Lightweight object detection network for helmets on the road
Nanjun Ye, Shupeng Zhang · 2023
In this work, a lightweight object detection network based on YOLOv5 is proposed to solve the detection problem of nonmotor vehicle helmet wearing on the road. By replacing the original CSPDarknet53 backbone network with MobileNetv3, the number of network model parameters is reduced and the computing speed is increased. With further introduction of the criss-cross Attention, it improves the accuracy of the simplified network and improves the detection reliability. Compared with YOLOv5 network, the detection efficiency is improved 140%(FPS) at the cost of reducing 17.1%mAP, hence the task of helmet detection on the road can run in real time on the edge computing equipment.