International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Traffic rule enforcement in urban environments remains largely dependent on manual monitoring practices, leading to limited scalability, inconsistent evaluation, and delayed action against safety violations. One major challenge faced by transportation authorities is identifying two-wheeler riders who fail to wear helmets and subsequently generating actionable reports for follow-up. Existing surveillance systems provide limited automation and require human intervention to review video feeds, detect violations, and record offender details. This paper presents an intelligent Helmet Detection and Reporting System designed to automate the identification of helmet misuse and streamline violation tracking through computer vision techniques. The proposed framework utilizes deep learning–based object detection to locate motorcycles and analyze rider head regions from video footage, thereby determining helmet compliance in real time. In cases of detected violations, the system further extracts vehicle number plates, applies optical character recognition to identify registration numbers, and compiles structured violation evidence suitable for reporting. Unlike conventional manual workflows, the system provides continuous, data-driven, and unbiased assessment of helmet usage under varying traffic densities and lighting conditions. Experimental evaluation conducted on realistic traffic datasets demonstrates high detection accuracy, reduced dependency on manual supervision, and significant improvement in reporting efficiency. The proposed system highlights the potential of automated computer vision pipelines to enhance safety, support enforcement agencies, and promote rule compliance in intelligent transportation environments.

Keywords: Helmet Detection, Traffic Surveillance, Number Plate Recognition, Computer Vision, Deep Learning, OCR, Intelligent Transportation Systems

Downloads: | DOI: 10.17148/IJARCCE.2026.151117