Helmet wearing detection algorithm based on improved YOLOv8

This is the authors' abstract. We don't add key points for this paper.

Aiming at the problems of low detection accuracy and slow detection speed of existing helmet detection models in complex environments, an improved YOLOv8n helmet wearing detection algorithm is proposed in this paper. Firstly, CBAM attention mechanism is added to the backbone network to strengthen the feature extraction capability of the backbone network. Then SimSPPF module is used to replace SPPF module in backbone network to improve the speed of model detection. Finally, DIoU-NMS is used instead of NMS to enhance the detection of occluded targets. The experimental results show that the average detection accuracy of the improved YOLOv8n algorithm is 94.85% and the detection speed is 109.11 FPS, which is 1.41% higher than that of the improved YOLOv8n algorithm.