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ORIGINAL RESEARCH article

Front. Comput. Sci.

Sec. Computer Vision

Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1561899

A Benchmark Dataset and Methodology for Fine Grained Vehicle Make and Model Classification

Provisionally accepted
  • 1 University of Engineering and Technology, Taxila, Taxila, Pakistan
  • 2 Lahore Leads University, Lahore, Punjab, Pakistan
  • 3 Synapsify, Islamabad, Pakistan, Islamabad, Pakistan
  • 4 School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, KwaZulu-Natal, South Africa

The final, formatted version of the article will be published soon.

    Urban transportation management increasingly relies on Intelligent Transportation Systems (ITS), where Vehicle Make and Model Recognition (VMMR) plays a vital role in surveillance, traffic monitoring, and infrastructure planning. However, traffic conditions in developing nations such as Pakistan present unique challenges due to unstructured driving practices and lack of lane discipline. We introduce a large VMMR dataset for Pakistan's traffic dynamics to address these challenges. This dataset comprises 129,000 images across 94 vehicle classes. We collected the dataset through web scraping and overhead traffic video recording, followed by an iterative semi-automated annotation process to ensure quality and reliability. For evaluation, we perform a fine-grained analysis using modern deep-learning architectures, including VGG, EfficientNet, and Vision Transformers. Experimental results are obtained through model simulations. These results establish a new benchmark in vision-based traffic analytics for developing countries. Our best-performing model achieves an accuracy of 97.3%, demonstrating the potential of the data set to advance ITS applications.

    Keywords: Vehicle make and model recognition (VMMR), intelligent transportation systems (ITS), Fine-grained classification, Traffic Analysis, Benchmarking, vehicle data set

    Received: 16 Jan 2025; Accepted: 31 Mar 2025.

    Copyright: © 2025 Hayee, Hussain, Yousaf, Yasir, Ahmad and Viriri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Fawad Hussain, University of Engineering and Technology, Taxila, Taxila, Pakistan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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