@article{ref1,
title="Detection method of high-altitude safety protective equipment for construction workers based on deep learning",
journal="China safety science journal (CSSJ)",
year="2022",
author="Zhang, M. and Han, Y. and Liu, Z.",
volume="32",
number="5",
pages="140-146",
abstract="In order to address the lack of dynamics and coordination in current detection technology of safety protection equipment, a new detection method for construction workers working at height was proposed based on deep learning. Comprehensive detection of helmet and seat belt in state of dynamic videos was realized by replacing backbone feature extraction network of YOLOv4 with lightweight network MobileNetV2. Then, tests were carried out to verify effectiveness of the method. The results show that detection speed of the proposed method is increased by 2. 7 times in central processing unit (CPU) operating environment, and single-frame video detection speed for single target, multi-target and small target can be maintained between 25-27 milliseconds in graphics processing unit (GPU) operating environment. At the same time, mean average precision rate of 91. 57%, 89. 69% and 86. 63% can be achieved. © PHYSOR 2022 China Safety Science Journal. All rights reserved.<p /><p>Language: zh</p>",
language="zh",
issn="1003-3033",
doi="10.16265/j.cnki.issn1003-3033.2022.05.1141",
url="http://dx.doi.org/10.16265/j.cnki.issn1003-3033.2022.05.1141"
}