A data generation method with dual discriminators and regularization for surface defect detection under limited data

Abstract

Surface defect detection is a critical aspect of industrial production as it directly impacts product quality. However, advancements in manufacturing have made it challenging to collect surface defect images, which often limits the performance of deep learning-based defect detection models. Although generative adversarial network (GAN)-based image augmentation methods can help alleviate the problem of insufficient data, they are not effectively used when there is limited defect data. To address these challenges, our paper proposes a data generation method that can generate surface defect images under limited data with stability. Firstly, we propose a dual discriminator architecture based on GAN to facilitate the deployment of the regularization scheme. Next, we introduce a regularization scheme into the dual discriminator, which enhances the quality and diversity of the defect images generated by making it difficult for the discriminator to overfit under limited data. Finally, we introduce an overfitting heuristic that can adaptively adjust the strength of the regularization algorithm based on the degree of overfitting. The experimental results demonstrate that the defect images generated by our proposed data generation method have high quality and rich diversity, significantly improving the performance of defect detection models in various scenarios. For instance, the generated defect images increased the mean average precision at the intersection over union threshold of 0.5 (mAP@.5) value of the defect detection model from 82.26 to 97.59 on the Northeastern University (NEU) dataset. Additionally, we propose a new metric for measuring the quality of generated defect images and construct a surface defect dataset of solar aluminum profile frames collected in a real industrial scene to accelerate the development of defect detection technology.