@article{ref1,
title="A remote-vision-based safety helmet and harness monitoring system based on attribute knowledge modeling",
journal="Remote sensing (Basel)",
year="2023",
author="Wu, Xiao and Li, Yupeng and Long, Jihui and Zhang, Shun and Wan, Shuai and Mei, Shaohui",
volume="15",
number="2",
pages="e347-e347",
abstract="Remote-vision-based image processing plays a vital role in the safety helmet and harness monitoring of construction sites, in which computer-vision-based automatic safety helmet and harness monitoring systems have attracted significant attention for practical applications. However, many problems have not been well solved in existing computer-vision-based systems, such as the shortage of safety helmet and harness monitoring datasets and the low accuracy of the detection algorithms. To address these issues, an attribute-knowledge-modeling-based safety helmet and harness monitoring system is constructed in this paper, which elegantly transforms safety state recognition into images’ semantic attribute recognition. Specifically, a novel transformer-based end-to-end network with a self-attention mechanism is proposed to improve attribute recognition performance by making full use of the correlations between image features and semantic attributes, based on which a security recognition system is constructed by integrating detection, tracking, and attribute recognition. Experimental results for safety helmet and harness detection demonstrate that the accuracy and robustness of the proposed transformer-based attribute recognition algorithm obviously outperforms the state-of-the-art algorithms, and the presented system is robust to challenges such as pose variation, occlusion, and a cluttered background.<p /> <p>Language: en</p>",
language="en",
issn="2072-4292",
doi="10.3390/rs15020347",
url="http://dx.doi.org/10.3390/rs15020347"
}