Abstract
The construction site belongs to the high-risk operation area, and whether the safety protection articles are correctly worn will directly affect the personal safety of operators. Aiming at the problems of weak anti-interference and low detection accuracy of existing detection methods, a new detection method for safety appliances based on improved YOLOv5 is proposed. This method embeds the BoT module into YOLOv5 backbone network, and uses its self attention mechanism to further mine image feature clues, so as to enhance the network's ability to extract features from target regions. The experimental results show that the average precision mAP of the method based on the improved YOLOv5 is 87.1%, and the recall rate is 80.8%. Compared with the original YOLOv5, the precision is improved by two percent.
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