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 and human identity recognition and
can carry out helmet detection and 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 the 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.
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