2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVASR-1.6
Paper Title CAM-Guided U-Net with Adversarial Regularization for Defect Segmentation
Authors Dongyun Lin, Yiqun Li, Shitala Prasad, Tin Lay Nwe, Sheng Dong, Zaw Min Oo, Institute for Infocomm Research (I2R), Singapore
SessionMLR-APPL-IVASR-1: Machine learning for image and video analysis, synthesis, and retrieval 1
LocationArea D
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.