Current - Issue

1. A. Barlybayev, N. Amangeldy, B. Kurmetbek, I. Krak, B. Razakhova, N. Tursynova, and R. Turebayeva, "Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study," Cogent Engineering,
vol. 11, no. 1, p. 2333209, 2024.
2. M. Ahmad, S. Khan, and R. Umer, "Personal protective equipment detection: a deep learning-based sustainable approach,"
Sustainability, vol. 15, no. 18, p. 13990, 2023.
3. A. J. Alhussein, "Personal protective equipment detection using YOLOv8," Cogent Engineering, vol. 11, no. 1, 2024.
4. F. Zhou and Y. Liu, "A systematic review of computer vision-based PPE compliance in industry practice," Artificial Intelligence
Review, vol. 57, 2024.5. R. Patel and M. Chauhan, "Automated PPE compliance monitoring in industrial construction and mining environments," Automation
in Construction, vol. 167, p. 104273, 2025.
6. P. Gupta et al., "PPE detector: a YOLO-based architecture to detect PPE in real-world imagery," Journal of Imaging, vol. 6, no. 7, p.
68, 2022.
7. H. Kim and J. Park, "Smart construction site safety monitoring using deep learning and IoT integration," IEEE Internet of Things
Journal, 2024.
8. S. Rahman et al., "YOLO-based real-time PPE detection for construction safety," IEEE Access, vol. 12, pp. 129456–129468, 2024.
9. K. Verma and A. Singh, "Deep learning models for helmet and vest detection in hazardous environments," Automation in Construction,
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10. D. Santos et al., "Computer vision-based worker tracking and PPE validation," Sensors, vol. 22, no. 18, p. 6789, 2022.
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Science and Engineering, 2021.
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13. J. Redmon and A. Farhadi, "YOLOv3: an incremental improvement," arXiv: 1804.02767, 2018.
14. A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "YOLOv4: optimal speed and accuracy of object detection," arXiv: 2004.10934,
2020.
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detectors," arXiv: 2207.02696, 2022.
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in Proc. IEEE CVPR, pp. 580–587, 2014.
18. R. Girshick, "Fast R-CNN," in Proc. IEEE ICCV, pp. 1440–1448, 2015.
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Advances in Neural Information Processing Systems, vol. 28, 2015.
20. W. Liu et al., "SSD: single shot multibox detector," in Proc. ECCV, pp. 21–37, Springer, 2016.
21. Z. Xie, H. Liu, Z. Li, and Y. He, "A CNN-based approach towards real-time hard hat detection," in Proc. IEEE PIC, pp. 430–434,
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22. Z. Wang et al., "Fast personal protective equipment detection for real construction sites using deep learning approaches," Sensors, vol.
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in Proc. Creative Construction e-Conference 2020, pp. 33–38, 2020.
24. Q. Fang et al., "Detecting non-hardhat-use by a deep learning method from far-field surveillance videos," Automation in Construction,
vol. 85, pp. 1–9, 2018.
25. J. Li et al., "Safety helmet wearing detection based on image processing and machine learning," in Proc. IEEE ICACI, pp. 201–205,
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26. Z. Zhu, M. W. Park, and N. Elsafty, "Automated monitoring of hardhats wearing for onsite safety enhancement," in Proc. 11th
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vol. 36, pp. 38–52, 2013.
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planning," Automation in Construction, vol. 60, pp. 74–86, 2015.
31. S. Barro-Torres, T. M. Fernández-Caramés, H. J. Pérez-Iglesias, and C. J. Escudero, "Real-time personal protective equipment
monitoring system," Computer Communications, vol. 36, no. 1, pp. 42–50, 2012.