• Tamil Selvi
• Nanda M B
• Likhitha M
• Yashaswini GS
• Hrishab N

: The integration of Artificial Intelligence (AI) and Machine Learning (ML) in real-time traffic violation detection has revolutionized road safety, providing automated solutions for law enforcement. This study develops a system to detect motorbike riders not wearing helmets and identify their license plates using advanced ML techniques, including convolutional neural networks (CNNs) for object detection and optical character recognition (OCR) for plate identification, leveraging TensorFlow, OpenCV, and YOLOv3 for high accuracy and speed. Upon detecting a violation, the system captures the license plate and sends an automated notification via a Telegram bot to relevant authorities, including details like the license plate number and timestamp. This instant communication ensures quicker responses, enhances enforcement efficiency, reduces manual effort and human errors, and promotes road safety through consistent monitoring and effective helmet-wearing regulation enforcement.

Deep Learning, Convolutional Neural Network (CNN), Deep neural networks (DNNs), Optical Character Recognition (OCR), Object Detection, TensorFlow, OpenCV, YOLOv3 .

"IMPACT OF AI&ML IN REAL-TIME TRAFFIC VIOLATION DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.b54-b62, February-2025, Available :http://www.jetir.org/papers/JETIR2502108.pdf

"IMPACT OF AI&ML IN REAL-TIME TRAFFIC VIOLATION DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppb54-b62, February-2025, Available at : http://www.jetir.org/papers/JETIR2502108.pdf