  In industrial product quality assessment, it is essential to determine
whether a product is defect-free and further analyze the severity of anomality.
To this end, accurate defect segmentation on images of products provides an
important functionality. In industrial inspection tasks, it is common to
capture abundant defect-free image samples but very limited anomalous ones.
Therefore, it is critical to develop automatic and accurate defect segmentation
systems using only a small number of annotated anomalous training images. This
paper tackles the challenging few-shot defect segmentation task with sufficient
normal (defect-free) training images but very few anomalous ones. We present
two effective regularization techniques via incorporating abundant defect-free
images into the training of a UNet-like encoder-decoder defect 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 distinctive 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. Both techniques are implemented on an
encoder-decoder segmentation network backboned by ResNet-34 for few-shot defect
segmentation. Extensive experiments are conducted on the recently released
MVTec Anomaly Detection dataset with high-resolution industrial images. Under
both 1-shot and 5-shot defect segmentation settings, the proposed method
significantly outperforms several benchmarking methods.
