Safety Helmet Detection Using YOLO V8
Krunal H. Patel, Vrajesh Patel, Vikrant Prajapati, Darshak Chauhan, Adil Haji, Sheshang Degadwala
Abstract
Ensuring safety in the workplace is crucial to the wellbeing of workers and the success of organizations. One essential aspect of workplace safety is the use of safety helmets in hazardous environments. Safety helmets protect workers from head injuries caused by falling objects, electric shocks, and other hazards. In recent years, computer vision-based safety helmet detection systems have gained popularity as a means of ensuring compliance with safety regulations and reducing accidents. This study proposes a safety helmet detection system based on the You Only Look Once (YOLO) V8 algorithm, which is a state-of-the-art object detection algorithm that has shown superior performance in detecting small objects in real-time. The proposed system involves training the YOLO V8 algorithm on a dataset of images containing workers with and without safety helmets. The dataset was carefully curated to include various lighting conditions, camera angles, and helmet types. The trained model was then evaluated on a separate test set to measure its performance. Experimental results demonstrate that the proposed approach achieves high accuracy in detecting safety helmets, with an average precision of 0.99 and a recall of 0.99. The model also demonstrated robustness to variations in lighting and camera angles, making it suitable for real-world deployment.