Open Access
ARTICLE
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. https://doi.org/10.32604/cmc.2020.011386
Received 06 May 2020; Accepted 05 June 2020; Issue published 16 September 2020
Abstract
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.
Keywords
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. https://doi.org/10.32604/cmc.2020.011386
Citations