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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 1–25 of 63 papers

TitleStatusHype
U-Net: Convolutional Networks for Biomedical Image SegmentationCode3
SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in StructuresCode3
Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil InfrastructureCode2
Learning-Based Defect Recognitions for Autonomous UAV InspectionsCode2
Topology-aware Mamba for Crack Segmentation in StructuresCode1
CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV InspectionsCode1
UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image RestorationCode1
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded ImagesCode1
Mamba meets crack segmentationCode1
Local Intensity Order Transformation for Robust Curvilinear Object SegmentationCode1
Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack SegmentationCode1
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
Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack DetectorsCode1
Real-time High-Resolution Neural Network with Semantic Guidance for Crack SegmentationCode1
SegFormer: Simple and Efficient Design for Semantic Segmentation with TransformersCode1
Segmentation tool for images of cracksCode1
Automatic Road Crack Detection Using Random Structured Forests—0
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