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Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture.

Hyo Jong Lee1, Ihsan Ullah2, Weiguo Wan3

  • 1Division of Computer Science and Engineering, CAIIT, Chonbuk National University, Jeonju 54896, Korea. hlee@chonbuk.ac.kr.

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Summary

This study introduces a new deep learning method for vehicle make and model recognition (MMR) using an efficient SqueezeNet model. The approach achieves high accuracy with fast processing, making it suitable for real-time applications.

Keywords:
deep learningresidual SqueezeNetvehicle make recognition

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Artificial Intelligence

Background:

  • Vehicle make and model recognition (MMR) is crucial for automated visual systems.
  • Existing methods may lack efficiency or accuracy for large-scale datasets.

Purpose of the Study:

  • To develop a novel and efficient deep learning approach for vehicle MMR.
  • To leverage the SqueezeNet architecture for improved performance.

Main Methods:

  • Utilized a modified SqueezeNet architecture with bypass connections for enhanced efficiency.
  • Trained and tested the model on a large-scale dataset of frontal vehicle images.

Main Results:

  • Achieved a 96.3% recognition rate at the rank-1 level.
  • Demonstrated an efficient processing time of 108.8 ms per image.
  • The deep model requires less than 5 MB of space for inference.

Conclusions:

  • The proposed SqueezeNet-based MMR system is highly accurate and efficient.
  • The model's small footprint and speed make it viable for real-time applications.