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Robust pixel-wise concrete crack segmentation and properties retrieval using image patches

https://doi.org/10.1016/j.autcon.2020.103535
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Autonomous concrete crack detection using deep fully convolutional neural network
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Computer vision-based concrete crack detection using U-net fully convolutional networks
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Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection
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Robust Automated Concrete Damage Detection Algorithms for Field Applications
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Image-Based Automated 3D Crack Detection for Post-disaster Building Assessment
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Concrete Crack Assessment Using Digital Image Processing and 3D Scene Reconstruction
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Automatic Pavement-Crack Detection and Segmentation Based on Steerable Matched Filtering and an Active Contour Model
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inferredcites →https://doi.org/10.1061/9780784482865.056openalex-snapshot
Vision-Based Semi-Supervised Learning Method for Concrete Crack Detection
inferredcites →https://doi.org/10.1109/83.902291openalex-snapshot
Active contours without edges
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A binary level set model and some applications to Mumford-Shah image segmentation
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inferredcites →https://doi.org/10.1109/tmi.2016.2528162openalex-snapshot
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A texture‐Based Video Processing Methodology Using Bayesian Data Fusion for Autonomous Crack Detection on Metallic Surfaces
inferredcites →https://doi.org/10.1111/mice.12263openalex-snapshot
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
inferredcites →https://doi.org/10.1111/mice.12353openalex-snapshot
Vision‐Based Fatigue Crack Detection of Steel Structures Using Video Feature Tracking
inferredcites →https://doi.org/10.1111/mice.12412openalex-snapshot
Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network
inferredcites →https://doi.org/10.1117/1.2740762openalex-snapshot
Boundary-based image segmentation using binary level set method
inferredcites →https://doi.org/10.1117/12.915384openalex-snapshot
Computer-vision based crack detection and analysis
inferredcites →https://doi.org/10.1118/1.2409238openalex-snapshot
Subvoxel precise skeletons of volumetric data based on fast marching methods
inferredcites →https://doi.org/10.1126/science.aag2612openalex-snapshot
A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs
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ImageNet classification with deep convolutional neural networks
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Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding
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A Local Active Contour Model for Image Segmentation with Intensity Inhomogeneity

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Towards Robust Semantic Segmentation Models for Infrastructure Defect: Benchmarks and Fourier-Based Improvement Strategies
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A review of recent advances in data-driven computer vision methods for structural damage evaluation: algorithms, applications, challenges, and future opportunities
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Automatic classification and isolation of cracks on masonry surfaces using deep transfer learning and semantic segmentation
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An integrated mask R-CNN framework for concrete crack segmentation and skeleton-based quantification in automated infrastructure inspection
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Integrating PCA and GVF Snakes for High-Accuracy Metal Surface Defect Detection in Robotics Automation
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