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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 41–50 of 63 papers

TitleStatusHype
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
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