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Manual visual inspection performed by certified inspectors is still the mainform 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 dependentirely on the individual experience. Our recently introduced disparity (orinverse depth) transformation algorithm allows better discrimination betweendamaged and undamaged road areas, and it can be easily deployed to any semanticsegmentation network for better road pothole detection results. To boost theperformance, we propose a novel attention aggregation (AA) framework, whichtakes the advantages of different types of attention modules. In addition, wedevelop an effective training set augmentation technique based on adversarialdomain adaptation, where the synthetic road RGB images and transformed roaddisparity (or inverse depth) images are generated to enhance the training ofsemantic segmentation networks. The experimental results demonstrate that,firstly, the transformed disparity (or inverse depth) images become moreinformative; secondly, AA-UNet and AA-RTFNet, our best performingimplementations, respectively outperform all other state-of-the-artsingle-modal and data-fusion networks for road pothole detection; and finally,the training set augmentation technique based on adversarial domain adaptationnot only improves the accuracy of the state-of-the-art semantic segmentationnetworks, but also accelerates their convergence.

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