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Crack Segmentation

Crack segmentation in computer vision involves identifying and delineating cracks or fractures in various types of surfaces, such as roads, pavements, walls, or infrastructure. This task is crucial for infrastructure maintenance, as it helps in assessing the condition of structures and planning repairs.

Papers

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Showing 11–20 of 63 papers

TitleStatusHype
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded ImagesCode1
CrackFormer: Transformer Network for Fine-Grained Crack DetectionCode1
Fully Convolutional Networks for Semantic SegmentationCode1
CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and FrameworksCode1
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-ResolutionCode1
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
A Convolutional-Transformer Network for Crack Segmentation with Boundary AwarenessCode1
Dual Super-Resolution Learning for Semantic SegmentationCode1
Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels using Localization with a Classifier and ThresholdingCode1
IRFusionFormer: Enhancing Pavement Crack Segmentation with RGB-T Fusion and Topological-Based LossCode1
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