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Open AccessArticle

An End-To-End Model for Pipe Crack Three-Dimensional Visualization Based on a Cascade Neural Network

by Xia Fang 1, Yang Wang 1, Yong Li 1, Jie Wang 1,* and Libin Zhou 2
1
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China
2
School of Computer, Data & Information Sciences, College of Letters & Science, University of Wisconsin Madison, Madison, WI 53706, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(4), 1290; https://doi.org/10.3390/app10041290
Received: 20 January 2020 / Revised: 9 February 2020 / Accepted: 10 February 2020 / Published: 14 February 2020
With the continuous progress of machine vision technology, crack detection in pipelines has been greatly improved. For crack detection in deep holes, inner tubes, and other environments, it is not only necessary to detect the existence of cracks, but also to collect important information regarding the crack detection direction for further analysis. Because shooting with a frontal field of view causes the real side wall images to produce certain distortions, the detection and calibration of cracks requires a certain amount of professional technology and time. It usually takes a long time to collect the image to eliminate the distortion, and then to identify the crack and mark the direction according to the data line. Therefore, a simple and efficient end-to-end neural network model for crack recognition and three-dimensional visualization are proposed by using a cascade network and simple recognition technology in conjunction with inertial navigation equipment. In addition, we screen the crack data via pixel calibration and eliminate the ambiguous data to make the visualization more accurate. Experiments in pipelines and burrows show that the accuracy, performance, and efficiency of the proposed method reached a high level. View Full-Text
Keywords: crack detection; cascading neural networks; distortion correction; three-dimensional visualization; end-to-end model crack detection; cascading neural networks; distortion correction; three-dimensional visualization; end-to-end model
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Fang, X.; Wang, Y.; Li, Y.; Wang, J.; Zhou, L. An End-To-End Model for Pipe Crack Three-Dimensional Visualization Based on a Cascade Neural Network. Appl. Sci. 2020, 10, 1290.

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