Enhancing live working safety: a helmet detection method for operators

Addressing the accuracy issues in detecting helmet-wearing personnel in power operation scenarios, there are challenges posed by safety helmets, which are small targets with features like reflection and shadow, resulting in limited available features and susceptibility to environmental interference. Several factors contribute to the challenge faced by detection models in accurately pinpointing and identifying whether personnel are wearing helmets correctly. To tackle these challenges, this paper introduces an enhanced helmet detection algorithm, utilizing YOLOv8n as its backbone. Firstly, Introducing a Global Attention Mechanism (GAM) into the YOLOv8n detection network, improving deep neural network performance, minimizing information loss, and enhancing global feature interaction. This improvement aids in capturing crucial features of targets, especially small targets like helmet straps. Secondly, the introduction of the P2 small object detection layer enables YOLOv8 to more effectively detect small target objects. Consequently, the model demonstrates enhanced feature extraction and adaptive generalization abilities for small targets exhibiting various deformations. Through the dedicated P2 layer, YOLOv8 can detect and locate small targets more sensitively, thereby enhancing the accuracy of small object detection. Experimental results demonstrate that the improved YOLOv8n achieves an average precision mAP0.5 of 87.5%, representing a 6.8% improvement over the original YOLOv8n. This enhancement effectively boosts the accuracy of helmet detection