Optimization of target detection algorithm based on Yolov5

In order to improve the target detection performance of the detection model pair in the target tracking scenario, this paper abandons the use of the CSPDarknet53 backbone feature extraction network in the original algorithm and selects the lightweight network MobileNetV3-large as the new backbone feature extraction network. And the channel attention SE module in the original inverted residual structure of the network is changed to the integrated attention module based on the channel dimension and spatial dimension. According to the training results on the dataset CrowdHuman, compared with the original Yolov5 model, the improved detection algorithm improves the detection accuracy by 1.5% and the detection speed by 10.2 frames per second, which is more in line with the real-time requirements of the automatic driving scene.