International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 3
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Automated PPE Compliance Monitoring at Construction Sites Using Deep Learning

Author(s) Mr. Ketan Kanjiya, Mr. Piyush Sonani, Mr. Upendrasinh Zala
Country India
Abstract Construction sites are among the most hazardous working environments, where failure to use Personal Protective Equipment (PPE) can lead to serious injuries and safety violations. Manual monitoring of PPE compliance is often labor intensive, subjective, and difficult to maintain across large and dynamic construction environments. Recent advances in deep learning and computer vision provide effective solutions for automatically detecting safety equipment from visual data. In this study, a deep learning based object detection approach using the YOLO26 architecture is investigated for detecting PPE and related safety violations in construction environments.
The model is trained and evaluated on the Construction Site Safety dataset, which contains annotated images representing ten classes such as Hardhat, Mask, Safety Vest, Person, and non-compliance categories including NO-Hardhat and NO-Mask. Targeted data augmentation and mixed-precision training are employed to improve model robustness and training efficiency. Experimental results demonstrate strong detection performance, achieving a mean Average Precision (mAP@50) of 85.39%, precision of 86.69%, and recall of 78.68%. The results indicate that the proposed approach provides an efficient and scalable solution for automated PPE compliance monitoring in construction environments.
Keywords PPE Compliance Monitoring, Construction Site Safety, Deep Learning, Object Detection, Computer Vision, Real-Time Safety Compliance
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-13
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E-ISSN 2582-2160
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