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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 1–50 of 63 papers

TitleStatusHype
Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations—0
On-Device Crack Segmentation for Edge Structural Health Monitoring—0
SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in StructuresCode3
FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM—0
Cracks in concrete—0
Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images—0
CrossDiff: Diffusion Probabilistic Model With Cross-conditional Encoder-Decoder for Crack Segmentation—0
Unified Few-shot Crack Segmentation and its Precise 3D Automatic Measurement in Concrete Structures—0
CrackUDA: Incremental Unsupervised Domain Adaptation for Improved Crack Segmentation in Civil Structures—0
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices—0
DSCformer: A Dual-Branch Network Integrating Enhanced Dynamic Snake Convolution and SegFormer for Crack Segmentation—0
Multi-temporal crack segmentation in concrete structure using deep learning approaches—0
Deep Learning-Based Fatigue Cracks Detection in Bridge Girders using Feature Pyramid Networks—0
Topology-aware Mamba for Crack Segmentation in StructuresCode1
Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation—0
Distribution-aware Noisy-label Crack SegmentationCode0
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
IRFusionFormer: Enhancing Pavement Crack Segmentation with RGB-T Fusion and Topological-Based LossCode1
EfficientCrackNet: A Lightweight Model for Crack Segmentation—0
Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil InfrastructureCode2
Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack SegmentationCode1
Mamba meets crack segmentationCode1
Vision Mamba-based autonomous crack segmentation on concrete, asphalt, and masonry surfaces—0
Robust feature knowledge distillation for enhanced performance of lightweight crack segmentation models—0
Segmentation tool for images of cracksCode1
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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