# Real-Time Dense Stereo Embedded in A UAV for Road Inspection

arXiv: https://arxiv.org/abs/1904.06017 · HTML: https://mlsanity.com/paper/1904.06017/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [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)
- [RoadBEV: Road Surface Reconstruction in Bird's Eye View](https://mlsanity.com/paper/2404.06605/) (2024)
- [These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion](https://mlsanity.com/paper/2411.03717/) (2024)
- [Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection](https://mlsanity.com/paper/2112.13082/) (2021)
- [FastRSR: Efficient and Accurate Road Surface Reconstruction from Bird's Eye View](https://mlsanity.com/paper/2504.09535/) (2025)
- [Computer-Aided Road Inspection: Systems and Algorithms](https://mlsanity.com/paper/2203.02355/) (2022)
- [An Empirical Evaluation of Four Off-the-Shelf Proprietary Visual-Inertial Odometry Systems](https://mlsanity.com/paper/2207.06780/) (2022)

## Abstract (arXiv)

The condition assessment of road surfaces is essential to ensure their serviceability while still providing maximum road traffic safety. This paper presents a robust stereo vision system embedded in an unmanned aerial vehicle (UAV). The perspective view of the target image is first transformed into the reference view, and this not only improves the disparity accuracy, but also reduces the algorithm's computational complexity. The cost volumes generated from stereo matching are then filtered using a bilateral filter. The latter has been proved to be a feasible solution for the functional minimisation problem in a fully connected Markov random field model. Finally, the disparity maps are transformed by minimising an energy function with respect to the roll angle and disparity projection model. This makes the damaged road areas more distinguishable from the road surface. The proposed system is implemented on an NVIDIA Jetson TX2 GPU with CUDA for real-time purposes. It is demonstrated through experiments that the damaged road areas can be easily distinguished from the transformed disparity maps.