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

TL;DR

This paper introduces a novel attention aggregation framework and adversarial domain adaptation technique to improve road pothole detection accuracy using semantic segmentation, outperforming existing methods and enhancing training efficiency.

Contribution

The paper proposes a new attention aggregation framework and a domain adaptation-based data augmentation method for better pothole detection in semantic segmentation networks.

Findings

AA-UNet and AA-RTFNet outperform state-of-the-art methods

Disparity transformation enhances image informativeness

Adversarial domain adaptation accelerates training convergence

Abstract

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.