Abstract: Safety production management is an important policy for the development of high-risk enterprises such as the construction industry and heavy industry,and safety helmets play a key role in head protection in the production environment.Therefore,it is necessary to strengthen the supervision of helmet wearing.In recent years,the monitoring method of helmet wearing based on image vision has become the main means for enterprises to implement management.How to improve the detection accuracy and speed of helmet wearing is a crucial issue for applications.To deal with this issue,an improved YOLO v4 algorithm is proposed to promote the accuracy and efficiency of safety helmet wearing detection in this paper.First,a 128×128 feature map output is added to the original three feature map outputs of the YOLO v4 algorithm,and the 8 times downsampling of the feature map output is changed to 4 times downsampling to provide more small target features for subsequent feature fusion.Second,the feature fusion module is improved based on the idea of dense connection to realize feature reuse,so that the Yolo Head classifier responsible for small target detection can utilize the features of different levels,to obtain better target detection and classification results.Finally,comparative experiments are carried out.The results show that the average accuracy of the proposed method is 2.96% higher than the original network detection accuracy to be 91.17%,and the detection speed is basically unchanged to be 52.9 frame/s.Thereupon,the proposed algorithm can achieve better detection accuracy while meeting real-time detection requirements,and effectively realize the high-speed and high-precision detection of helmet wearing.

Key words: K-means clustering, Deep learning, Safety helmet wearing detection, Small target detection, YOLO v4