Lightweight object detection network for helmets on the road

Nanjun Ye; Shupeng Zhang

doi:10.1117/12.2681042

8 June 2023 Lightweight object detection network for helmets on the road

Nanjun Ye, Shupeng Zhang

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127073G (2023) https://doi.org/10.1117/12.2681042
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

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.

Citation Download Citation

Nanjun Ye and Shupeng Zhang "Lightweight object detection network for helmets on the road", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127073G (8 June 2023); https://doi.org/10.1117/12.2681042

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