We Learn Better Road Pothole Detection: From Attention Aggregation to Adversarial Domain Adaptation

Rui Fan, Hengli Wang, Mohammud J. Bocus, Ming Liu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)


Manual visual inspection performed by certified inspectors is still the main form of road pothole detection. This process is, however, not only tedious, time-consuming and costly, but also dangerous for the inspectors. Furthermore, the road pothole detection results are always subjective, because they depend entirely on the individual experience. Our recently introduced disparity (or inverse depth) transformation algorithm allows better discrimination between damaged and undamaged road areas, and it can be easily deployed to any semantic segmentation network for better road pothole detection results. To boost the performance, we propose a novel attention aggregation (AA) framework, which takes the advantages of different types of attention modules. In addition, we develop an effective training set augmentation technique based on adversarial domain adaptation, where the synthetic road RGB images and transformed road disparity (or inverse depth) images are generated to enhance the training of semantic segmentation networks. The experimental results demonstrate that, firstly, the transformed disparity (or inverse depth) images become more informative; secondly, AA-UNet and AA-RTFNet, our best performing implementations, respectively outperform all other state-of-the-art single-modal and data-fusion networks for road pothole detection; and finally, the training set augmentation technique based on adversarial domain adaptation not only improves the accuracy of the state-of-the-art semantic segmentation networks, but also accelerates their convergence.
Original languageEnglish
Title of host publicationComputer Vision -- ECCV 2020 Workshops
EditorsAdrien Bartoli, Andrea Fusiello
PublisherSpringer International Publishing AG
Number of pages16
VolumePart IV
ISBN (Electronic)978-3-030-66823-5
ISBN (Print)978-3-030-66822-8
Publication statusPublished - 3 Jan 2021
EventComputer Vision – ECCV 2020 Workshops - Glasgow, United Kingdom
Duration: 23 Aug 202028 Aug 2020
Conference number: 16

Publication series

NameLecture Notes in Computer Science


ConferenceComputer Vision – ECCV 2020 Workshops
Abbreviated titleECCV2020
CountryUnited Kingdom
Internet address

Bibliographical note

Funding Information:
Acknowledgements. This work was supported by the National Natural Science Foundation of China, under grant No. U1713211, Collaborative Research Fund by Research Grants Council Hong Kong, under Project No. C4063-18G, and the Research Grant Council of Hong Kong SAR Government, China, under Project No. 11210017, awarded to Prof. Ming Liu.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.


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