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Framework for Vehicle Make and Model Recognition-A New Large-Scale Dataset and an Efficient Two-Branch-Two-Stage Deep

Yangxintong Lyu1, Ionut Schiopu1, Bruno Cornelis1,2

  • 1Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.

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|November 11, 2022
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Summary
This summary is machine-generated.

This study introduces a new dataset and a two-branch deep learning model for vehicle make and model recognition (VMMR). The novel approach achieves high accuracy, outperforming single-branch methods by reducing confusion in recognizing vehicle details.

Keywords:
Intelligent Transportation System (ITS)Vehicle Make and Model Recognitiondeep-learninglarge-scale vehicle datasettwo-branch strategy

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Intelligent Transportation Systems (ITS)

Background:

  • Vehicle Make and Model Recognition (VMMR) is vital for Intelligent Transportation Systems (ITS), enabling applications like intelligent surveillance and autonomous driving.
  • Existing VMMR systems require accurate and efficient performance in real-world scenarios.

Purpose of the Study:

  • To introduce a new large-scale dataset, Diverse large-scale VMM (DVMM), featuring popular European vehicle brands.
  • To propose a novel two-branch deep learning framework for enhanced VMMR.
  • To develop a new metric for evaluating classification confusion in VMMR.

Main Methods:

  • A novel two-branch deep learning architecture was designed for separate make and model recognition.
  • A two-stage training procedure and a unique decision module were implemented for processing predictions.
  • A new metric based on the true positive rate was introduced to assess classification confusion.

Main Results:

  • The proposed framework achieved 93.95% accuracy on the DVMM dataset and 95.85% on traditional datasets.
  • The two-branch approach demonstrated superior performance over one-branch methods across various dataset scales.
  • The method significantly reduced vehicle model confusion and inter-make ambiguity.

Conclusions:

  • The proposed DVMM dataset is general, diverse, and practical for VMMR research.
  • The novel two-branch VMMR paradigm offers improved robustness and reduced confusion compared to single-branch designs.
  • The study highlights the effectiveness of the proposed deep learning framework for accurate vehicle recognition in ITS.