Enhanced Helmet Wearing Detection Using Improved YOLO Algorithm.

To address the accuracy limitations of existing safety helmet detection algorithms in complex environments, we propose an enhanced YOLOv8 algorithm, called YOLOv8- CSS. We introduce a Coordinate Attention (CA) mechanism in the backbone network to improve focus on safety helmet regions in complex backgrounds, suppress irrelevant feature interference, and enhance detection accuracy. We also incorporate the SEAM module to improve the detection and recognition of occluded objects, increasing robustness and accuracy. Additionally, we design a fine-neck structure to fuse features of different sizes from the backbone network, reducing model complexity while maintaining detection accuracy. Finally, we adopt the Wise-IoU loss function to optimize the training process, further enhancing detection accuracy. Experimental results show that YOLOv8-CSS significantly improves detection performance in general scenarios, complex backgrounds, and for distant small objects. YOLOv8-CSS improves precision, recall, mAP@0.5, and mAP@0.5:0.95 by 1.67%, 5.55%, 3.38%, and 5.87%, respectively, compared to YOLOv8n. Our algorithm also reduces model parameters by 21.25% and computational load by 15.89%. Comparisons with other mainstream object detection algorithms validate our approach's effectiveness and superiority. [ABSTRACT FROM AUTHOR]