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Nondestructive defect detection in castings by using spatial attention bilinear convolutional neural network
journal contribution
posted on 2023-06-09, 23:08 authored by Zhenhui Tang, Engang Tian, Yongxiong Wang, Licheng Wang, Tai YangX-ray images of castings are widely used in manufacturing for quality assurance. This article investigates the X-ray-image-based defective detection. The main contributions in this article are twofold: first, a new full-image method is proposed to classify defective castings and nondefective ones; and second, by combining two technologies, spatial attention mechanism and bilinear pooling used in deep convolutional neural networks (CNNs), a new spatial attention bilinear CNN is proposed to enhance the representation power of CNN. To validate the above initiatives, extensive experimental studies have been carried out to show the advantages of the new method over a number of existing ones.
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Publication status
- Published
File Version
- Accepted version
Journal
IEEE Transactions on Industrial InformaticsISSN
1551-3203Publisher
Institute of Electrical and Electronics EngineersExternal DOI
Issue
1Volume
17Page range
82-89Department affiliated with
- Engineering and Design Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2021-02-23First Open Access (FOA) Date
2021-03-30First Compliant Deposit (FCD) Date
2021-03-30Usage metrics
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