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@article{175293,
        author = {Karsten Solomon Raj S and Jeffin Amos K and Ahamed Nowfal S and Alamelu Mangai M},
        title = {AI-Powered Smart Surveillance for Two-Wheeler Safety Compliance Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2181-2187},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175293},
        abstract = {Ensuring helmet compliance among motorcyclists is a critical road safety measure. This paper presents an AI-powered smart surveillance system that automates helmet violation detection and license plate recognition using deep learning techniques. The system employs YOLOv8 for real-time helmet detection and Paddle OCR for optical character recognition of license plates. Video feeds from traffic surveillance cameras are processed to identify riders without helmets, and violation details are logged in a database. Automated email alerts are sent to traffic authorities using an SMTP-based notification system. Experimental results demonstrate high accuracy in detecting helmet violations and recognizing license plates under varying environmental conditions. The proposed system enhances traffic law enforcement by reducing manual intervention and enabling large-scale deployment. Future work includes improving accuracy in low-light conditions, integrating edge computing for real-time processing, and expanding the system to detect additional traffic violations.},
        keywords = {Helmet Detection, YOLOv8, License Plate Recognition, Paddle OCR, Traffic Surveillance},
        month = {April},
        }