 data augmentation strategy to enhance small target density in training samples. The traditional PANet in YOLOv10 is replaced by the Bidirectional Feature Pyramid Network (BiFPN), which uses cross-scale bidirectional connections and learnable weights to optimize multi-scale feature fusion. A Squeeze-and-Excitation (SE) channel attention module is integrated into the CSPDarknet backbone to emphasize key feature channels and mitigate background interference for occluded objects. Experiments on a self-constructed dataset (6508 images with multi-scale and occluded targets) show the improved YOLOv10 achieves 69.5% mAP@0.5, a 7.7 percentage point increase over YOLOv10n, while maintaining 12.1 ms inference speed. Ablation studies verify Mosaic-9 enhances small target perception, BiFPN boosts mAP@0.5 by 5.7%, and SE improves occlusion robustness by 4.8%. This work offers a generalizable multi-module optimization framework for YOLO-series models, applicable to various small and occluded target detection tasks, advancing lightweight object detection algorithms and intelligent vision systems.

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder in women that is frequently diagnosed by ultrasound imaging by examining ovarian abnormality. Ultrasound images, though, are normally contaminated with speckle noise that degrades image quality and poses difficulties for diagnosis. Conventional denoising methods like mean and median filtering are unable to eliminate noise properly while maintaining fine details. To solve this problem, this paper introduces a new Attention-based Autoencoder (AAE) for denoising PCOS ultrasound images. The model uses an attention mechanism to selectively amplify significant image areas and suppress noise, enhancing image quality for diagnosis. The introduced method was tested on a publicly available ultrasound dataset with synthetic speckle noise at various levels (variance 0.5, 0.02, and 0.001). Experimental results prove that the suggested method performs better than conventional denoising methods, with peak signal-to-noise ratio (PSNR) values of 31.33, 34.25, and 36.23, respectively. The structural similarity index measure (SSIM) also reveals notable improvements and corresponding scores of 85.21, 92.33, and 99.25. Beyond technical performance, this work supports the development of scalable, AI-driven PCOS diagnostic tools within a technopreneurship incubator model. These results indicate that AAEs can improve ultrasound image quality, facilitating more accurate PCOS diagnosis.

To address the mission planning challenge for agile satellites in dense point target observation, a clustering strategy based on an ant colony algorithm and a heuristic simulated genetic annealing optimization algorithm are proposed. First, the imaging observation process of agile satellites is analyzed, and an improved ant colony algorithm is employed to optimize the clustering of observation tasks, enabling the satellites to complete more observation tasks efficiently with a more stable attitude. Second, to solve for the optimal group target observation sequence and achieve higher total observation benefits, a task planning model based on multi-target observation benefits and attitude maneuver energy consumption is established, considering the visible time windows of targets and the time constraints between adjacent targets. To overcome the drawbacks of traditional simulated annealing and genetic algorithms, which are prone to local optimal solution and a slow convergence speed, a novel Simulated Genetic Annealing Algorithm is designed while optimizing the sum of target observation weights and yaw angles while also accounting for factors such as target visibility windows and satellite attitude transition times between targets. Ultimately, the feasibility and efficiency of the proposed algorithm are substantiated by comparing its performance against traditional heuristic optimization algorithms using a dataset comprising large-scale dense ground targets.