Wei Chen1, Mi Liu1,*, Xuhong Zhou2, Jiandong Pan3, Haozhi Tan4
CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3159-3174, 2022, DOI:10.32604/cmc.2022.026664
Abstract In construction, it is important to check whether workers wear safety helmets in real time. We proposed using an unmanned aerial vehicle (UAV) to monitor construction workers in real time. As the small target of aerial photography poses challenges to safety-helmet-wearing detection, we proposed an improved YOLOv4 model to detect the helmet-wearing condition in aerial photography: (1) By increasing the dimension of the effective feature layer of the backbone network, the model's receptive field is reduced, and the utilization rate of fine-grained features is improved. (2) By introducing the cross stage partial (CSP) structure into path aggregation network (PANet), the… More >
Login
Register
