Pothole Detection Based on Disparity Transformation and Road Surface Modeling
TLDR
Wang et al. as mentioned in this paper presented a robust pothole detection algorithm that is both accurate and computationally efficient, where a dense disparity map is first transformed to better distinguish between damaged and undamaged road areas.Abstract:
Pothole detection is one of the most important tasks for road maintenance. Computer vision approaches are generally based on either 2D road image analysis or 3D road surface modeling. However, these two categories are always used independently. Furthermore, the pothole detection accuracy is still far from satisfactory. Therefore, in this paper, we present a robust pothole detection algorithm that is both accurate and computationally efficient. A dense disparity map is first transformed to better distinguish between damaged and undamaged road areas. To achieve greater disparity transformation efficiency, golden section search and dynamic programming are utilized to estimate the transformation parameters. Otsu’s thresholding method is then used to extract potential undamaged road areas from the transformed disparity map. The disparities in the extracted areas are modeled by a quadratic surface using least squares fitting. To improve disparity map modeling robustness, the surface normal is also integrated into the surface modeling process. Furthermore, random sample consensus is utilized to reduce the effects caused by outliers. By comparing the difference between the actual and modeled disparity maps, the potholes can be detected accurately. Finally, the point clouds of the detected potholes are extracted from the reconstructed 3D road surface. The experimental results show that the successful detection accuracy of the proposed system is around 98.7% and the overall pixel-level accuracy is approximately 99.6%.read more
Citations
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Book ChapterDOI
SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection
TL;DR: This paper introduces a novel module, named surface normal estimator (SNE), which can infer surface normal information from dense depth/disparity images with high accuracy and efficiency, and proposes a data-fusion CNN architecture, referred to as RoadSeg, which can extract and fuse features from both RGB images and the inferred surfacenormal information for accurate freespace detection.
Book ChapterDOI
SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection
TL;DR: Wang et al. as discussed by the authors proposed a data-fusion CNN architecture, referred to as RoadSeg, which can extract and fuse features from both RGB images and the inferred surface normal information for accurate freespace detection.
Journal ArticleDOI
Road Damage Detection Based on Unsupervised Disparity Map Segmentation
TL;DR: A novel road damage detection algorithm based on unsupervised disparity map segmentation that requires no parameters when detecting road damage and performs both accurately and efficiently.
Journal ArticleDOI
Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms.
TL;DR: Li et al. as mentioned in this paper proposed a dynamic fusion module (DFM), which can be easily deployed in existing data-fusion networks to fuse different types of visual features effectively and efficiently.
Proceedings ArticleDOI
Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots
TL;DR: Zhang et al. as discussed by the authors developed a Normal Inference Module (NIM) which can generate surface normal information from dense depth images with high accuracy and efficiency, which can be deployed in existing convolutional neural networks (CNNs) to refine the segmentation performance.
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