Lightweight safety helmet detection algorithm for smart construction sites based on improved yolov8

Abstract

Safety helmet wearing detection is a crucial component of safety management in construction sites. Traditional detection methods based on manual monitoring are inefficient, while existing deep learning models often suffer from high computational costs and poor detection performance for small or occluded targets. To address these issues, this paper proposes a lightweight detection algorithm named GCW-YOLOv8. Firstly, the Ghost Module is introduced into the backbone network to replace the conventional convolution layers, significantly reducing the number of parameters and floating-point operations (FLOPs) while maintaining feature extraction capability. Secondly, the Coordinate Attention (CA) mechanism is embedded into the neck network to enhance the model's sensitivity to spatial location and channel information, thereby improving the detection accuracy of small targets. Finally, the Wise-IoU (WIoU) loss function is adopted to replace the original CIoU loss, utilizing a dynamic non-monotonic focusing mechanism to optimize the gradient assignment for low-quality samples. Experimental results on the safety helmet dataset show that the proposed algorithm achieves a mean Average Precision (mAP@0.5) of 94.5%, while the inference speed reaches 108 FPS. Compared with the baseline YOLOv8n, our method improves detection accuracy by 1.4% while reducing parameter count by 34.4%, achieving a superior balance between accuracy and efficiency for smart construction site applications.