Hongyu Lin, Feng Jiang^*, Yu Jiang, Huiyin Luo, Jian Yao, Jiaxin Liu

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5321-5336, 2023, DOI:10.32604/cmc.2023.036893

Abstract Detecting non-motor drivers’ helmets has significant implications for traffic control. Currently, most helmet detection methods are susceptible to the complex background and need more accuracy and better robustness of small object detection, which are unsuitable for practical application scenarios. Therefore, this paper proposes a new helmet-wearing detection algorithm based on the You Only Look Once version 5 (YOLOv5). First, the Dilated convolution In Coordinate Attention (DICA) layer is added to the backbone network. DICA combines the coordinated attention mechanism with atrous convolution to replace the original convolution layer, which can increase the perceptual field of the network to get more… More >

Yanman Li¹, Jun Zhang¹, Yang Hu¹, Yingnan Zhao^2,*, Yi Cao³

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1219-1230, 2022, DOI:10.32604/csse.2022.028224

Abstract Safety helmet-wearing detection is an essential part of the intelligent monitoring system. To improve the speed and accuracy of detection, especially small targets and occluded objects, it presents a novel and efficient detector model. The underlying core algorithm of this model adopts the YOLOv5 (You Only Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (Complete Intersection Over Union) Loss function, and the Mish activation function. First, it applies the attention mechanism in the feature extraction. The network can learn the weight of each channel independently and enhance… More >

Wei Chen¹, Mi Liu^1,*, 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 >