Scarcity-GAN: Scarce data augmentation for defect detection via generative adversarial nets

Data augmentation is a crucial and challenging task for improving defect detection with limited data. Many generative models have been proposed and shown promising performance on this task. However, existing models are unable to capture the fine features of defects when training data is scarce, resulting in the inability to synthesize defects and a lack of diversity in the synthesized defects. Additionally, most models do not consider the location of synthesized defects in the image, thus limiting the ability for augmenting defect data through data generation. In this paper, we propose a new augmentation model named Scarce Data Augmentation Generative Adversarial Nets (Scarcity-GAN) to address the scarce data augmentation problem. Firstly, we design a new clustering module which selects data containing similar features to the target defect from extra datasets, in order to help the GAN learn the features of the target defect. Secondly, we modify the vanilla generator with an Encoder-Decoder model. The generator takes two inputs: one is the defect-free images, which are encoded by the Encoder to obtain defect-free features, and the other is the extra defect feature maps in the target defect set after clustering. Next, we design a Fusion Patch-Embedding module to merge the two different features, ensuring that the synthesized defects are located on the object accurately. We also design a new loss function for the generator, and then prove that it makes our model get converged. Last, we conduct extensive experiments to demonstrate the significant performance improvement and generalizability of Scarcity-GAN on two scarce datasets: industrial O-ring and Metal Iron Sheet datasets; and one general dataset: the public CODEBRIM dataset. The experimental results show that our Scarcity-GAN outperforms the SOTA augmentation models on different scarce datasets.