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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
SegFormer: Simple and Efficient Design for Semantic Segmentation with TransformersCode1
CrackFormer: Transformer Network for Fine-Grained Crack DetectionCode1
Joint Super-Resolution and Rectification for Solar Cell Inspection—0
Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack DetectorsCode1
Optimized Deep Encoder-Decoder Methods for Crack Segmentation—0
Dual Super-Resolution Learning for Semantic SegmentationCode1
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
U-Net: Convolutional Networks for Biomedical Image SegmentationCode3
Fully Convolutional Networks for Semantic SegmentationCode1
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