Cam-Guided U-Net With Adversarial Regularization For Defect Segmentation

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Cam-Guided U-Net With Adversarial Regularization For Defect Segmentation
Title:
Cam-Guided U-Net With Adversarial Regularization For Defect Segmentation
Journal Title:
2021 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
23 August 2021
Citation:
Lin, D., Li, Y., Prasad, S., Nwe, T. L., Dong, S., & Oo, Z. M. (2021). Cam-Guided U-Net With Adversarial Regularization For Defect Segmentation. 2021 IEEE International Conference on Image Processing (ICIP). doi:10.1109/icip42928.2021.9506582
Abstract:
Defect segmentation is critical in real-wold industrial product quality assessment. There are usually a huge number of normal (defect-free) images but a very limited number of annotated anomalous images. This poses huge challenges to exploiting Fully-Convolutional Networks (FCN), e.g., UNet, as they require sufficient anomalous images with defect annotations during training. To further leverage the information from normal data, a novel CAM-guided U-Net with adversarial regularization (CAM-UNet-AR) is proposed. We first modify the existing CAM-UNet to incorporate the CAMs for both normal and anomalous classes and fine-tune the segmentation network using a combined loss which jointly considers pixel-wise classification, foreground segmentation and boundary segmentation. Secondly, an auxiliary adversarial regularization module (ARM) is proposed to facilitate the segmentation network to encode the ``normal components'' from training images into consistent representations. Extensive experiments on MVTec AD dataset show the superiority of our proposed network over multiple state-of-the-art U-Net variants.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 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.
ISSN:
2381-8549
1522-4880
ISBN:
978-1-6654-4115-5
978-1-6654-3102-6
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