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Worker’s Helmet Recognition and Identity Recognition Based on Deep Learning
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作者 Jie Wang Guangzu Zhu +1 位作者 Shiqi Wu Chunshan Luo 《Open Journal of Modelling and Simulation》 2021年第2期135-145,共11页
For decades, safety has been a concern for the construction industry. Helmet detection caught the attention of machine learning, but the problem of identity recognition has been ignored in previous studies, which brin... For decades, safety has been a concern for the construction industry. Helmet detection caught the attention of machine learning, but the problem of identity recognition has been ignored in previous studies, which brings trouble to the subsequent safety education of workers. Although, many scholars have devoted themselves to the study of person re-identification which neglected safety detection. The study of this paper mainly proposes a method based on deep learning, which is different from the previous study of helmet detection </span><span style="font-family:Verdana;">and human identity recognition and can carry out helmet detection and</span><span style="font-family:Verdana;"> identity recognition for construction workers. This paper proposes a computer vision-based worker identity recognition and helmet recognition method. We collected 3000 real-name channel images and constructed a neural network based on </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">You Only Look Once (YOLO) v3 model to extract the features of the construction worker’s face and helmet, respectively. Experiments show that the method has a high recognition accuracy rate, fast recognition speed, accurate recognition of workers and helmet detection, and solves the problem of poor supervision of real-name channels. 展开更多
关键词 Construction Safety human identity recognition Helmet recognition Computer Vision Deep Learning
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