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Keywords = safety helmet wearing (SHW) detection

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14 pages, 1328 KiB  
Article
A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3
by Haikuan Wang, Zhaoyan Hu, Yuanjun Guo, Zhile Yang, Feixiang Zhou and Peng Xu
Appl. Sci. 2020, 10(19), 6732; https://doi.org/10.3390/app10196732 - 25 Sep 2020
Cited by 49 | Viewed by 6908
Abstract
In the practical scenario of construction sites with extremely complicated working environment and numerous personnel, it is challenging to detect safety helmet wearing (SHW) in real time on the premise of ensuring high precision performance. In this paper, a novel SHW detection model [...] Read more.
In the practical scenario of construction sites with extremely complicated working environment and numerous personnel, it is challenging to detect safety helmet wearing (SHW) in real time on the premise of ensuring high precision performance. In this paper, a novel SHW detection model on the basis of improved YOLOv3 (named CSYOLOv3) is presented to heighten the capability of target detection on the construction site. Firstly, the backbone network of darknet53 is improved by applying the cross stage partial network (CSPNet), which reduces the calculation cost and improves the training speed. Secondly, the spatial pyramid pooling (SPP) structure is employed in the YOLOv3 model, and the multi-scale prediction network is improved by combining the top-down and bottom-up feature fusion strategies to realize the feature enhancement. Finally, the safety helmet wearing detection dataset containing 10,000 images is established using the construction site cameras, and the manual annotation is required for the model training. Experimental data and contrastive curves demonstrate that, compared with YOLOv3, the novel method can largely ameliorate mAP by 28% and speed is improved by 6 fps. Full article
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