Year: 2025
Type: article
Abstract: The growing number of vehicles on urban roads has turned traffic management and road safety into a pressing challenge. Manual methods of spotting violations are still common, but they tend to be slow, inconsistent, and unable to keep pace with heavy traffic conditions. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), and computer vision provide an opportunity to design smarter, automated solutions for this problem. This work introduces a system that uses the YOLO object detection model to identify traffic rule violations such as speeding, running red lights, and triple riding. To track offenders, Easy OCR is applied for reading vehicle license plates directly from video frames. By bringing together object detection and OCR, the system cuts down on human involvement, lowers the chance of mistakes, and allows for quicker enforcement actions. The approach is intended not only to support authorities in monitoring but also to encourage disciplined driving, making roads safer and traffic management more reliable.
Author: Bhavana B M
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7091693190
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