• 图像处理 •

### 基于改进YOLOv5的安全帽佩戴检测

1. （1.国网浙江省电力有限公司宁波供电公司物资部，浙江宁波315000；
2.国网浙江省电力有限公司宁波市奉化区供电有限公司，浙江宁波315599）
• 出版日期:2022-06-23 发布日期:2022-06-23
• 作者简介:岳衡(1985—),男,河北唐山人,工程师,硕士,研究方向:供应链运营管理,物力仓储管理,计算机视觉,E-mail: 18858252809@163.com; 黄晓明(1973—),男,浙江宁波人,高级工程师,硕士,研究方向:超高压输电运维技术,E-mail: huangxiaoming@snapmail.cc; 林明辉(1983—),男,高级工程师,硕士,研究方向:供应链管理,E-mail: linminghui@snapmail.cc; 高明(1978—),男,高级工程师,硕士,研究方向:工程建设技术,电网调控运行技术,E-mail: gaoming@snapmail.cc; 李扬(1982—),男,工程师,硕士,研究方向:供应链运营管理,电器工程及其自动,E-mail: liyang@snapmail.cc; 陈凌(1989—),男,助理工程师,本科,研究方向:物资管理,E-mail: chenling@snapmail.cc。
• 基金资助:
国网浙江省电力有限公司双创项目（B711JZ200009）

### 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

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.