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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 26–50 of 63 papers

TitleStatusHype
Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges—0
CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV InspectionsCode1
A statistical method for crack detection in 3D concrete images—0
UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image RestorationCode1
Mind Marginal Non-Crack Regions: Clustering-Inspired Representation Learning for Crack Segmentation—0
Fine-tuning vision foundation model for crack segmentation in civil infrastructures—0
Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation—0
Real-time High-Resolution Neural Network with Semantic Guidance for Crack SegmentationCode1
Infrastructure Crack Segmentation: Boundary Guidance Method and Benchmark DatasetCode0
TrueDeep: A systematic approach of crack detection with less data—0
Dual flow fusion model for concrete surface crack segmentation—0
A Few-Shot Attention Recurrent Residual U-Net for Crack SegmentationCode0
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded ImagesCode1
A Convolutional-Transformer Network for Crack Segmentation with Boundary AwarenessCode1
Learning-Based Defect Recognitions for Autonomous UAV InspectionsCode2
Optimized Hybrid Focal Margin Loss for Crack Segmentation—0
The Devil is in the Crack Orientation: A New Perspective for Crack Detection—0
CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and FrameworksCode1
RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid Attention Mechanisms for Pavement Crack Segmentation—0
Weakly-Supervised Crack Detection—0
Local Intensity Order Transformation for Robust Curvilinear Object SegmentationCode1
What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification—0
SCNet: A Generalized Attention-based Model for Crack Fault Segmentation—0
Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels using Localization with a Classifier and ThresholdingCode1
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-ResolutionCode1
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