An Overview of YOLO-Based Helmet Detection: Modifications and Advancements
Seba Al Mokdad, Manar Wasif Abu Talib, Simon Zerisenay Ghebremeskel, Fouad Lamghari, Shaher Bano Mirza, Ali Bou Nassif · 2024
Small objects detection is a major challenge in computer vision and one well-known example of this is helmet detection. Helmets have a small size, which limits typical YOLO (You Only Look Once) object detection models from efficiently detecting them. Recent studies have focused on modifying several YOLO architectures to propose a solution to this problem. This study provides a comprehensive evaluation of novel solutions that address the helmet detection problem which include modifying multiple YOLO versions. A comprehensive examination of 53 research publications, which were published the period between 2019 to 2023, out of which 30 were included investigates the contributions in terms of model modifications, datasets used for evaluation, loss function optimization, and resulting performance measures. Several YOLO versions are addressed including YOLOv3, YOLOv4, YOLOv5, YOLOv7, and YOLOX. The studies discuss various modifications to YOLO including architecture modifications, integration of attention mechanisms, addition of detection scales, optimization and regularization techniques, loss function Improvements and anchor box optimization. The findings provide insight into the evolution of helmet detection algorithms within the YOLO architecture and how these modifications helped in addressing the problem of small objects detection. This review aims to provide academics and practitioners with significant insights into cutting-edge techniques for overcoming the challenges of identifying small objects as helmets.