Few-Shot Defect Segmentation Leveraging Abundant Defect-Free Training Samples Through Normal Background Regularization And Crop-And-Paste Operation

In industrial quality assessment, it is challenging to conduct automated and accurate defect segmentation under the condition that abundant defect-free images but very limited anomalous images are available. This paper tackles the challenging few-shot defect segmentation task under such condition. We propose two regularization techniques via incorporating abundant defect-free images into the training of an encoder-decoder segmentation network. We first propose a Normal Background Regularization (NBR) loss which is jointly minimized with the segmentation loss, enhancing the encoder network to produce discriminative representations for normal regions. Secondly, we crop/paste defective regions to the randomly selected normal images for data augmentation and propose a weighted binary cross-entropy loss to enhance the training by emphasizing more realistic crop-and-pasted augmented images based on feature-level similarity comparison. Extensive experiments on MVTec AD and MTSD datasets demonstrate the superiority of the proposed method over the competing methods under few-shot settings.