Crack Segmentation
13 papers with code • 2 benchmarks • 3 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
CRACK500
Khanhha's dataset
Most implemented papers
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.
Learning-Based Defect Recognitions for Autonomous UAV Inspections
KangchengLiu/Crack-Detection-and-Segmentation-Dataset-for-UAV-Inspection
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We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmark dataset on the internet for crack detection and segmentation, which is open-sourced for the research community.
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images
yuki-11/csbsr
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IEEE Transactions on Instrumentation and Measurement (TIM) 2024
This paper proposes crack segmentation augmented by super resolution (SR) with deep neural networks.
Infrastructure Crack Segmentation: Boundary Guidance Method and Benchmark Dataset
Cracks provide an essential indicator of infrastructure performance degradation, and achieving high-precision pixel-level crack segmentation is an issue of concern.
