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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 31–40 of 63 papers

TitleStatusHype
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
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
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
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