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A New Dataset for Vehicle Logo Detection

  • Shuo Yang
  • Chunjuan Bo
  • Junxing ZhangEmail author
  • Meng Wang
  • Lijun Chen
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

This paper establishes a multilevel dataset for solving the vehicle logo detection task; we call it ‘VLD-30’. Vehicle logo detection is applied to the Intelligent Transport System widely, such as vehicle monitoring. As for the object detection algorithm of deep-learning, a good dataset can improve the robustness of it. Our dataset has a very high reliability by including analysis on various factors. In order to confirm the dataset performance, we use the typical target detection algorithm, such as Faster-RCNN and YOLO. The experimental results show that our dataset achieves significant improvements for the small object detection, and vehicle logo detection is potential to be developed.

Keywords

VLD-30 Deep-learning Vehicle logo detection 

Notes

Acknowledgements

This work is supported by Key Research Guidance Plan Project of Liaoning Province (No. 2017104013), Natural Science Foundation of Liaoning Province (No. 201700133).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shuo Yang
    • 1
  • Chunjuan Bo
    • 2
    • 3
  • Junxing Zhang
    • 1
    Email author
  • Meng Wang
    • 1
  • Lijun Chen
    • 1
  1. 1.College of Electromechanical EngineeringDalian Minzu UniversityDalianChina
  2. 2.College of Information and Communication EngineeringDalian Minzu UniversityDalianChina
  3. 3.Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs CommissionDalian Minzu UniversityDalianChina

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