In view of complex background interference and foreign object occlusion in the construction scenes,which reduces the accuracy of helmet wearing detection,we propose a safety helmet wearing detection algorithm for complex construction scenes.This paper improves the YOLOv5 algorithm,adding the Coordinate Attention(CA)mechanism,replacing the first two layers in the backbone network using the Stem Block,applying a Decoupled detection Head(DH)structure with the addition of the Coordinate Attention mechanism.At the same time,an additional large-scale feature extraction layer is added.Results on the helmet dataset show that the improved CADH-YOLOv5 algorithm with a mean detection precision of 91.2%can significantly im-prove the performance of safety helmet wearing detection for complex construction scenes,which is superior to similar algo-rithms,and has limited real-time performance.
维普
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