Towards Safer Roads: An End-to-End Deep Learning Framework for Traffic Violation Detection and License Plate Recognition

Abstract

Automated traffic violation detection is a critical component of intelligent transportation systems, aimed at enhancing road safety and minimizing human oversight. This paper proposes a real-time deep learning-based framework capable of detecting multiple traffic violations, including helmet and seatbelt non-usage, triple riding, and mobile phone usewhile performing automatic license plate recognition. The system integrates five independently trained YOLOv8 models on specialized datasets and uses Keras-OCR(Optical Character Recognition) for license number extraction, triggered conditionally upon violation detection to optimize computational efficiency. The proposed system achieved an average mAP@50 of 97.9%, with a precision of 96.9% and recall of 95.9%, outperforming existing models in both speed and accuracy. The challenges faced while developing the system were low-light conditions, motion blur, and license plate variability which are discussed in this paper alongside legal and ethical considerations.Future work will focus on enhancing OCR robustness, supporting complex violations through temporal analysis, and deploying on edge-computing platforms within smart city infrastructures.