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IJMLC 2022 Vol.12(3): 79-84 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2022.12.3.1083

Real-time Identification of Worker’s Personal Safety Equipment with Hybrid Machine Learning Techniques

Wen-Der Yu, Hsien-Chou Liao, Wen-Ta Hsiao, Hsien-Kuan Chang, Ting-Yu Wu, and Chen-Chung Lin

Abstract—This paper presents an application of the hybrid Machine Learning (ML) techniques to real-time detection of unsafe personal safety equipment (e.g., helmet and safety vest) of construction workers on site, so that the unsafe behaviors can be corrected timely to reduce safety risks. Three different Convolutional Neural Network (CNN) based Deep Learning (DL) techniques were adopted for worker position locating, object classification, and subtle feature detection, including Faster R-CNN, YOLO and DenseNet. The lab testing showed high detectability with the Recall of 95% and the Precision of 90%. In in-situ implementation of a real-world construction site, a moderately acceptable detectability was achieved, with the Cleanness of 85% and Correctness of 80%. It is concluded that the proposed method quotes profound potentials to enhance the current safety management practice of construction site.

Index Terms—Construction safety management, computer visualization, machine learning, convolutional neural networks.

W. D. Yu, W. T. Hsiao, and H. K. Chang are with the Department of Construction Engineering, Chaoyang University of Technology, Taichung, 41349 Taiwan (Corresponding author: H. K. Chang; e-mail: wenderyu@cyut.edu.tw, wdshiau@cyut.edu.tw, hkchang@cyut.edu.tw).
H. C. Liao is with the Department of Computer Science and Information Technology, Chaoyang University of Technology, Taichung, 41349 Taiwan (e-mail: cliao@cyut.edu.tw).
T. Y. Wu is with the A2 I Center at Chaoyang University of Technology, Taichung, 41349 Taiwan (e-mail: s10727603@gm.cyut.edu.tw).
C. C. Lin is with the Institute of Labor, Occupational Safety and Health, Ministry of Labor, New Taipei City, Taiwan (e-mail: lcc@mail.iosh.gov.tw).

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Cite: Wen-Der Yu, Hsien-Chou Liao, Wen-Ta Hsiao, Hsien-Kuan Chang, Ting-Yu Wu, and Chen-Chung Lin, "Real-time Identification of Worker’s Personal Safety Equipment with Hybrid Machine Learning Techniques," International Journal of Machine Learning and Computing vol. 12, no. 3, pp. 79-84, 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijmlc@ejournal.net


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