Open Access

ARTICLE

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

by Kisaezehra¹, Muhammad Umer Farooq^1,*, Muhammad Aslam Bhutto², Abdul Karim Kazi¹

1 Department of Computer Science and Information Technology, NED University of Engineering & Technology, Karachi, 75270, Pakistan
2 Department of Civil Engineering, NED University of Engineering & Technology, Karachi, 75270, Pakistan

* Corresponding Author: Muhammad Umer Farooq. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 911-927. https://doi.org/10.32604/iasc.2023.031359

Received 15 April 2022; Accepted 30 June 2022; Issue published 29 September 2022

Abstract

The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety (OHS) is of prime importance. Like in other developing countries, this industry pays very little, rather negligible attention to OHS practices in Pakistan, resulting in the occurrence of a wide variety of accidents, mishaps, and near-misses every year. One of the major causes of such mishaps is the non-wearing of safety helmets (hard hats) at construction sites where falling objects from a height are unavoidable. In most cases, this leads to serious brain injuries in people present at the site in general and the workers in particular. It is one of the leading causes of human fatalities at construction sites. In the United States, the Occupational Safety and Health Administration (OSHA) requires construction companies through safety laws to ensure the use of well-defined personal protective equipment (PPE). It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible. However, such monitoring through technological aids or automated tools is very much possible. The present study describes a systematic strategy based on deep learning (DL) models built on the You-Only-Look-Once (YOLOV5) architecture that could be used for monitoring workers’ hard hats in real-time. It can indicate whether a worker is wearing a hat or not. The proposed system uses five different models of the YOLOV5, namely YOLOV5n, YOLOv5s, YOLOv5 m, YOLOv5l, and YOLOv5x for object detection with the support of PyTorch, involving 7063 images. The results of the study show that among the DL models, the YOLOV5x has a high performance of 95.8% in terms of the mAP, while the YOLOV5n has the fastest detection speed of 70.4 frames per second (FPS). The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

---

Keywords

---