Proceedings of the Multimedia University Engineering Conference (MECON 2022)

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Vehicle Types Recognition in Night-Time Scene

Authors
Willy Liew1, *, Mohd Haris Lye Abdullah1, *, Rehan Shahid2, Amr Ahmed2
1Faculty of Engineering, Multimedia University, Persiaran Multimedia, 63100, Cyberjaya, Selangor, Malaysia
2Tapway, Pacific Place, Block C, Commercial Centre, C-3-1, Jalan PJU 1a/4, Ara Damansara, 47301, Petaling Jaya, Selangor, Malaysia
*Corresponding author. Email: 1171101719@student.mmu.edu.my
*Corresponding author. Email: haris.lye@mmu.edu.my
Corresponding Authors
Willy Liew, Mohd Haris Lye Abdullah
Available Online 23 December 2022.
DOI
10.2991/978-94-6463-082-4_15How to use a DOI?
Keywords
Vehicle recognition; Object detection model; Generative adversarial network (GAN)
Abstract

Vehicle type recognition in night-time scene is a challenging issue to be resolved due to insufficient luminance, complex lighting environment in night-time and scarcity of public night-time vehicle dataset. Hence, in this paper, we analyse and evaluate the performance of several state-of-the-art model architectures including Faster R-CNN, YOLO and SSD for vehicle detection in night-time scene. Through comparison of evaluation metrics, YOLOv3 with DarkNet-53 achieves the best trade-off between detection accuracy and model architecture complexity, with Average Precision (AP) of 87.43%, recall rate of 91.48% and processing speed of 13.06 FPS with UA-DETRAC validation dataset. In addition, daytime to night-time image augmentation techniques through Neural Style Transfer (NST), conditional GAN (cGAN) and Cycle-Consistent Adversarial Networks (CycleGAN) are implemented to increase the number of night-time images for training dataset by translating the daytime images into night-time scene. Among the three approaches, CycleGAN can generate realistic and natural synthesized night-time images which contribute to improving detection accuracy of the vehicle type recognition model from mAP of 91.81% to 96.47%. Finally, we implement multiple objects tracking technique with Deep SORT algorithm to perform vehicle counting.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Multimedia University Engineering Conference (MECON 2022)
Series
Advances in Engineering Research
Publication Date
23 December 2022
ISBN
978-94-6463-082-4
ISSN
2352-5401
DOI
10.2991/978-94-6463-082-4_15How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Willy Liew
AU  - Mohd Haris Lye Abdullah
AU  - Rehan Shahid
AU  - Amr Ahmed
PY  - 2022
DA  - 2022/12/23
TI  - Vehicle Types Recognition in Night-Time Scene
BT  - Proceedings of the Multimedia University Engineering Conference (MECON 2022)
PB  - Atlantis Press
SP  - 139
EP  - 153
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-082-4_15
DO  - 10.2991/978-94-6463-082-4_15
ID  - Liew2022
ER  -