YOLOv5s-MC: Lightweight road target detection network

Abstract

For the problem of large number of target detection algorithm parameters, a lightweight real-time detection algorithm YOLOv5s-MC based on improved YOLOv5s road scenes is proposed. firstly, CA attention is added to the model to improve the sensitivity of the network to detect targets; secondly, in the feature fusion network, add adaptive weight parameters using AS-Concat structure are added to better fuse the feature information of different layers and improve the detection accuracy of the algorithm ; adding a small target detection layer to improve the detection accuracy of tiny targets; finally introducing Mobilnetv2, a lightweight network, as the overall backbone layer to realize the lightweight requirement of the network; to verify the advantages of the proposed algorithm, experiments were conducted on the kitti dataset. The experimental results show that the proposed algorithm, compared with the original network, improves the average accuracy by 0.2% with 55.8% less parameters and 33.7% less computation, and the detection speed reaches 35 FPS, which meets the requirements of real-time detection and improves the ability of algorithm deployment in weak hardware computing power scenarios to a certain extent.