A lightweight model based on YOLOv5 for helmet wearing detection

Xiongxin Zou; Zuguo Chen; Yimin Zhou

doi:10.1117/12.2627279

14 February 2022 A lightweight model based on YOLOv5 for helmet wearing detection

Xiongxin Zou, Zuguo Chen, Yimin Zhou

Proceedings Volume 12161, 4th International Conference on Informatics Engineering & Information Science (ICIEIS2021); 121610Z (2022) https://doi.org/10.1117/12.2627279
Event: 4th International Conference on Informatics Engineering and Information Science, 2021, Tianjin, China

Abstract

Helmet wearing is a major concern for the safety and protection of people on the construction site. Statistic data demonstrate that injuries and accidents occur mainly due to not following prescribed procedures, i.e., not wearing helmet. Camera-based surveillance system can conduct online monitoring task to detect such abnormalities through captured images with image processing system analysis. Although deep learning-based method can achieve higher image identification performance, it requires extensive hardware support of the computational resources. Therefore, it is imperative to design a lightweight network with lower hardware requirement to address such problem. In this paper, a GhostNet, YOLOv5 and a lightweight network are combined to design a model to analyze the image for online monitoring with faster processing speed. The performance of the proposed model is compared with those of the mainstream lightweight models. Experimental results have demonstrated that the proposed model has higher detection accuracy and flexible adaptability.

Citation Download Citation

Xiongxin Zou, Zuguo Chen, and Yimin Zhou "A lightweight model based on YOLOv5 for helmet wearing detection", Proc. SPIE 12161, 4th International Conference on Informatics Engineering & Information Science (ICIEIS2021), 121610Z (14 February 2022); https://doi.org/10.1117/12.2627279

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KEYWORDS

Performance modeling

Detection and tracking algorithms

Image processing

Convolution

Head

Instrument modeling

Safety

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