Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety

Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety

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Worker safety at construction sites is a growing concern for many construction industries.Wearing safety helmets can reduce injuries to workers at construction sites, but due Fitness to various reasons, safety helmets are not always worn properly.Hence, a computer vision-based automatic safety helmet detection system is extremely important.

Many researchers have developed machine and deep learning-based helmet detection systems, but few have focused on helmet detection at construction sites.This paper presents a You Only Look Once (YOLO)-based real-time computer vision-based automatic safety helmet detection system at a construction site.YOLO architecture is high-speed and can process 45 frames per second, making YOLO-based architectures feasible to use in real-time safety helmet detection.

A benchmark dataset containing 5000 images of hard hats was used Balisong in this study, which was further divided in a ratio of 60:20:20 (%) for training, testing, and validation, respectively.The experimental results showed that the YOLOv5x architecture achieved the best mean average precision (mAP) of 92.44%, thereby showing excellent results in detecting safety helmets even in low-light conditions.