Real Time Car Model and Plate Detection System by Using Deep Learning Architectures

Abstract
The advent of deep learning has revolutionized computer vision, enabling real-time analysis crucial for traffic management and vehicle identification. This research introduces a system combining vehicle make and model detection with Automatic Number Plate Recognition (ANPR), achieving a groundbreaking 97.5% accuracy rate. Unlike traditional methods, which focus on either make and model detection or ANPR independently, this study integrates both aspects into a single, cohesive system, providing a more holistic and efficient solution for vehicle identification, ensuring robust performance even in adverse weather conditions. The paper explores the use of deep learning techniques, including OpenCV, in combination with Python programming language. Leveraging MobileNet-V2 and YOLOx (You Only Look Once) for vehicle identification, and YOLOv4-tiny, Paddle OCR (optical character recognition), and SVTR-tiny for ANPR, the system was rigorously tested at Firat University’s entrance with a thousand images captured under various conditions such as fog, rain, and low light. The system’s exceptional success rate in these tests highlights its robustness and practical applicability. Additionally, experiments evaluate the system’s accuracy and effectiveness, using Gradient-weighted Class Activation Mapping (GradCam) technology to gain insights into neural networks’ decision-making processes and identify areas for improvement, particularly in misclassifications. The implications of this research for computer vision are significant, paving the way for advanced applications in autonomous driving, traffic management, stolen vehicles, and security surveillance. Achieving real-time, high-accuracy vehicle identification, the integrated Vehicle Make and Model Recognition (VMM R) and ANPR system sets a new standard for future research in the field.

Author
Twana saeed Mustafa; Murat Karabatak

DOI
https://ieeexplore.ieee.org/document/10601685

Publisher
IEEE Access

ISSN
2169-3536

Publish Date: