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.
|Title of host publication||Computer Vision -- ECCV 2020 Workshops|
|Editors||Adrien Bartoli, Andrea Fusiello|
|Publisher||Springer International Publishing AG|
|Number of pages||16|
|Publication status||Published - 3 Jan 2021|
|Event||Computer Vision – ECCV 2020 Workshops - Glasgow, United Kingdom|
Duration: 23 Aug 2020 → 28 Aug 2020
Conference number: 16
|Name||Lecture Notes in Computer Science|
|Conference||Computer Vision – ECCV 2020 Workshops|
|Period||23/08/20 → 28/08/20|
Bibliographical noteFunding 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.
© 2020, Springer Nature Switzerland AG.