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Image and Feature Space Based Domain Adaptation for Vehicle Detection

Ying Tian1, *, Libing Wang1, Hexin Gu2, Lin Fan3

1 School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
2 School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan, 114051, China.
3 Faculty of Business, Economics & Law, The University of Queensland, Brisbane, QLD 4072, Australia.

* Corresponding Author: Ying Tian. Email: .

Computers, Materials & Continua 2020, 65(3), 2397-2412.


The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.


Cite This Article

Y. Tian, L. Wang, H. Gu and L. Fan, "Image and feature space based domain adaptation for vehicle detection," Computers, Materials & Continua, vol. 65, no.3, pp. 2397–2412, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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