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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
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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