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Real-Time Vehicle Color Recognition Based on YOLO9000

  • Xifang WuEmail author
  • Songlin Sun
  • Na Chen
  • Meixia Fu
  • Xiaoying Hou
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

In this paper, we proposed a real-time automated vehicle color recognition method using you look only once (YOLO)9000 object detection for intelligent transportation system applications in smart city. The workflow in our method contains only one step which achieves recognize vehicle colors from original images. The model proposed is trained and fine tuned for vehicle localization and color recognition so that it can be robust under different conditions (e.g., variations in background and lighting). Targeting a more realistic scenario, we introduce a dataset, called VDCR dataset, which collected on access surveillance. This dataset is comprised up of 5216 original images which include ten common colors of vehicles (white, black, red, blue, gray, golden, brown, green, yellow, and orange). In our proposed dataset, our method achieved the recognition rate of 95.47% and test-time for one image is 74.46 ms.

Keywords

Vehicle color recognition YOLO9000 Intelligent surveillance 

Notes

Acknowledgments

This work is supported by National Natural Science Foundation of China (Project 61471066) and the open project fund (No. 201600017) of the National Key Laboratory of Electromagnetic Environment, China.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Xifang Wu
    • 1
    • 2
    • 3
    Email author
  • Songlin Sun
    • 1
    • 2
    • 3
  • Na Chen
    • 1
    • 2
    • 3
  • Meixia Fu
    • 1
    • 2
    • 3
  • Xiaoying Hou
    • 1
    • 2
    • 3
  1. 1.School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of EducationBeijing University of Posts and TelecommunicationsBeijingChina
  3. 3.National Engineering Laboratory for Mobile Network SecurityBeijing University of Posts and TelecommunicationsBeijingChina

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