Computer and Modernization ›› 2022, Vol. 0 ›› Issue (06): 104-108.

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Helmet-wearing Detection Based on Improved YOLOv5

  

  1. (1. Dept. of Materials of Ningbo Power Supply Company, State Grid Zhejiang Province Electric Power Company, Ningbo 315000, China;
    2. Ningbo Fenghua District Power Supply Company, State Grid Zhejiang Province Electric Power Company, Ningbo 315599, China)
  • Online:2022-06-23 Published:2022-06-23

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Abstract: To the problem that YOLOv5 cannot be focused by weights and cannot produce more distinguishable features, thereby reducing the accuracy of helmet detection, attention module was used. Besides, squeeze and excitation layer and efficient channel attention module were studied. To the problem that the non maximum suppression used by YOLOv5 to remove redundant results will only retain the highest confidence prediction frame of the same class when objects were highly overlapped, the Soft-NMS algorithm was used to keep more prediction boxes. Weighted non maximum suppression was used to fuse multiple prediction boxes information to improve the accuracy of the prediction boxes. For the problem of information loss caused by down-sampling , focus modules was used to improve the detection effect, and the various modules were integrated to obtain the optimal FESW-YOLO algorithm. Compared with YOLOv5, the algorithm improves the mAP@0.5 by 2.1 percentage points and the mAP@0.5:0.95 by1.2 percentage points on the helmet data set respectively, which improves the accuracy of safety helmet supervision.

Key words: object detection, helmet monitoring, convolutional network, deep learning