GBSG-YOLOv8n: A Model for Enhanced Personal Protective Equipment Detection in Industrial Environments

Abstract

The timely and accurate detection of whether or not workers in an industrial environment are correctly wearing personal protective equipment (PPE) is paramount for worker safety. However, current PPE detection faces multiple inherent challenges, including complex backgrounds, varying target size ranges, and relatively low accuracy. In response to these challenges, this study presents a novel PPE safety detection model based on YOLOv8n, called GBSG-YOLOv8n. First, the global attention mechanism (GAM) is introduced to enhance the feature extraction capability of the backbone network. Second, the path aggregation network (PANet) structure is optimized in the Neck network, strengthening the model’s feature learning ability and achieving multi-scale feature fusion, further improving detection accuracy. Additionally, a new SimC2f structure has been designed to handle image features and more effectively improve detection efficiency. Finally, GhostConv is adopted to optimize the convolution operations, effectively reducing the model’s computational complexity. Experimental results demonstrate that, compared to the original YOLOv8n model, the proposed GBSG-YOLOv8n model in this study achieved a 3% improvement in the mean Average Precision (mAP), with a significant reduction in model complexity. This validates the model’s practicality in complex industrial environments, enabling a more effective detection of workers’ PPE usage and providing reliable protection for achieving worker safety. This study emphasizes the significant potential of computer vision technology in enhancing worker safety and provides a robust reference for future research regarding industrial safety.