At present, UAV aerial photography has a good prospect in agricultural production, disaster response, and other aspects. The application of UAVs can greatly improve work efficiency and decision-making accuracy. However, owing to inherent features such as a wide field of view and large differences in the target scale in UAV aerial photography images, this can lead to existing target detection algorithms missing small targets or causing incorrect detections. To solve these problems, this paper proposes a small target detection algorithm for UAV aerial photography based on improved YOLOv5s. Firstly, a small target detection layer is applied in the algorithm to improve the detection performance of small targets in aerial images. Secondly, the enhanced weighted bidirectional characteristic pyramid Mul-BiFPN is adopted to replace the PANet network to improve the speed and accuracy of target detection. Then, CIoU was replaced by Focal EIoU to accelerate network convergence and improve regression accuracy. Finally, a non-parametric attention mechanism called the M-SimAM module is added to enhance the feature extraction capability. The proposed algorithm was evaluated on the VisDrone-2019 dataset. Compared with the YOLOV5s, the algorithm improved by 7.30%, 4.60%, 5.60%, and 6.10%, respectively, in mAP@50, mAP@0.5:0.95, the accuracy rate (P), and the recall rate (R). The experiments show that the proposed algorithm has greatly improved performance on small targets compared to YOLOv5s.

As urbanization accelerates, solid waste management has become one of the key issues in urban governance. Accurate and efficient waste sorting is a crucial step in enhancing waste processing efficiency, promoting resource recycling, and achieving sustainable development. However, there are still many challenges inherent in today’s garbage detection methods. These challenges include the high computational cost of detection, the complexity of the detection background, and the difficulty in accurately evaluating the spatial relationship between rectangular detection frames during the inspection process. Therefore, this study improves the latest YOLOv8s object detection model, introducing a garbage detection model that balances light weight and detection performance. Firstly, this study introduces a newly designed structure, the CG-HGNetV2 network, to optimize the backbone network of YOLOv8s. This novel framework leverages local features, surrounding context, and global context to enhance the accuracy of semantic segmentation. It efficiently extracts features through a hierarchical approach, significantly reducing the computational cost of the model. Additionally, this study introduces an innovative network called MSE-AKConv, which integrates an attention module into the network architecture. The irregular convolution operations facilitate efficient feature extraction, enhancing the ability to extract valid information from complex backgrounds. In addition, this study introduces a new method to replace CIoU (complete intersection over union). On the basis of calculating IoU (intersection over union), it also considers the outer boundary of the two rectangles. By calculating the minimum distance between the boundaries, this method handles cases where boundaries are close but not overlapping, offering a more detailed similarity assessment than that provided by traditional IoU. In this study, the model was trained and evaluated using a publicly available dataset. Specifically, the model has improved the precision (P), recall rate (R), and mAP@50 (mean average precision at 50) by 4.80%, 0.10%, and 1.30%, while reducing model parameters by 6.55% and computational demand by 0.03%. This study not only provides an efficient automated solution for waste detection, but also opens up new avenues for ecological environmental protection.

YOLOv5 is currently one of the mainstream algorithms for object detection. In this paper, we propose the FRL-YOLO model specifically for river floating object detection. The algorithm integrates the Fasternet block into the C3 module, conducting convolutions only on a subset of input channels to reduce computational load. Simultaneously, it effectively captures spatial features, incorporates reparameterization techniques into the feature extraction network, and introduces the RepConv design to enhance model training efficiency. To further optimize network performance, the ACON-C activation function is employed. Finally, by employing a structured non-destructive pruning approach, redundant channels in the model are trimmed, significantly reducing the model’s volume. Experimental results indicate that the algorithm achieves an average precision value (mAP) of 79.3%, a 0.4% improvement compared to yolov5s. The detection speed on the NVIDIA GeForce RTX 4070 graphics card reaches 623.5 fps/s, a 22.8% increase over yolov5s. The improved model is compressed to a volume of 2 MB, representing only 14.7% of yolov5s.

The YOLOv5 algorithm, widely utilized in printed circuit board (PCB) defect detection, undergoes a reduction in speed when deployed on compact industrial computing platforms due to its substantial parameter count. Consequently, this study proposes a lightweight and high-performance PCB defect detection model based on YOLOv5, named ASTKD-PCB-LDD.Employing the k-means++ algorithm for optimal anchor boxes and the SCYLLA-IoU(SIoU) loss function over the Complete IoU(CIoU) loss function, the model enhances accuracy in locating and detecting small-target defects.It designs the Faster-Ghost lightweight backbone network to replace the YOLOv5 backbone network, and employs a slim-neck neck architecture to supersede the YOLOv5 neck network, which significantly reduces computational requirements and enhances inference speed, yielding a more efficient PCB-LDD network. The align soft-target knowledge distillation(ASTKD) approach is crafted to employ the PCB-LDD model as the teacher and the pruned model (via the Layer-Adaptive Magnitude-based Pruning(LAMP)) as the student, aiming to preserve detection accuracy while reducing parameters. Experimental results demonstrate that the model size has been reduced from 14.5MB to 4MB, achieving a compression rate of 27.6%; the mean average precision (mAP) achieves 98%, and the detection speed is enhanced from 73.2 frames/s to 112.3 frames/s, resulting in a speedup of 153.4%. This approach successfully achieves the goal of combining high performance with lightweight design, significantly enhancing the accuracy and efficiency of PCB defect detection.