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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62339
Volume:
10 |
5 |
May 2026
Pages:
1-12
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.
Electronics and Communication Engineering
SSM Institute of engineering and technology
Electronics and Communication Engineering
SSM Institute of engineering and technology
Electronics and Communication Engineering
SSM Institute of engineering and technology
Electronics and Communication Engineering
SSM Institute of engineering and technology
Industrial environments are inherently prone to accidents due to human error, equipment malfunction, or unsafe operational practices. This project proposes an intelligent industrial safety system that utilises an ESP32 microcontroller integrated with a high-resolution camera module and advanced AI-based image processing algorithms to detect potential accidents in real time with minimal latency. The system continuously monitors the workspace using a distributed sensor network, captures live video from strategic viewpoints, and processes frames using machine learning algorithms trained to identify hazardous situations including human falls, equipment collisions, unauthorized zone entry, and other safety violations. Upon detection, multi-channel alerts are triggered immediately, enabling prompt emergency response. The proposed system significantly enhances workplace safety standards, reduces emergency response time, and minimizes the risk of severe injuries. Comprehensive evaluation demonstrates 88–91% detection accuracy, 99.8% system availability, and a response latency of 0.5–2 seconds — compared to 30–60 seconds for manual detection — while achieving approximately 95% cost reduction over conventional SCADA/PLC-based systems.
KEYWORDS
This project has successfully designed, developed, and validated an innovative IoT-Based Industrial Monitoring and Control System that represents a significant advancement over traditional industrial automation approaches. The system seamlessly integrates distributed IoT microcontrollers, multi-sensor environmental monitoring, camera-based monitoring, and artificial intelligence to create a comprehensive, affordable, and scalable solution for industrial safety and operational monitoring. Comprehensive testing demonstrated that the system detects accidents in 0.5–2 seconds (compared to 30–60 seconds for manual detection), achieves 88–91% accuracy in accident detection, and maintains 99.8% system availability. The system achieves approximately 95% cost reduction compared to traditional industrial automation systems while providing superior functionality — making advanced industrial safety accessible to small and medium-sized enterprises previously unable to afford such technology.
[1] Rajesh, S. and Anand, K. (2024). "Real-Time Accident Detection in Industrial Environments." IEEE Transactions on Industrial Electronics, 71(2), pp.1847–1858. [2] TensorFlow Lite (2024). "Deploying Machine Learning on Microcontrollers." Google TensorFlow Lite Team. [3] Espressif Systems (2024). "ESP32 Technical Reference Manual." Espressif Systems Inc. [4] OSHA (2024). "Industrial Safety Standards and Guidelines." U.S. Department of Labor. [5] OpenCV Contributors (2024). "Computer Vision Library Documentation." OpenCV Foundation. [6] Sharma, A. and Verma, P. (2023). "Machine Learning for Industrial Safety Systems." Computers & Industrial Engineering, 165, pp.107–125. [7] Natarajan, P. and Subramani, V. (2023). "Integrated Monitoring Systems for Smart Environments." Smart Systems Review, 25(4), pp.521–538. [8] Kumar, R. and Mehta, S. (2022). "IoT-Based Monitoring Systems in Manufacturing." Journal of Manufacturing Technology Management, 33(2), pp.245–265. [9] Gill, A.Q., Van, H. and Henderson-Sellers, P. (2022). "Industry 4.0: Intelligent and Adaptive Industrial Automation using IoT and AI." IEEE Transactions on Industrial Informatics, 18(5), pp.3224–3236. [10] Wan, S., Li, X. and Liu, F. (2022). "Recent Advances in Industrial Internet: Applications and Security." IEEE Internet of Things Journal, 9(3), pp.1542–1558. [11] Liu, Y., Zhang, X. and Kumar, R. (2022). "Edge Computing with IoT Sensors for Real-Time Industrial Monitoring." International Journal of Industrial Engineering, 29(2), pp.145–156. [12] Lee, M. and Park, J. (2021). "Smart Factory Implementation and Benefits." Manufacturing Engineering Journal, 48(3), pp.312–329. [13] Boyes, H., Hallaq, B., Cunningham, J. and Watson, T. (2018). "The Industrial Internet of Things (IIoT): An Analysis Framework." Computers in Industry, 101, pp.1–12.
Publication Date
5/3/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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