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Improving synthetic to realistic semantic segmentation with parallel generative ensembles for autonomous urban driving

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journal contribution
posted on 05.10.2021, 08:29 by Dewei Yi, Hui FangHui Fang, Yining Hua, Jinya Su, Mohammed Quddus, Jungong Han
Semantic segmentation is paramount for autonomous vehicles to have a deeper understanding of the surrounding traffic environment and enhance safety. Deep neural networks (DNN) have achieved remarkable performances in semantic segmentation. However, training such a DNN requires a large amount of labelled data at pixel level. In practice, it is a labour-intensive task to manually annotate dense pixel-level labels. To tackle the problem associated with a small amount of labelled data, Deep Domain Adaptation (DDA) methods have recently been developed to examine the use of synthetic driving scenes so as to significantly reduce the manual annotation cost. Despite remarkable advances, these methods unfortunately suffer from the generalisability problem that fails to provide a holistic representation of the mapping from the source image domain to the target image domain. In this paper, we therefore develop a novel ensembled DDA to train models with different upsampling strategies, discrepancy and segmentation loss functions. The models are, therefore, complementary with each other to achieve better generalisation in the target image domain. Such a design does not only improve the adapted semantic segmentation performance, but also strengthen the model reliability and robustness. Extensive experimental results demonstrate the superiorities of our approach over several state-of-the-art methods.

Funding

University of Aberdeen Internal Funding to Pump-Prime Interdisciplinary Research and Impact under grant number SF10206-57

History

School

  • Architecture, Building and Civil Engineering
  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Cognitive and Developmental Systems

Publisher

Institute of Electrical and Electronics Engineers

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

14/09/2021

Publication date

2021-10-05

Copyright date

2021

ISSN

2379-8920

eISSN

2379-8939

Language

en

Depositor

Dr Hui Fang. Deposit date: 4 October 2021