Traffic Violation Detection Using Deep Learning

Abstract

Traffic violation detection is a crucial aspect of intelligent transportation systems, enabling automated identification of vehicles for security, law enforcement, and toll collection. This process involves image acquisition, pre-processing, segmentation, feature extraction, and character recognition. Various techniques, including edge detection, morphological operations, and deep learning-based object detection models, enhance accuracy and robustness. Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO) models have significantly improved real-time detection performance. Challenges such as varying lighting conditions, occlusions, and diverse plate formats necessitate adaptive algorithms. Optical Character Recognition (OCR) is employed to extract alphanumeric details. Machine learning and deep learning techniques refine detection precision. Integration with cloud computing and IoT enhances scalability and deployment. Future advancements focus on improving accuracy, speed, and adaptability to complex environments.