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|Title:||Defect-GAN : high-fidelity defect synthesis for automated defect inspection||Authors:||Zhang, Gongjie
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2021||Source:||Zhang, G., Cui, K., Hung, T.- Y., & Lu, S. (2021). Defect-GAN : high-fidelity defect synthesis for automated defect inspection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2524-2534.||Project:||DELTA-NTU CORP-SMA-RP15||metadata.dc.contributor.conference:||2021 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)||Abstract:||Automated defect inspection is critical for effective and efficient maintenance, repair, and operations in advanced manufacturing. On the other hand, automated defect inspection is often constrained by the lack of defect samples, especially when we adopt deep neural networks for this task. This paper presents Defect-GAN, an automated defect synthesis network that generates realistic and diverse defect samples for training accurate and robust defect inspection networks. Defect-GAN learns through defacement and restoration processes, where the defacement generates defects on normal surface images while the restoration removes defects to generate normal images. It employs a novel compositional layer-based architecture for generating realistic defects within various image backgrounds with different textures and appearances. It can also mimic the stochastic variations of defects and offer flexible control over the locations and categories of the generated defects within the image background. Extensive experiments show that Defect-GAN is capable of synthesizing various defects with superior diversity and fidelity. In addition, the synthesized defect samples demonstrate their effectiveness in training better defect inspection networks.||URI:||https://hdl.handle.net/10356/146285||Schools:||School of Electrical and Electronic Engineering||Organisations:||Delta Research Center, Singapore||Research Centres:||Delta-NTU Corporate Laboratory||Rights:||© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Conference Papers|
Updated on Oct 3, 2023
Updated on Oct 3, 2023
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