# Road Damage Detection Based on Unsupervised Disparity Map Segmentation

arXiv: https://arxiv.org/abs/1910.04988 · HTML: https://mlsanity.com/paper/1910.04988/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 24 of 24)

- [Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms](https://mlsanity.com/paper/2103.02433/) (2021)
- [SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection](https://mlsanity.com/paper/2107.14599/) (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)
- [RoadFormer+: Delivering RGB-X Scene Parsing through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion](https://mlsanity.com/paper/2407.21631/) (2024)
- [SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint Learning](https://mlsanity.com/paper/2407.05283/) (2024)
- [SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data](https://mlsanity.com/paper/2205.13326/) (2022)
- [UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration](https://mlsanity.com/paper/2401.15647/) (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)
- [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)
- [From Ideal to Real: Unified and Data-Efficient Dense Prediction for Real-World Scenarios](https://mlsanity.com/paper/2506.20279/) (2025)
- [Computer-Aided Road Inspection: Systems and Algorithms](https://mlsanity.com/paper/2203.02355/) (2022)
- [Surface Defect Detection and Evaluation for Marine Vessels using Multi-Stage Deep Learning](https://mlsanity.com/paper/2203.09580/) (2022)
- [E3CM: Epipolar-Constrained Cascade Correspondence Matching](https://mlsanity.com/paper/2308.16555/) (2023)
- [Freespace Optical Flow Modeling for Automated Driving](https://mlsanity.com/paper/2307.15989/) (2023)
- [Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms](https://mlsanity.com/paper/2503.18082/) (2025)
- [SIM2E: Benchmarking the Group Equivariant Capability of Correspondence Matching Algorithms](https://mlsanity.com/paper/2208.09896/) (2022)
- [Cooperative Saliency-based Obstacle Detection and AR Rendering for Increased Situational Awareness](https://mlsanity.com/paper/2302.00916/) (2023)
- [Classification of Potholes Based on Surface Area Using Pre-Trained Models of Convolutional Neural Network](https://mlsanity.com/paper/2309.17426/) (2023)
- [Towards Increasing the Robustness of Predictive Steering-Control Autonomous Navigation Systems Against Dash Cam Image Angle Perturbations Due to Pothole Encounters](https://mlsanity.com/paper/2310.03959/) (2023)
- [Establishing Reality-Virtuality Interconnections in Urban Digital Twins for Superior Intelligent Road Inspection and Simulation](https://mlsanity.com/paper/2412.17699/) (2024)
- [DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization](https://mlsanity.com/paper/2505.20041/) (2025)
- [WP-CrackNet: A Collaborative Adversarial Learning Framework for End-to-End Weakly-Supervised Road Crack Detection](https://mlsanity.com/paper/2510.17566/) (2025)

## Abstract (arXiv)

This paper presents a novel road damage detection algorithm based on unsupervised disparity map segmentation. Firstly, a disparity map is transformed by minimizing an energy function with respect to stereo rig roll angle and road disparity projection model. Instead of solving this energy minimization problem using non-linear optimization techniques, we directly find its numerical solution. The transformed disparity map is then segmented using Otus's thresholding method, and the damaged road areas can be extracted. The proposed algorithm requires no parameters when detecting road damage. The experimental results illustrate that our proposed algorithm performs both accurately and efficiently. The pixel-level road damage detection accuracy is approximately 97.56%.