Crack Segmentation
25 papers with code • 3 benchmarks • 4 datasets
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.
Benchmarks
These leaderboards are used to track progress in Crack Segmentation
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Libraries
Use these libraries to find Crack Segmentation models and implementations
CRACK500
Khanhha's dataset
CrackVision12K
Most implemented papers
U-Net: Convolutional Networks for Biomedical Image Segmentation
labmlai/annotated_deep_learning_paper_implementations
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
Fully Convolutional Networks for Semantic Segmentation
pochih/fcn-pytorch
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CVPR 2015
Convolutional networks are powerful visual models that yield hierarchies of features.
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
NVlabs/SegFormer
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NeurIPS 2021
We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders.
CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks
We also present a pipeline that combines Image Processing and Deep Learning models.
Dual Super-Resolution Learning for Semantic Segmentation
Specifically, for semantic segmentation on CityScapes, we can achieve \geq2% higher mIoU with similar FLOPs, and keep the performance with 70% FLOPs.
Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors
hitachi-rd-cv/weakly-sup-crackdet
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Automatic crack detection is a critical task that has the potential to drastically reduce labor-intensive building and road inspections currently being done manually.
CrackFormer: Transformer Network for Fine-Grained Crack Detection
LouisNUST/CrackFormer-II
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ICCV 2021
The CrackFormer is composed of novel attention modules in a SegNet-like encoder-decoder architecture.
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution
Yuki-11/CSSR
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International Conference on Machine Vision and Applications (MVA) 2021
This paper proposes a method for crack segmentation on low-resolution images.
Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels using Localization with a Classifier and Thresholding
jacobkoenig/WeaklySupervisedCrackSeg
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Our work proposes a weakly supervised approach that leverages a CNN classifier in a novel way to create surface crack pseudo labels.
Local Intensity Order Transformation for Robust Curvilinear Object Segmentation
ty-shi/liot
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This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes.
