In view of the significant influence of background environment on the existing road safety helmet detec-tion algorithm and the issue of low detection accuracy in detection scenarios such as occlusion and similarity between tar-gets and the environment,this paper considers the YOLOv8 model from the perspectives of feature fusion and loss calcu-lation.By utilizing the progressive feature pyramid network structure to reduce the substantial semantic gap in the process of multi-scale feature fusion,the algorithm's detection capability in complex scenes is enhanced.Additionally,the pro-posed PCAHead for detection and HelmetIoU bounding box loss function optimize the model's understanding and data processing capabilities,improving the efficiency and accuracy of model loss calculation,thereby accelerating model con-vergence.Experimental results show that the mAP@50 of the Helmet-YOLOn algorithm and Helmet-YOLOs algorithm have increased by 3.7 percentage points and 2.9 percentage points,respectively,outperforming all models of the same scale in the experiment.Furthermore,the large-scale model of Helmet-YOLO also outperforms most experimental models in terms of latency and accuracy.The experiments demonstrate that the Helmet-YOLO algorithm has higher accuracy and robustness,making it more suitable for road safety helmet detection in complex scenarios.
万方数据
维普
