LAPSE:2023.28405
Published Article
LAPSE:2023.28405
A Transfer Learning Methodology for Recognizing Unsafe Behavior during Lifting Operations in a Chemical Plant
April 11, 2023
Large lifting equipment is used regularly in the maintenance operations of chemical plant installations, where safety controls must be carried out to ensure the safety of lifting operations. This paper presents a convolutional neural network (CNN) methodology, based on the PyTorch framework, to identify unsafe behavior among lifting operation drivers, specifically, by collecting 22,352 images of equipment lifting operations over a certain time period in a chemical plant. The lifting drivers’ behavior was divided into eight categories, and a ResNet50 network model was selected to identify the drivers’ behavior in the pictures. The results show that the proposed ResNet50 network model based on transfer learning achieves a 99.6% accuracy rate, a 99% recall rate and a 99% F1 value for the expected behaviors of eight lifting operation drivers. This knowledge regarding unsafe behavior in the chemical industry provides a new perspective for preventing safety accidents caused by the dangerous behaviors of lifting operation drivers.
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Keywords
behavior recognition, chemical operations, transfer learning, unsafe driver behavior
Subject
Suggested Citation
Li H, Xue X, Wang Y, Wu L, Li X. A Transfer Learning Methodology for Recognizing Unsafe Behavior during Lifting Operations in a Chemical Plant. (2023). LAPSE:2023.28405
Author Affiliations
Li H: School of Resources Engineering, Xi’an University of Architecture and Technology, No.13 Yanta Road, Xi’an 710055, China
Xue X: School of Resources Engineering, Xi’an University of Architecture and Technology, No.13 Yanta Road, Xi’an 710055, China
Wang Y: School of Resources Engineering, Xi’an University of Architecture and Technology, No.13 Yanta Road, Xi’an 710055, China; Shaanxi Construction Engineering Group Corporation, NO.11 Construction Engineering Group Company Limited, Wenxing West Road, Xiany
Wu L: School of Resources Engineering, Xi’an University of Architecture and Technology, No.13 Yanta Road, Xi’an 710055, China
Li X: School of Resources Engineering, Xi’an University of Architecture and Technology, No.13 Yanta Road, Xi’an 710055, China
Xue X: School of Resources Engineering, Xi’an University of Architecture and Technology, No.13 Yanta Road, Xi’an 710055, China
Wang Y: School of Resources Engineering, Xi’an University of Architecture and Technology, No.13 Yanta Road, Xi’an 710055, China; Shaanxi Construction Engineering Group Corporation, NO.11 Construction Engineering Group Company Limited, Wenxing West Road, Xiany
Wu L: School of Resources Engineering, Xi’an University of Architecture and Technology, No.13 Yanta Road, Xi’an 710055, China
Li X: School of Resources Engineering, Xi’an University of Architecture and Technology, No.13 Yanta Road, Xi’an 710055, China
Journal Name
Processes
Volume
11
Issue
3
First Page
971
Year
2023
Publication Date
2023-03-22
ISSN
2227-9717
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Original Submission
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PII: pr11030971, Publication Type: Journal Article
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LAPSE:2023.28405
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External Link
https://doi.org/10.3390/pr11030971
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[v1] (Original Submission)
Apr 11, 2023
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Apr 11, 2023
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