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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 21–30 of 63 papers

TitleStatusHype
CrackFormer: Transformer Network for Fine-Grained Crack DetectionCode1
Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack DetectorsCode1
Dual Super-Resolution Learning for Semantic SegmentationCode1
Fully Convolutional Networks for Semantic SegmentationCode1
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
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
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