Diffusion-Based Image Generation for In-Distribution Data Augmentation in Surface Defect Detection

Luigi Capogrosso, Federico Girella, Francesco Taioli, Michele Dalla Chiara, Muhammad Aqeel, Franco Fummi, Francesco Setti, Marco Cristani
Department of Engineering for Innovation Medicine, University of Verona, Italy
Paper arXiv Code
🎉 Accepted @ VISAPP 2024 🎉
In&Out teaser
Idea underlying our In&Out data augmentation approach. (Left, blue dots) The blue dots outside the bulk of negative data could be wrongly classified as anomalies (false positives), being slightly different from most of the negative data. (Right, yellow crosses) State-of-the-art per-region data augmentation methods (for example, MemSeg (Yang et al.,2023)) add positive synthetic samples in that zone, which helps in deciding what is certainly not anomalous data. (Left, red dots) On the other hand, the red dot partially outside the bulk of positive data could be, in principle, understood as a negative sample, leading to a false negative. (Right, red crosses) Diffusion-based generated data is capable of producing defects very similar to the ones in the bulk of positive data, helping the classifier not produce false negative classifications. (Right, red crosses) Diffusion-based generated data is capable of producing defects very similar to the ones in the bulk of positive data, helping the classifier not produce false negative classifications.

Abstract

In this study, we show that diffusion models can be used in industrial scenarios to improve the data augmentation procedure in the context of surface defect detection. In general, defect detection classifiers are trained on ground-truth data formed by normal samples (negative data) and samples with defects (positive data), where the latter are consistently fewer than normal samples. For these reasons, state-of-the-art data augmentation procedures add synthetic defect data by superimposing artifacts to normal samples. This leads to out-of-distribution augmented data so that the classification system learns what is not a normal sample but does not know what a defect really is. We show that diffusion models overcome this situation, providing more realistic in-distribution defects so that the model can learn the defect's genuine appearance. We propose a novel approach for data augmentation that mixes out-of-distribution with in-distribution samples, which we call In&Out. The approach can deal with two data augmentation setups: i) when no defects are available (zero-shot data augmentation) and ii) when defects are available, which can be in a small number (few-shot) or a large one (full-shot). We focus the experimental part on the most challenging benchmark in the state-ofthe-art, i.e., the Kolektor Surface-Defect Dataset 2, defining the new state-of-the-art classification AP score under weak supervision of .782.

BibTeX

@InProceedings{capogrosso2024diffusion,
  author    = {Capogrosso, Luigi and Girella, Federico and Taioli, Francesco and Chiara, Michele and Aqeel, Muhammad and Fummi, Franco and Setti, Francesco and Cristani, Marco},
  booktitle = {19th International Conference on Computer Vision Theory and Applications (VISAPP)},
  title     = {{Diffusion-Based Image Generation for In-Distribution Data Augmentation in Surface Defect Detection}},
  year      = {2024},
}