Safety Helmet Detection is an important computer vision application developed to improve workplace safety, especially in construction sites, factories, and other hazardous working environments. The main problem is that some workers may fail to wear safety helmets, which can increase the risk of head injuries during accidents. Manually monitoring every worker is difficult, time-consuming, and may not provide continuous safety supervision. To solve this problem, I have developed a Safety Helmet Detection system using YOLOv8 (You Only Look Once) and computer vision techniques. The system is trained using a dataset containing images of workers with helmets, workers without helmets, and workers.When an image or video is provided, the model detects the objects and identifies whether the worker is wearing a safety helmet. This helps automate the monitoring process and provides faster and more consistent detection than manual observation. The system is useful for construction workers, safety officers, supervisors, industries, and organizations that need to maintain workplace safety. It can help companies monitor safety practices, reduce the possibility of accidents, and encourage workers to follow safety rules. Construction companies, factories, industrial sites, infrastructure projects, and safety management teams can use this technology. It can also be integrated with CCTV cameras to support continuous workplace monitoring. Overall, the Safety Helmet Detection system provides an efficient, automated, and practical approach to improving worker safety and supporting safer working environments.

Sri Dharshini S, Nathiyini T M, Nishwanth S, Dr. Pradeepa K,"YOLOv8-Based Safety Helmet Detection for Construction Site Monitoring" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(4): Page(418-424) July - August 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.