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@article{174449,
        author = {Kolli Lavanya Sri Venkata Sarika and Mullamuri Harsha Vardhan and Baig Riyaz and Vanumu kanaka pradeep kumar. and Kattamudi Tejaswini},
        title = {YOLO BASED LICENSE PLATE DETECTION OF TRIPLE RIDERS AND NON-HELMETERS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3966-3972},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174449},
        abstract = {Triple riding on two-wheelers has become a pervasive traffic violation in urban areas, where motorcycles and scooters serve as essential modes of transportation. This practice not only violates traffic regulations but also significantly heightens the risk of accidents and injuries, jeopardizing the safety of both the riders and other road users. Compounding this issue is the widespread non-compliance with mandatory helmet laws, which are critical for ensuring rider safety. Despite the severity of these violations, manual monitoring systems are ineffective, time-consuming, and prone to human error, particularly in high-traffic or complex urban environments. This paper proposes an innovative, automated system for detecting triple riders and verifying helmet usage compliance using YOLOv8, a state-of-the-art deep learning-based object detection model. The system aims to provide real-time, accurate, and efficient detection of two-wheelers, riders, helmets, and license plates, significantly improving traffic law enforcement and road safety. The YOLOv8 model is trained to identify motorcycles and their riders in a variety of traffic scenarios, including different lighting conditions (day, night), weather conditions (sunny, rainy, foggy), and traffic densities (crowded, sparse). The system also incorporates a rider counting algorithm to detect triple riding violations, automatically flagging motorcycles with three or more riders. In addition to triple riding, the system also checks for helmet compliance by using advanced image processing techniques. If a rider is found without a helmet, the violation is flagged. Furthermore, the system’s ability to detect license plates allows for the automatic generation of violation notices, which are sent directly to the vehicle owner via email. These notifications include detailed information about the offense, such as the location, time, and associated penalties, facilitating efficient enforcement without the need for human intervention. The integration of cloud computing allows for real-time analysis and reporting, ensuring that traffic authorities can monitor and respond to violations as they occur. By automating the detection of triple riding and helmet non-compliance, this system offers a scalable solution to improve traffic safety and reduce the risks associated with two-wheeler violations. Moreover, it enhances the overall efficiency of traffic.},
        keywords = {},
        month = {March},
        }