Enhancing Construction Site Safety: Personal Protective Equipment Detection Using Yolov11 and OpenCV

Ruchitraa Rajagopal, Harshitha Pulluru, Aaradhya Joshi, Shanmuganathan C.

2025

Abstract

One of the most emerging industries in the world is construction. Even after installing a lot of safety rules and regulations, construction site accidents are a major threat. This is because of non-compliance with the assigned safety protocols and the lack of a system assigned for supervision. The paper provides a model that can automate the safety system using Computer Vision (CV) and Machine Learning (ML) techniques. The model analyzes the surveillance footage to detect workers and their personal protective equipment (PPE), such as safety vests, masks, and hard hats. It also calculates the distance between workers and machinery and sends alerts in case of unsafe distances. The system also provides a graphical user interface where the site managers or supervisors can easily monitor the surveillance footage with the model results in real time. The warnings are stored in a database along with a video clip and other essential information. This way, the supervisors can view the database and get an idea about the type of warnings and when and where they occurred. This way, they can guide the workers regarding site safety. The model used by the system is YOLOv11. It is trained and tested to give better results in various weather conditions. The model achieves a mAP of 81% at 0.5 IoU and a mAP of 60.3% at 0.5 to 0.95 IoU, with an overall accuracy, precision, and recall of 97%, 87%, and 76%, respectively. It is computationally efficient, with 14.7GB FLOPs, 6.4MB parameters, and an inference speed of 2.4ms, making the model applicable for real-time analysis.

Paper Citation

in Harvard Style

Rajagopal R., Pulluru H., Joshi A. and C. S. (2025). Enhancing Construction Site Safety: Personal Protective Equipment Detection Using Yolov11 and OpenCV. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 344-353. DOI: 10.5220/0013882800004919

in Bibtex Style

@conference{icrdicct`2525,
author={Ruchitraa Rajagopal and Harshitha Pulluru and Aaradhya Joshi and Shanmuganathan C.},
title={Enhancing Construction Site Safety: Personal Protective Equipment Detection Using Yolov11 and OpenCV},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={344-353},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013882800004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Enhancing Construction Site Safety: Personal Protective Equipment Detection Using Yolov11 and OpenCV
SN - 978-989-758-777-1
AU - Rajagopal R.
AU - Pulluru H.
AU - Joshi A.
AU - C. S.
PY - 2025
SP - 344
EP - 353
DO - 10.5220/0013882800004919
PB - SciTePress