Open Access

ARTICLE

Towards Efficient Vehicle Recognition: A Unified System for VMMR, ANPR, and Color Classification

Saad Sadiq¹, Kashif Sultan¹, Muhammad Sheraz², Teong Chee Chuah^2,*, Muhammad Usman Hashmi³

1 Department of Software Engineering, Bahria University H-11 Campus, Islamabad, 44000, Pakistan
2 Centre for Smart Systems and Automation, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Persiaran Multimedia, Cyberjaya, 63100, Selangor, Malaysia
3 Department of Computer Science, Bahria University E-8 Campus, Islamabad, 44220, Pakistan

* Corresponding Author: Teong Chee Chuah. Email:

(This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)

Computers, Materials & Continua 2025, 85(2), 3945-3963. https://doi.org/10.32604/cmc.2025.067538

Received 06 May 2025; Accepted 03 July 2025; Issue published 23 September 2025

Abstract

Vehicle recognition plays a vital role in intelligent transportation systems, law enforcement, access control, and security operations—domains that are becoming increasingly dynamic and complex. Despite advancements, most existing solutions remain siloed, addressing individual tasks such as vehicle make and model recognition (VMMR), automatic number plate recognition (ANPR), and color classification separately. This fragmented approach limits real-world efficiency, leading to slower processing, reduced accuracy, and increased operational costs, particularly in traffic monitoring and surveillance scenarios. To address these limitations, we present a unified framework that consolidates all three recognition tasks into a single, lightweight system. The framework utilizes MobileNetV2 for efficient VMMR, YOLO (You Only Look Once) for accurate license plate detection, and histogram-based clustering in the HSV color space for precise color identification. Rather than optimizing each module in isolation, our approach emphasizes tight integration, enabling improved performance and reliability. The system also features adaptive image calibration and robust algorithmic enhancements to ensure consistent results under varying environmental conditions. Experimental evaluations demonstrate that the proposed model achieves a combined accuracy of 93.3%, outperforming traditional methods and offering practical scalability for deployment in real-world transportation infrastructures.

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Keywords

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