Research on target detection algorithms for site safety equipment

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.