A lightweight vehicle-pedestrian detection algorithm based on YOLOv5s

To enable the deployment of target detection algorithms for mobile device, we propose a lightweight improved algorithm based on YOLOv5s. Firstly, we propose a four-scale prediction network to enhance detection of small targets by adding a 160×160 prediction head to the Head layer of the network. Secondly, MobileNetV3s is chosen to replaceCSPDarkNet53 as the backbone network, and CA (Coordinate Attention) is introduced into the MobileNetV3s. We use Ghost Module to replace the traditional convolution in the Neck part. Finally, We use SIoU_Loss to replace CIoU_Loss. The experiments' results show that the Flops of this paper has decreased by 41.8% and the model size has decreasedby65.1% relative to the YOLOv5s before the improvement, and the mAP@.5 has reached 93.3%, providing theoretical support for the deployment of vehicle-pedestrian detection algorithm in mobile device.