DLS-GAN: Generative Adversarial Nets for Defect Location Sensitive Data Augmentation

Limited data usually cause deep neural networks to hold poor performance after training, and many generative models are proposed to synthesize data to improve the performance of models. However, existing models ignore capturing the small defect details (e.g., features and locations), resulting in that most models cannot augment the Defect Location Sensitive Data (DLS data) in which the ratio of object size to the image size is small (e.g., 20%) and the locations of the defects are only on the object. In this paper, we propose a new augmentation model, named Defect Location Sensitive data augmentation GAN (DLS-GAN), to address DLS data augmentation problem. First, we modify the vanilla generator with two Encoder-Decoder models, and view the limited masked-images masked by labeling the defect-free pixels while remaining the defect pixels in defect images and many defect-free images as the input of the two models. The extracted feature map from the first Encoder-Decoder model provides the defect features and location information; the second one extracts the features of defect-free images, and integrates the two different features with a designed Defect Feature Transfer Module to synthesize images with desired defects. Second, we employ two discriminators to estimate the scores of both distribution matching degree and defect similarity between real data and generated ones. With the two modifications, we design a new loss function, and then prove that it makes our model get converged. Last, we conduct extensive experiments to demonstrate the significant performance improvement and generalizability of DLS-GAN on different types of DLS datasets. The experimental results show that our DLS-GAN outperforms the SOTA generative models in terms of synthesizing high quality images with desired defects. Note to Practitioners —Automated defect image detectors play an important role in the field of automated manufacturing. Training a detector with superior detection performance usually requires a large number of samples. However, it is difficult to collect many defect samples in practice. Although existing generative methods can synthesize realistic-like images, they cannot generate the Defect Location Sensitive Data (DLS Data) which refer to the samples that the defects appear at the specific locations in product objects, resulting in the synthesized images invalid. This paper proposes a new defect image generation model called DLS-GAN to address this problem, and validates its performance in different real-world industrial datasets ranging from DLS Data to Non-DLS Data. Such generated images can be adopted as useful resources for improving the detection performance of automated detector.