A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3
Source abstract
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 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Occupational Health and Safety Research · Advanced Neural Network Applications · Fire Detection and Safety Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
School of Informatics, University of Leicester, Leicester LE1 7RH, UK
Haier Digital Technology (Shanghai) Co., Ltd., Shanghai 201600, China
Funding
National Science Foundation of China · Key Project of Science and Technology Commission of Shanghai Municipality · Shenzhen Science and Technology Innovation Commission application demonstration project · Natural Science Foundation of Guangdong Province · Outstanding Young Researcher Innovation Fund of Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
References (28)
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