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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 51–63 of 63 papers

TitleStatusHype
Optimized Hybrid Focal Margin Loss for Crack Segmentation—0
The Devil is in the Crack Orientation: A New Perspective for Crack Detection—0
RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid Attention Mechanisms for Pavement Crack Segmentation—0
Weakly-Supervised Crack Detection—0
What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification—0
SCNet: A Generalized Attention-based Model for Crack Fault Segmentation—0
Joint Super-Resolution and Rectification for Solar Cell Inspection—0
Optimized Deep Encoder-Decoder Methods for Crack Segmentation—0
Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges—0
Recovering compressed images for automatic crack segmentation using generative models—0
A Deep Neural Networks Approach for Pixel-Level Runway Pavement Crack Segmentation Using Drone-Captured Images—0
Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network—0
Automatic Road Crack Detection Using Random Structured Forests—0
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