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: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
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).