@article{ref1,
title="Identification of miners' unsafe behaviors based on transfer learning and residual network",
journal="China safety science journal (CSSJ)",
year="2020",
author="Wen, T. and Wang, G. and Kong, X. and Liu, M. and Bo, J.",
volume="30",
number="3",
pages="41-46",
abstract="In order to accurately identify unsafe behaviors of miners and reduce occurrence of accidents in coal mines, an image recognition method combining transfer learning and deep residual network is proposed. Firstly, behavior instances of miners were divided into three dimensions, namely completely safe behaviors, relatively safe behaviors, and unsafe behaviors, among which completely safe behaviors included walking, sitting and standing, relatively safe behaviors included bending, squatting, lifting, pushing, pulling, waving and clapping, and unsafe behaviors included falling and throwing. Then, ResNet50 network was used for training, and transfer learning weight parameters of ImageNet data set were fine-tuned. Finally, 12 classification was conducted through full connection layer, and final classification results were checked against test data. The results show that residual network model based on transfer learning is superior to other deep neural network models in identification accuracy of falling and throwing movements, and it can effectively identify unsafe behaviors, thus avoiding accidents caused by human factors. © 2022 China Safety Science Journal. All rights reserved.<p /><p>Language: zh</p>",
language="zh",
issn="1003-3033",
doi="10.16265/j.cnki.issn1003-3033.2020.03.007",
url="http://dx.doi.org/10.16265/j.cnki.issn1003-3033.2020.03.007"
}