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IJMLC 2020 Vol.10(4): 549-555 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.4.971

Robust Vehicle Detection Under Adverse Weather Conditions Using Auto-encoder Feature

V. D. Nguyen, D. D. Tran, M. M. Tran, N. M. Nguyen, and V. C. Nguyen

Abstract—Existing deep learning-based obstacle detection systems are often designed and implemented based on raw input feature. These systems obtain high accuracy under normal driving conditions. But they fail to operate under difficult driving conditions, which are different from their training. Recently, an unsupervised auto-encoder has been successfully applied to produce robust input features for a stereo matching system under difficult driving conditions. Therefore, this paper investigates an auto-encoder feature to improve the performance of existing vehicle detections under adverse weather conditions. Experimental results show that the proposed method obtained better result than existing state-of-the-art object detection methods in term of accuracy.

Index Terms—Vehicle detection, auto-encoder, deep learning, and local binary pattern.

V. D. Nguyen, V. C. Nguyen, D. D. Tran, M. M. Tran, and N. M. Nguyen are with the Department of Software Engineering, School of Computing and Information Technology, Eastern International University, Vietnam (e-mail: vinh.nguyen@eiu.edu.vn, vu.nguyen@eiu.edu.vn, man.tran.set15@eiu.edu.vn, duy.tran.set15@eiu.edu.vn, nhan.nguyen@eiu.edu.vn).

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Cite: V. D. Nguyen, D. D. Tran, M. M. Tran, N. M. Nguyen, and V. C. Nguyen, "Robust Vehicle Detection Under Adverse Weather Conditions Using Auto-encoder Feature," International Journal of Machine Learning and Computing vol. 10, no. 4, pp. 549-555, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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