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I have problem for detection for surface ceramics image, how i can detect crack surface, pls give me some advice.

Crack detection matlab code. Automatic Concrete Crack Detection, Image processing, Construction Safety and. 0 Comments Leave a Reply. How can I run codes in a project about Road crack detection? These codes are Matlab. Dec 25, 2017 - MATLAB image processing codes. MATLAB language for image processing, such as image open, heavy, closed, vertical mirror image, horizontal mirror, gray scale, and. • The main problem about a railway analysis is detection of cracks in the structure. Ufs Hwk Suite Download Here Minecraft there. If these deficiencies are not controlled at early stages.

Abstract Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed.

First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Scrabble Junior Dora The Explorer Edition Instructions.

Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments. Citation: Shi P, Fan X, Ni J, Khan Z, Li M (2017) A novel underwater dam crack detection and classification approach based on sonar images. PLoS ONE 12(6): e0179627. Editor: Jonathan A. Coles, University of Glasgow, UNITED KINGDOM Received: March 11, 2016; Accepted: June 1, 2017; Published: June 22, 2017 Copyright: © 2017 Shi et al. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was funded by the National Natural Science Foundation of China (grant numbers 61573128, 61203365), and the Fundamental Research Funds for the Central Universities (grant number 2017B02914). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Introduction Numerous factors such as cracks, abrasions, cavitation, and erosion can threaten the safety of a dam []. Out of these, cracks represent the primary danger because they can exist not only at the dam’s surface but also extend into the interior [].

In other words, cracks in dams are the equivalent of mutations as dams accumulate internal damage []. Thus, cracks are always used to indicate the degree of risk in the field of dam damage, which has attracted the attention of numerous scholars []. Various traditional methods such as electrical prospecting, elastic wave testing, tomography, and ground penetrating radar [–] are employed to detect cracks in dams. However, some of these methods are expensive, and others are neither sufficiently convenient nor reliable. Recently, detecting underwater dam cracks using sonar images has become one of the most important methods because it is nondestructive, intuitive, convenient and efficient [].

Sonar data is obtained based on echo intensity when the sonar beam scans the crack area. And the echo intensity is displayed on the sonar image screen using different gray levels. The gray levels in these sonar images represent information that can accurately reflect crack depth.

However, the sonar images can not accurately reflect the crack features on the dam surface, since their echo intensities are always the same. Thus, the sonar systems used in practice always provide only low-resolution imagery []. In addition, underwater environments are complex, vary over time, and are susceptible to substantial interference [–], which leads to measurement signals being overcome by noise. Moreover, unstructured cracks are random and diverse, which makes them difficult to describe. Finally, the images obtained from sonar lack calibration, and features obtained from a sample image without manual review cannot accurately reflect the relationship between a crack in the image and an actual crack. As a result, sonar images of dam cracks are highly uncertain and fuzzy, making detection and classification difficult. Many crack detection algorithms based on imagery such as neural networks, genetic algorithms, mathematical morphology and tensor voting methods, have been proposed [–].