Road Damage Detection Based on Unsupervised Disparity Map Segmentation
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%.
Code (1)
Tasks
Road Damage DetectionSimilar Papers 제목 키워드 기반
Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms
Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based. The former approaches typically employ 2-D image analysis/understanding or 3-D point cloud modeling and seg…
Graph AttentionGraph Neural NetworkSegmentationSemantic SegmentationRethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation
Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety and vehicle condition. Pothole detection is typically performed by either structural engineers or certified…
3D ReconstructionClusteringGPUSuperpixelsWe Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation
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. F…
Domain AdaptationSegmentationSemantic SegmentationThermal Image SegmentationPothole Detection Based on Disparity Transformation and Road Surface Modeling
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 al…
Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection
This paper presents a novel pothole detection approach based on single-modal semantic segmentation. It first extracts visual features from input images using a convolutional neural network. A channel attention module the…
SegmentationSemantic Segmentation