YOLO-ESCA: A High-Performance Safety Helmet Standard Wearing Behavior Detection Model Based on Improved YOLOv5

To solve the problem of workers incorrectly wearing helmets, this study proposes a standard helmet wear detection model, YOLO-ESCA based on improved YOLOv5n. This model can monitor workers' helmet wear in real time via UAVs and other means and automatically reduce video streaming detection results. The model is trained using a self-built dataset that containing 4400 images. To address the shortcomings of the original YOLOv5, an improved version of the proposed approach, in which the efficient intersection over union loss function (EIOU-loss), Soft-NMS nonmaximal suppression, and the convolutional block attention module (CBAM) are employed, is proposed, and a small target detection layer (ADL) is added to improve model performance. The experimental results show that the mAP@0.5 of the improved model is up to 94.7%, the FPS is up to 65.3, the model size is only 4.47MB, and that the number of detections on the self-constructed dataset and SHWD dataset is 41.7% and 73% greater, respectively, than that of the original model, respectively.