An End-To-End Model for Pipe Crack Three-Dimensional Visualization Based on a Cascade Neural Network
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 t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/4/1290 |
| _version_ | 1818920586151198720 |
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| author | Xia Fang Yang Wang Yong Li Jie Wang Libin Zhou |
| author_facet | Xia Fang Yang Wang Yong Li Jie Wang Libin Zhou |
| author_sort | Xia Fang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T01:24:06Z |
| format | Article |
| id | doaj.art-ed57e0382f15449980421b4c4cb294e2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-20T01:24:06Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ed57e0382f15449980421b4c4cb294e22022-12-21T19:58:17ZengMDPI AGApplied Sciences2076-34172020-02-01104129010.3390/app10041290app10041290An End-To-End Model for Pipe Crack Three-Dimensional Visualization Based on a Cascade Neural NetworkXia Fang0Yang Wang1Yong Li2Jie Wang3Libin Zhou4School of Mechanical Engineering, Sichuan University, Chengdu 610065, ChinaSchool of Mechanical Engineering, Sichuan University, Chengdu 610065, ChinaSchool of Mechanical Engineering, Sichuan University, Chengdu 610065, ChinaSchool of Mechanical Engineering, Sichuan University, Chengdu 610065, ChinaSchool of Computer, Data & Information Sciences, College of Letters & Science, University of Wisconsin Madison, Madison, WI 53706, USAWith 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.https://www.mdpi.com/2076-3417/10/4/1290crack detectioncascading neural networksdistortion correctionthree-dimensional visualizationend-to-end model |
| spellingShingle | Xia Fang Yang Wang Yong Li Jie Wang Libin Zhou An End-To-End Model for Pipe Crack Three-Dimensional Visualization Based on a Cascade Neural Network Applied Sciences crack detection cascading neural networks distortion correction three-dimensional visualization end-to-end model |
| title | An End-To-End Model for Pipe Crack Three-Dimensional Visualization Based on a Cascade Neural Network |
| title_full | An End-To-End Model for Pipe Crack Three-Dimensional Visualization Based on a Cascade Neural Network |
| title_fullStr | An End-To-End Model for Pipe Crack Three-Dimensional Visualization Based on a Cascade Neural Network |
| title_full_unstemmed | An End-To-End Model for Pipe Crack Three-Dimensional Visualization Based on a Cascade Neural Network |
| title_short | An End-To-End Model for Pipe Crack Three-Dimensional Visualization Based on a Cascade Neural Network |
| title_sort | end to end model for pipe crack three dimensional visualization based on a cascade neural network |
| topic | crack detection cascading neural networks distortion correction three-dimensional visualization end-to-end model |
| url | https://www.mdpi.com/2076-3417/10/4/1290 |
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