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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 51–60 of 63 papers

TitleStatusHype
Recovering compressed images for automatic crack segmentation using generative models—0
RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid Attention Mechanisms for Pavement Crack Segmentation—0
Robust feature knowledge distillation for enhanced performance of lightweight crack segmentation models—0
SCNet: A Generalized Attention-based Model for Crack Fault Segmentation—0
Cracks in concrete—0
CrackUDA: Incremental Unsupervised Domain Adaptation for Improved Crack Segmentation in Civil Structures—0
CrossDiff: Diffusion Probabilistic Model With Cross-conditional Encoder-Decoder for Crack Segmentation—0
Deep Learning-Based Fatigue Cracks Detection in Bridge Girders using Feature Pyramid Networks—0
Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges—0
Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges—0
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