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
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.
There was no specific funding for the research done