An improved YOLOv5s target detection algorithm

The existing YOLOv5s object detection model has issues of missing small targets and lower detection accuracy in complex scenes. Therefore, YOLOv5s needs to be optimized based on these problems. Firstly, the coordinate attention mechanism and the structure based on receptive field Block are introduced to improve the accuracy of the model. Secondly, the coordinate attention mechanism and the omni-dimensional dynamic convolutional structure are introduced to improve the situation of missing small targets. Selected part of the data in PASCAL VOC2007 for experiments, and the final results show that the model in this paper improves the recall by 1.7%, the average precision mAP50 by 2%, and the average precision mAP50-95 by 3% compared to the yolov5s model. The model in this paper improves the detection accuracy to a certain extent and improves the leakage detection of important targets and is tested in industrial scenarios with good results.