Helmet Wearing Detection Algorithm Base on Improved YOLOV5
LI
Shuangyuan1，LI Tianyu 2**
(1.Information Center,Jilin Institute of Chemical Technology,Jilin 132022,China;2.School of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin 132022,China)
Abstract: With the application of artificial intelligence technology in production and life, safety behavior detection technology has become a research hotspot in the field of construction. Aiming at the existing helmet wearing detection algorithms with low accuracy and poor model robustness and other problems, this paper proposes a helmet wearing detection model based on the combination of Coordinate Attention (CA) mechanism and YOLOv5. The optimized model in this paper significantly improves the accuracy and has good performance in small target detection. The accuracy of the algorithm combining the improved CA mechanism and YOLOv5 reaches 90.2%, which is 1.6 percentage points higher than the average accuracy of the YOLOv5 model, and the algorithm outperforms the other comparison models. After comparison experiments, it is verified that the model has better detection effect and higher network accuracy, and can still maintain stable performance and show good detection effect under complex construction scenarios.
李双远, 李天宇. 基于改进YOLOv5的安全帽佩戴检测算法[J]. 吉林化工学院学报, 2024, 41(9): 15-21.
LI Shuangyuan, LI Tianyu . Helmet Wearing Detection Algorithm Base on Improved YOLOV5. Journal of Jilin Institute of Chemical Technology, 2024, 41(9): 15-21.
