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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
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
CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and FrameworksCode1
Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels using Localization with a Classifier and ThresholdingCode1
Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack DetectorsCode1
CrackFormer: Transformer Network for Fine-Grained Crack DetectionCode1
CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV InspectionsCode1
A Convolutional-Transformer Network for Crack Segmentation with Boundary AwarenessCode1
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-ResolutionCode1
Dual Super-Resolution Learning for Semantic SegmentationCode1
Fully Convolutional Networks for Semantic SegmentationCode1
Real-time High-Resolution Neural Network with Semantic Guidance for Crack SegmentationCode1
IRFusionFormer: Enhancing Pavement Crack Segmentation with RGB-T Fusion and Topological-Based LossCode1
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded ImagesCode1
Local Intensity Order Transformation for Robust Curvilinear Object SegmentationCode1
Mamba meets crack segmentationCode1
SegFormer: Simple and Efficient Design for Semantic Segmentation with TransformersCode1
Segmentation tool for images of cracksCode1
Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack SegmentationCode1
Topology-aware Mamba for Crack Segmentation in StructuresCode1
UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image RestorationCode1
Infrastructure Crack Segmentation: Boundary Guidance Method and Benchmark DatasetCode0
A Few-Shot Attention Recurrent Residual U-Net for Crack SegmentationCode0
Distribution-aware Noisy-label Crack SegmentationCode0
Unified Few-shot Crack Segmentation and its Precise 3D Automatic Measurement in Concrete Structures—0
EfficientCrackNet: A Lightweight Model for Crack Segmentation—0
Fine-tuning vision foundation model for crack segmentation in civil infrastructures—0
FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM—0
What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification—0
Vision Mamba-based autonomous crack segmentation on concrete, asphalt, and masonry surfaces—0
Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network—0
The Devil is in the Crack Orientation: A New Perspective for Crack Detection—0
Weakly-Supervised Crack Detection—0
Joint Super-Resolution and Rectification for Solar Cell Inspection—0
A statistical method for crack detection in 3D concrete images—0
TrueDeep: A systematic approach of crack detection with less data—0
A Deep Neural Networks Approach for Pixel-Level Runway Pavement Crack Segmentation Using Drone-Captured Images—0
Mind Marginal Non-Crack Regions: Clustering-Inspired Representation Learning for Crack Segmentation—0
Multi-temporal crack segmentation in concrete structure using deep learning approaches—0
On-Device Crack Segmentation for Edge Structural Health Monitoring—0
Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations—0
Automatic Road Crack Detection Using Random Structured Forests—0
Optimized Deep Encoder-Decoder Methods for Crack Segmentation—0
Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation—0
Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images—0
Optimized Hybrid Focal Margin Loss for Crack Segmentation—0
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices—0
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