Analysis and Research on YOLOv5s Vehicle Detection with CA and BiFPN Fusion
Muyang Lin, Zhiwen Wang, Lincai Huang · 2022
An algorithm based on improved YOLOv5s is proposed to solve the problems of false and missing vehicle detections. Firstly, a coordinate-attention (CA) module is added to the backbone feature of an extraction network to obtain more important information during feature extraction and improve object detection accuracy. Then, the weighted bi-directional feature pyramid network (BiFPN) is adopted to replace the original PANet structure in the YOLOv5s network. This method enhances the multi-scale feature fusion of the model and improves the fusion efficiency. Experiment results present that the mean average precision (mAP) of the improved YOLOv5s algorithm on the BIT-Vehicle Dataset reaches 94.S%, which is 2.S% higher than that of the original YOLOv5s network, and the processing frame rate reaches 136.9, which allows real-time detection by satisfying its requirements.