Abstract
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.
Highlights
Safety at the workplace has become the focal point of many organizations owing to the consequences resulting from an unsafe environment on the productivity and health of the workforce [1]
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
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
Summary
Safety at the workplace has become the focal point of many organizations owing to the consequences resulting from an unsafe environment on the productivity and health of the workforce [1]. About 80% 90% of accidents are strongly related to the unsafe acts and behavior of workers. Behavior-based safety (BBS) is an effective approach that can be used to observe and identify people’s unsafe actions [6]. Developments in technology, aided by computer vision have been identified as an effective approach to automatically recognize people’s unsafe behavior [7]. The field safety supervision can be divided into two methods based on computer vision and sensor. The vision-based approach is applied to activity detection and tracking of construction workers. Such as, detecting near-miss incident, unsafe worker motions and assigning specific tasks to workers [9]. Helmet detection and Human Identity Recognition is an important application of computer vision in construction site
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