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Cited by 32Advanced Neural Network ApplicationsOccupational Health and Safety ResearchInfrastructure Maintenance and Monitoring

The DOI matches its Crossref record; 25 of the 26 references it cites were found in a registry or an index.

A Detailed Comparative Analysis of You Only Look Once-Based Architectures for the Detection of Personal Protective Equipment on Construction Sites

Open access
Elesawy, Abdelrahman, Mohammed Abdelkader, Eslam, Osman, Hesham·2024·Eng
DOI 10.3390/eng5010019
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Abstract

For practitioners and researchers, construction safety is a major concern. The construction industry is among the world’s most dangerous industries, with a high number of accidents and fatalities. Workers in the construction industry are still exposed to safety risks even after conducting risk assessments. The use of personal protective equipment (PPE) is essential to help reduce the risks to laborers and engineers on construction sites. Developments in the field of computer vision and data analytics, especially using deep learning algorithms, have the potential to address this challenge in construction. This study developed several models to enhance the safety compliance of construction workers with respect to PPE. Through the utilization of convolutional neural networks (CNNs) and the application of transfer learning principles, this study builds upon the foundational YOLO-v5 and YOLO-v8 architectures. The resultant model excels in predicting six key categories: person, vest, and four helmet colors. The developed model is validated using a high-quality CHV benchmark dataset from the literature. The dataset is composed of 1330 images and manages to account for a real construction site background, different gestures, varied angles and distances, and multi-PPE. Consequently, the comparison among the ten models of YOLO-v5 (You Only Look Once) and five models of YOLO-v8 showed that YOLO-v5x6’s running speed in analysis was faster than that of YOLO-v5l; however, YOLO-v8m stands out for its higher precision and accuracy. Furthermore, YOLOv8m has the best mean average precision (mAP), with a score of 92.30%, and the best F1 score, at 0.89. Significantly, the attained mAP reflects a substantial 6.64% advancement over previous related research studies. Accordingly, the proposed research has the capability of reducing and preventing construction accidents that can result in death or serious injury.

Overview

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Crossref · 10.3390/eng5010019

Cites

25 of 25 references verified0 citation errors · 0 not checkable

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Citations

Cites

Research works printed but not linked: 3

  1. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). European Conference on Computer Vision, Springer.
  2. Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv.
  3. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv.

Cited by

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Metadata

Authors
Elesawy, A., Mohammed Abdelkader, E., & Osman, H.
Affiliations
  • Cairo University (EG)
  • Structural Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt
ORCID
Osman, Hesham
Venue
Eng
Publisher
MDPI AG
Published
2024-02-21
Volume
5
Issue
1
Pages
347-366
ISSN
2673-4117 (ISSN-L)
DOI
10.3390/eng5010019
OpenAlex
W4392004622
Cited by
24
References
26
Citations by year
2026: 14 · 2025: 8 · 2024: 2
FWCI
3.38
Type
journal article
Language
en
Open access
Licence
Locations
  • Eng—Advances in Engineering (publishedVersion, cc-by)
  • PolyU Institutional Research Archive (Hong Kong Polytechnic University) (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Social Sciences › Safety Research › Occupational Health and Safety Research
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Keywords
personal protective equipment detection, construction safety, YOLOv5, YOLOv8, object detection, safety compliance monitoring, transfer learning, convolutional neural networks, construction accident prevention

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