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

arXiv: https://arxiv.org/abs/2008.06840 · HTML: https://mlsanity.com/paper/2008.06840/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2020.

## Built on by (most cited first, 12 of 12)

- [PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching](https://mlsanity.com/paper/2103.07094/) (2021)
- [Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms](https://mlsanity.com/paper/2103.02433/) (2021)
- [Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms](https://mlsanity.com/paper/2204.13590/) (2022)
- [Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms](https://mlsanity.com/paper/2109.02711/) (2021)
- [SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data](https://mlsanity.com/paper/2205.13326/) (2022)
- [UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite](https://mlsanity.com/paper/2304.08842/) (2023)
- [Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection](https://mlsanity.com/paper/2112.13082/) (2021)
- [Adaptive-Mask Fusion Network for Segmentation of Drivable Road and Negative Obstacle With Untrustworthy Features](https://mlsanity.com/paper/2304.13979/) (2023)
- [Computer-Aided Road Inspection: Systems and Algorithms](https://mlsanity.com/paper/2203.02355/) (2022)
- [DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder](https://mlsanity.com/paper/2111.03480/) (2021)
- [Classification of Potholes Based on Surface Area Using Pre-Trained Models of Convolutional Neural Network](https://mlsanity.com/paper/2309.17426/) (2023)
- [RoadFusion: Latent Diffusion Model for Pavement Defect Detection](https://mlsanity.com/paper/2507.15346/) (2025)

## Abstract (arXiv)

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.