  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.
