Abstract:
The failure of construction workers to wear safety equipment as required is an important cause of safety accidents. In fortunate cases people are injured and severe cases can cause death. In order to prevent this from happening, this paper proposes an algorithm consisting of data augmentation for complex environments and object occlusion, more than 17,000 sample images and an advanced neural network model faster R-CNN. The experimental results show that the method can identify the personnel and helmets in the surveillance video in real time, and the average recognition accuracy is up to 90.3%, and the detection time is up to 0.037s per image. It satisfies the safety requirements of real-time detection of construction sites.
Source: 2018 International Conference on Electrical, Control, Automation and Robotics
Publication Date: 2018
Full Text URL: Link to URL
Publication Types: Books, Reports, Papers, and Research Articles
Topics: Construction Personnel; Hard Hats; Mathematical Models; Video Imaging Detectors; Worker Safety
Publication Date: 2018
Full Text URL: Link to URL
Publication Types: Books, Reports, Papers, and Research Articles
Topics: Construction Personnel; Hard Hats; Mathematical Models; Video Imaging Detectors; Worker Safety