Safety helmet wearing detection algorithm in complex scenarios based on improved YOLOv8n

To address the challenges faced by safety helmet detection in complex environments, particularly the impact of fog, dust, occlusion, and varying lighting conditions , existing algorithms exhibit poor performance. To overcome these issues, this paper proposes an improved YOLOv8n algorithm. First, the Channel-Grouped Multi-Scale Convolution Module (CMSC) is integrated into C2f, using multi-scale convolution to enhance feature representation in foggy and dusty environments, reducing blurring. Second, the detection head is improved by switching from parallel to serial structure and introducing Partial Convolution (PConv), enhancing occlusion handling. Finally, WIoU v3 optimizes bounding box regression under varying lighting conditions via adaptive weight adjustment. Compared to the baseline, the method improves accuracy by 0.3%, recall by 2.7%, [email protected] by 2.2%, while reducing parameters to 2.5M and GFLOPs by 2.7G, achieving a balance between efficiency and accuracy.