SAITI-DCGAN: Self-Attention Based Deep Convolutional Generative Adversarial Networks for Data Augmentation of Infrared Thermal Images

Abstract

Defect detection plays a crucial role in industrial production, and the implementation of this technology has significant implications for improving both product quality and processing efficiency. However, the limited availability of defect samples for training deep-learning-based object detection models within industrial processes poses challenges for model training. In this paper, we propose a novel deep convolutional generative adversarial network with self-attention mechanism for the data augmentation of infrared thermal images for the application of aluminum foil sealing. To further expand its applicability, the proposed method is designed not only to address the specific needs of aluminum foil sealing but also to serve as a robust framework that can be adapted to a wide range of industrial defect detection tasks. To be specific, the proposed approach integrates a self-attention module into the generator, adopts spectral normalization in both the generator and discriminator, and introduces a two time-scale update rule to coordinate the training process of these components. The experimental results validated the superiority of the proposed approach in terms of the synthesized image quality and diversity. The results show that our approach can capture intricate details and distinctive features of defect images of aluminum foil sealing. Furthermore, ablation experiments demonstrated that the combination of self-attention, spectral normalization, and two time-scale update rules significantly enhanced the quality of image generation, while achieving a balance between stability and training efficiency. This innovative framework marks a notable technical breakthrough in the field of industrial defect detection and image synthesis, offering broad application prospects.