Real-Time IoT-Enabled Detection of Safety Gear Non-Compliance in Power Stations Using YOLOv8 and OpenCV

Abhiruchi Jagadale; Akanksha Bankar; Pallavi Nath; Pooja Kundaragi; Vidyashree Kokane

doi:10.2991/978-94-6463-718-2_81

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Real-Time IoT-Enabled Detection of Safety Gear Non-Compliance in Power Stations Using YOLOv8 and OpenCV

Authors

Abhiruchi Jagadale¹^{, *}, Akanksha Bankar¹, Pallavi Nath¹, Pooja Kundaragi¹, Vidyashree Kokane¹

¹Department of Computer Science and Business Systems, JSPM’s Rajarshi Shahu College of Engineering, Pune, Maharashtra, India

^*Corresponding author. Email: abhiruchi.j1903@gmail.com

Corresponding Author

Abhiruchi Jagadale

Available Online 23 May 2025.

DOI: 10.2991/978-94-6463-718-2_81 How to use a DOI?
Keywords: Object detection; YOLOv8; Deep Learning; OpenCV; CNN; Power Station; Personal Protective Equipment; Arc suit; Internet of Things (IoT); Arduino; Real time detection
Abstract: In industrial environments such as power stations, compliance with safety protocols is imperative for worker safety. The proposed system is an innovative solution for the real-time identification of Personal Protective Equipment (PPE) compliance, like arc suits, using Arduino-based Internet of Things (IoT) technologies coupled with computer vision. We have proposed a YOLO frame integrated with Open CV and Arduino micro controllers for worker identification without arc suit. Performance profiling shows its superiority over existing systems. We present implications for enhanced safety preventive measures and ways hazards can be reduced in industrial working location, and future research directions.
Copyright: © 2025 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series: Advances in Computer Science Research
Publication Date: 23 May 2025
ISBN: 978-94-6463-718-2
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-718-2_81 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Abhiruchi Jagadale
AU  - Akanksha Bankar
AU  - Pallavi Nath
AU  - Pooja Kundaragi
AU  - Vidyashree Kokane
PY  - 2025
DA  - 2025/05/23
TI  - Real-Time IoT-Enabled Detection of Safety Gear Non-Compliance in Power Stations Using YOLOv8 and OpenCV
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 954
EP  - 968
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_81
DO  - 10.2991/978-94-6463-718-2_81
ID  - Jagadale2025
ER  -

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