Research on target detection algorithms for site safety equipment

Jiacheng Xing

doi:10.1117/12.3029775

21 June 2024 Research on target detection algorithms for site safety equipment

Jiacheng Xing

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131673O (2024) https://doi.org/10.1117/12.3029775
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Safety helmets are the most common and practical personal protection tool on construction sites and can effectively prevent and mitigate head injuries caused by accidents. Detecting safety helmets and various safety equipment is a major task in the safety management of construction site personnel. It is also an important element in the intelligent monitoring technology of construction sites. With the development of artificial intelligence, it has now become an important part of intelligent construction site construction. The safety equipment target detection algorithm is investigated to comprehensively analyze the research status of artificial intelligence technology in safety equipment detection. In this paper, after introducing the attention mechanism into the yolov5s algorithm and adding the density residual module RDB and the cavity density residual module DRDB proposed in this paper, the accuracy of identifying helmet wearers is improved by 1.4% for aerial scenes and about 1.2% for seat belt wearers, and the accuracy of identifying helmet wearers is also improved slightly for ground scenes.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiacheng Xing "Research on target detection algorithms for site safety equipment", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131673O (21 June 2024); https://doi.org/10.1117/12.3029775

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