Safety Helmet Wearing Detection Algorithm for Complex Construction Scenes
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.
万方数据
维普
