A Texture Generation Approach for Detection of Novel Surface Defects

Surface defect detection is challenging due to varying defect types and their novelties. Because of this, it is hard for algorithms to implement across datasets. Moreover, current automated optical inspection (AOI) machines cannot handle this novelty effectively. In this work, we develop a new method for surface defect detection based on generative models, which can detect novelty according to learned distributions. Experimental results on real industrial datasets show that the proposed method can successfully construct the surface texture pattern generator. By transforming the image through the generator to the corresponding latent space, the defects can be separated effectively without a tedious effort of annotation in a large amount of training data.