Journal ArticleDOI
Image-Based Crack Detection for Real Concrete Surfaces
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TLDR
An image-based percolation model is proposed that extracts a continuous texture by referring to the connectivity of brightness and the shape of the percolated region, depending on the length criterion of the scalable local image processing techniques.Abstract:
In this paper, we introduce a novel image-based approach to detect cracks in concrete surfaces. Crack detection is important for the inspection, diagnosis, and maintenance of concrete structures. However, conventional image-based approaches cannot achieve precise detection since the image of the concrete surface contains various types of noise due to different causes such as concrete blebs, stain, insufficient contrast, and shading. In order to detect the cracks with high fidelity, we assume that they are composed of thin interconnected textures and propose an image-based percolation model that extracts a continuous texture by referring to the connectivity of brightness and the shape of the percolated region, depending on the length criterion of the scalable local image processing techniques. Additionally, noise reduction based on the percolation model is proposed. We evaluated the validity of the proposed technique by using precision recall and receiver operating characteristic (ROC) analysis by means of some experiments with actual concrete surface images. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.read more
Citations
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Journal ArticleDOI
Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks
TL;DR: This article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features, and shows quite better performances and can indeed find concrete cracks in realistic situations.
Journal ArticleDOI
A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure
TL;DR: This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements.
Journal ArticleDOI
Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network
TL;DR: The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures as mentioned in this paper, however, the current manual crack description is inadequate and outdated.
Journal ArticleDOI
Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete
TL;DR: Computational times for DCNN are shorter than the most efficient edge detection algorithms, not considering the training process, and show significant promise for future adoption of DCNN methods for image-based damage detection in concrete.
Journal ArticleDOI
Concrete Crack Detection by Multiple Sequential Image Filtering
TL;DR: A new robust automated image processing method for detecting cracks in surface images of concrete structures using genetic programming and elimination of residual noise after filtering and detection of indistinct cracks by iterative applications of the image filter to the local regions surrounding the cracks.
References
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Book
Introduction to percolation theory
Dietrich Stauffer,Amnon Aharony +1 more
TL;DR: In this paper, a scaling solution for the Bethe lattice is proposed for cluster numbers and a scaling assumption for cluster number scaling assumptions for cluster radius and fractal dimension is proposed.
Book
Introduction to percolation theory
Dietrich Stauffer,Amnon Aharony +1 more
TL;DR: In this article, a scaling solution for the Bethe lattice is proposed for cluster numbers and a scaling assumption for cluster number scaling assumptions for cluster radius and fractal dimension is proposed.
Journal ArticleDOI
Analysis of edge-detection techniques for crack identification in bridges
TL;DR: This paper provides a comparison of the effectiveness of four crack-detection techniques: fast Haar transform (FHT), fast Fourier transform, Sobel, and Canny and shows that the FHT was significantly more reliable than the other three edge-detector techniques in identifying cracks.
Journal ArticleDOI
Receiver operating characteristic (ROC) analysis: Basic principles and applications in radiology
TL;DR: The basic principles underlying ROC analysis will be explained and practical information on the use and interpretation of ROC curves will be provided.
Journal ArticleDOI
Improved image analysis for evaluating concrete damage
Tara C. Hutchinson,Zhiqiang Chen +1 more
TL;DR: This work illustrates the derivation of the method for conducting image analysis, which is grounded in Bayesian decision theory and subsequently presents results of the analysis of images with discrete cracks to illustrate its promise.