International Journal of Advanced Research in Computer and Communication Engineering

ABSTRACT : Road Safety and public health are two critical domains where simple preventive measures such as wearing helmets and face mask while riding plays a major role in reducing accidents and disease transmission. manual monitoring of such compliance is difficult to scale, especially in densely populated areas, making automated solutions is necessary. Recent advances in computer vision and deep learning have enabled intelligent surveillance systems capable of detecting helmet and mask violations in real time. Techniques such as YOLO (You Only Look Once), Convolutional Neural Networks (CNNs), and Optical Character Recognition (OCR) have shown strong performance for multi class detection tasks, including helmets, masks, and license plates. This review Systematically explores research contributions in three categories: helmet detection, mask detection, and combined helmet + mask detection. By comparing traditional machine learning approaches with state-of-the-art deep learning frameworks, the paper highlights the growing potential of unified AI-powered systems for improving public safety and traffic enforcement.

Keywords: Road Safety, Public Safety, Intelligent Surveillance, Helmet Detection, Face Mask Detection, YOLO, OCR.

Downloads: | DOI: 10.17148/IJARCCE.2025.14938