Safety Helmet Wearing Detection Model Based on Improved YOLO-M

The existing helmet-wearing detection algorithm's weaknesses such as many parameters, substantial detection interferences, and poor detection accuracy. We proposes a new helmet wearing detection model called YOLO-M, which uses MobileNetv3 as the backbone set of connections for feature removal, which can reduce the quantity of model parameters and model size. A residual edge for feature fusion, and At last, by changing the connection between CAM and SAM, a new attention module BiCAM is designed. The experiments show that YOLO-M has a higher detection accuracy and faster detection speed than other models, while also reducing the quantity of model parameters