AI-Powered Computer Vision Systems for Automated Safety Compliance Monitoring and PPE Detection in Automobile Manufacturing Plants

Ankush Mahajan; Kirti Dharwadkar

doi:10.55524/ijirem.2026.13.4.2

Abstract

This study investigates the deployment of AI-powered computer vision systems for automated safety compliance monitoring and personal protective equipment (PPE) detection in automobile manufacturing. Four objectives guide the research evaluating detection accuracy measuring incident reporting speed improvements assessing worker acceptance and identifying data security challenges. A mixed-method approach combined with system performance logs with responses from a 100-person survey. Hypothesis tests include one-sample t-tests for accuracy benchmarks paired t-tests for reporting times one-sample t-tests for acceptance scores and chi-square tests for security challenge reports. Results indicate the vision model achieves 92% PPE detection accuracy (t(99)=6.5p<0.001) reduces reporting time by an average of 3.2 minutes (t(99)=7.8p<0.001) yields high acceptance (mean=4.1 t(99)=8.2p<0.001) but reveals moderate security concerns (χ²(1)=12.4p<0.001). Practical implications highlight the need for robust encryption user training and continuous model retraining. Future research should examine long-term behavioral impacts and cross-plant scalability.

Keywords

Computer Vision, Personal Protective Equipment Safety, Compliance Automated Monitoring AI, Surveillance Manufacturing Safety, Incident Reporting Model Accuracy, Worker Acceptance, Data Security

References

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 779–788. Available from: https://doi.org/10.1109/CVPR.2016.91
J. Redmon and A. Farhadi, "YOLO9000: Better, Faster, Stronger," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 6517–6525. Available from: https://doi.org/10.1109/CVPR.2017.690
K. He, G. Gkioxari, P. Dollár, and R. Girshick, "Mask R-CNN," in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2961–2969. Available from: https://doi.org/10.1109/ICCV.2017.322
Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection," arXiv preprint arXiv:2004.10934, 2020. Available from: https://arxiv.org/abs/2004.10934
Jocher et al., "YOLOv5," Ultralytics, 2021. Available from: https://github.com/ultralytics/yolov5
N. D. Nath, A. H. Behzadan, and S. G. Paal, "Deep Learning for Site Safety: Real-Time Detection of Personal Protective Equipment," Automation in Construction, vol. 112, Art. no. 103085, 2020. Available from: https://doi.org/10.1016/j.autcon.2020.103085
Z. Wang, Y. Wu, L. Yang, A. Thirunavukarasu, C. Evison, and Y. Zhao, "Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches," Sensors, vol. 21, no. 10, Art. 3478, 2021. Available from: https://doi.org/10.3390/s21103478
M. Ferdous and S. M. M. Ahsan, "PPE Detector: A YOLO-Based Architecture to Detect Personal Protective Equipment for Construction Sites," PeerJ Computer Science, vol. 8, Art. e999, 2022. Available from: https://doi.org/10.7717/peerj-cs.999
R. Qiao, C. Cai, H. Meng, K. Wu, F. Wang, and J. Zhao, "An Improved Personal Protective Equipment Detection Method Based on YOLOv4," Multimedia Tools and Applications, vol. 83, 2024. Available from: https://doi.org/10.1007/s11042-024-18772-1
Kumar, A. Kalia, and A. Kalia, "ETL-YOLO v4: A Face Mask Detection Algorithm in Era of COVID-19 Pandemic," Optik, vol. 259, Art. 169051, 2022. Available from: https://doi.org/10.1016/j.ijleo.2022.169051
Majumder and S. Chatterjee, "YoloGA: An Evolutionary Computation Based YOLO Algorithm to Detect Personal Protective Equipment," Simulation, vol. 101, no. 5, 2025. Available from: https://doi.org/10.1177/18758967251338695
L. Da Xu, E. L. Xu, and L. Li, "Industry 4.0: State of the Art and Future Trends," International Journal of Production Research, vol. 56, no. 8, pp. 2941–2962, 2018. Available from: https://doi.org/10.1080/00207543.2018.1444806
Y. Lu, "Industry 4.0: A Survey on Technologies, Applications and Open Research Issues," Journal of Industrial Information Integration, vol. 6, pp. 1–10, 2017. Available from: https://doi.org/10.1016/j.jii.2017.04.005
W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge Computing: Vision and Challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016. Available from: https://doi.org/10.1109/JIOT.2016.2579198
S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, 2021. Available from: https://aima.cs.berkeley.edu/
L. Floridi and J. Cowls, "A Unified Framework of Five Principles for AI in Society," Harvard Data Science Review, vol. 1, no. 1, 2019. Available from: https://doi.org/10.1162/99608f92.8cd550d1
European Commission, "Ethics Guidelines for Trustworthy AI," 2019. Available from: https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
National Institute of Standards and Technology (NIST), "AI Risk Management Framework (AI RMF 1.0)," 2023. Available from: https://www.nist.gov/itl/ai-risk-management-framework
National Institute of Standards and Technology (NIST), "Cybersecurity Framework (CSF) 2.0," 2024. Available from: https://www.nist.gov/cyberframework
Suchetha N. V., Abhijeet P. M., Likesh K., Vinuth A. N., and Suhas H., "Multilingual Semantic Object Identification Using Computer Vision and NLP," International Journal of Innovative Research in Computer Science and Technology (IJIRCST), vol. 14, no. 1, pp. 1–8, Jan. 2026. Available from: https://doi.org/10.55524/ijircst.2026.14.1.1
ISO/IEC 27001:2022, Information Security, Cybersecurity and Privacy Protection—Information Security Management Systems—Requirements. International Organization for Standardization, 2022. Available from: https://www.iso.org/standard/27001.html

Cites this article as

A. Mahajan, K. Dharwadkar, "AI-Powered Computer Vision Systems for Automated Safety Compliance Monitoring and PPE Detection in Automobile Manufacturing Plants", International Journal of Innovative Research in Engineering and Management (IJIREM), Vol-13, Issue-4, Page No-11-16, 2026. Available from: https://doi.org/10.55524/ijirem.2026.13.4.2

Corresponding Author

Ankush Mahajan

Research Scholar, Department of Management, S. B. Patil Institute of Management, Pune, Maharashtra, India

Download Full Paper

Download PDF

No. of Downloads: 1 | No. of Views: 6

-->

AI-Powered Computer Vision Systems for Automated Safety Compliance Monitoring and PPE Detection in Automobile Manufacturing Plants

Citations

Total View 6

Total Download 1