Defect Image Recognition and Classification for Eddy Current Testing of Titanium Plate Based on Convolutional Neural Network
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-12
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
In the actual production environment, the eddy current imaging inspection of titanium plate defects is prone to scan shift, scale distortion, and noise interference in varying degrees, which leads to the defect false detection and even missed inspection.
In view of this problem, a novel image recognition and classification method based on convolutional neural network (CNN) for eddy current detection of titanium plate defects is proposed.
By constructing a variety of experimental conditions and collecting defect signals, the characteristics of eddy current testing (ECT) signals for titanium plate defects are analyzed, and then the convolution structure and learning parameters are set.
The structural characteristics of local connectivity and shared weights of CNN have better feature learning and characterization capabilities for titanium plate defect images under scan shift, scale distortion, and strong noise interference.
The results prove that, compared with other deep learning and classical machine learning methods, the CNN has a higher recognition and classification accuracy for the defect eddy current image of the titanium plate in the complex detection environment.
American Psychological Association (APA)
Deng, Weiquan& Bao, Jun& Ye, Bo. 2020. Defect Image Recognition and Classification for Eddy Current Testing of Titanium Plate Based on Convolutional Neural Network. Complexity،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1145015
Modern Language Association (MLA)
Deng, Weiquan…[et al.]. Defect Image Recognition and Classification for Eddy Current Testing of Titanium Plate Based on Convolutional Neural Network. Complexity No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1145015
American Medical Association (AMA)
Deng, Weiquan& Bao, Jun& Ye, Bo. Defect Image Recognition and Classification for Eddy Current Testing of Titanium Plate Based on Convolutional Neural Network. Complexity. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1145015
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1145015