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@article{174213,
        author = {Mallela Pavan Kumar and G. Bhargavi and Pothula Vaishnavi and Vasamsetti Praveen karthikeya and Venkata Krishna Thota},
        title = {Real-Time Monitoring of Personal Protective Equipment Compliance Using Surveillance Cameras},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3205-3210},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174213},
        abstract = {In high-risk industrial environments, ensuring the consistent use of Personal Protective Equipment (PPE) is crucial for worker safety and regulatory compliance. However, traditional manual monitoring methods often fall short due to their labor-intensive nature and susceptibility to human error. In this paper, we present a real-time PPE compliance monitoring system that leverages surveillance camera feeds, deep learning, and outlier filtering to detect safety violations effectively. Using the YOLOv8 object detection model alongside an outlier removal mechanism, our system identifies non-compliance instances, such as missing helmets or vests, and triggers alerts for immediate action. The proposed approach offers high detection accuracy, real-time performance, and scalability, making it a practical solution for modern safety management. Evaluation results show a precision of 94.2% and a recall of 92.8%, demonstrating the system's effectiveness in diverse industrial scenarios.},
        keywords = {Computer Vision, Deep Learning, Outlier Removal, PPE Compliance, Real-Time Monitoring, Safety Automation, Surveillance Cameras, YOLOv8.},
        month = {March},
        }