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Stream/Area:
-
Subject:
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Paper ID:
IJSREM62576
Volume:
10 |
5 |
May 2026
Pages:
1-11
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Robotics and Automation
Zeal College of Engineering and Research, Pune, India
Department of Robotics and Automation
Zeal College of Engineering and Research, Pune, India
Department of Robotics and Automation
Zeal College of Engineering and Research, Pune, India
Department of Robotics and Automation
Zeal College of Engineering and Research, Pune, India
Department of Robotics and Automation
Zeal College of Engineering and Research, Pune, India
Department of Robotics and Automation
Zeal College of Engineering and Research, Pune, India
In the contemporary industrial landscape, ensuring Personal Protective Equipment (PPE) compliance is a paramount challenge for occupational health and safety. Traditional manual surveillance is inherently flawed due to human fatigue and the logistical impossibility of constant monitoring. This paper proposes an "Intelligent PPE Compliance and Automated Attendance System" that leverages a synergy of Deep Learning (DL), Computer Vision (CV), and the Internet of Things (IoT). By integrating a YOLOv8-based object detection framework with a dlib-driven facial recognition module, the system automates both identity verification and safety auditing. Our architecture utilizes a high- definition optical sensor (Logitech C922) interfaced with a FastAPI-driven microservice and an Arduino-based electromechanical access control unit. Experimental results indicate a Mean Average Precision (mAP) of 91% for PPE detection and a 94% accuracy rate for facial identification, maintaining a low-latency throughput of 150ms per frame. This research underscores a robust, scalable paradigm for "Industry 4.0" safety automation.
KEYWORDS
The Industrial Safety Gear Detection and Attendance Monitoring System provides a practical solution for automating safety compliance and access control in industrial environments. By combining facial recognition with PPE detection, the system ensures that only authorized and properly equipped individuals are allowed entry. This reduces manual supervision, minimizes human error, and strengthens overall workplace safety. The integration of AI-based processing with hardware control enables real-time decision-making and efficient system operation. The modular architecture, consisting of perception, logic, and control layers, allows flexibility for future improvements and scalability. Additionally, the inclusion of logging and cloud storage ensures proper record-keeping, making the system reliable for auditing and monitoring purposes. Overall, the system demonstrates how intelligent automation can enhance both security and safety standards in industries. With its cost-effective design and capability to operate in real-time conditions, it serves as a strong foundation for developing more advanced smart safety systems in the future.
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Publication Date
5/6/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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