Algorithm for detecting wearing behavior of safety helmet based on improved YOLOv8
To achieve automatic detection of workers wearing safety helmets in industrial settings,a detection algorithm based on improved YOLOv8 network model is proposed.By adopting the Faster-Net approach,we refined the C2f structure in the original YOLOv8 model,significantly reducing the model's parameter count and computational requirements.Additionally,we incorporated an EMA at-tention mechanism into the detection head to enhance the model's feature detection capabilities.Fur-thermore,we introduced SAConv and ASFF algorithms into the detection head to facilitate adaptive fusion of feature information across different scales,thereby improving the feature extraction capabili-ties of the refined model.Through ablation and comparative experiments,we demonstrate that the improved algorithm offers higher detection accuracy and faster detection speed,validating its feasibili-ty and suitability for practical applications in monitoring safety helmets wearing in industrial settings.
detection algorithmYOLOv8detecting wearing behavior of safety helmetFasterNetattention mechanism
万方数据
维普
