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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
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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