An improved YOLOv8 safety helmet wearing detection network

In the field of industrial safety, wearing helmets plays a vital role in the protection of workers. Aiming at the complex background in the industrial environment, caused by the difference in distance, the helmet small target wear detection of misdetection, omission detection problem, an improved YOLOv8 safety helmet wearing detection network is proposed, which aims to enhance the capture of details, improve multi- scale feature processing and improve the accuracy of small target detection by introducing DWR(Dilation-wise Residual) attention module, ASPP(Atrous Spatial Pyramid Pooling) pool pyramid. Experiments are conducted on the SHWD dataset, and the results show that the mAP of the improved network is enhanced to 92.0%, and it exceeds the traditional target detection network in terms of accuracy, recall, and other key metrics, which further improves the detection of helmet wearing in complex environments, and greatly enhances the accuracy of detection.