Therefore, overcoming the challenge of limited defect sample size is critical for the successful implementation of object detection in defect detection applications. Figure 1: Diverse applications of aluminum foil sealing in industrial packaging. Researchers have devoted considerable efforts to addressing the issue of inadequate samples in industrial settings by developing data augmentation methods aimed at enhancing accuracy. [PERSON] et al. [6] utilized a contrastive learning model to tackle the issue of insufficient samples. [PERSON] et al. [7] applied a domain-shared convolutional neural network (CNN) to overcome the challenge of limited samples in machine fault diagnosis, particularly in cases with time-varying speed. [PERSON] et al. [8] introduced a prototype matching network model to handle cross-domain diagnosis with limited data. In a similar vein, [PERSON] et al. [9] proposed a meta-learning model for task ranking, providing a solution for fault diagnosis with a small sample size. However, many of the aforementioned studies on insufficient samples heavily depended on the data distribution or learning strategies from other fields [10]. To address data scarcity, generative adversarial networks (GANs) have emerged as a commonly used data augmentation method [11]. [PERSON] et al. [12] presented a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to augment low-dimensional fault data. [PERSON] et al. [13] developed an end-to-end generative adversarial network (PadGAN) for generating low b-value diffusion magnetic resonance imaging (dMRI) data, improving the data quality and detail of macaque brain fMRI images. [PERSON] et al. [14] introduced a GAN-based method with fused attention and perceptual quality enhancement to improve the quality and detail of solar coronal images, enhancing the observation and prediction of solar activities. [PERSON] et al. [15] proposed a super-resolution image reconstruction method using cascaded generative adversarial networks (GANs) for Sentinel-2 and Gaofen-2 images, enhancing image quality and detail. [PERSON] et al. [16] proposed a CVAEGAN method for fault diagnosis, incorporating self-modulation into the generator based on the input and feedback from the discriminator. A conditional variational autoencoder (AE) was integrated into the conditional Wasserstein GAN to generate higher-quality synthetic images. Nevertheless, GANs suffer from two major drawbacks: inconspicuous defect features and unstable training. The generator is not incentivized to learn the difference between local and global features, resulting in inconspicuous features in the synthesized images. Additionally, during training, the density ratio estimation of the discriminator is inaccurate and unstable, impeding the generator's ability to effectively learn the multimodal structure of the target distribution. Consequently, the GAN framework is inherently unstable and susceptible to loops during training. Further research and innovation are necessary to address these challenges and improve the effectiveness of data augmentation techniques in industrial fault diagnosis. To address the challenges outlined above, this study proposes a novel deep convolutional generative adversarial network with self-attention mechanism, named SAITI-DCGAN, to facilitate the efficient and high-quality generation of defect images for aluminum foil sealing. This approach addresses the issue of insufficient training samples of equipment or product defects in industrial processes, accomplishing this through the synthesis of image samples. Our approach can leverage the knowledge acquired from existing defect samples by integrating an enhanced deep convolutional generative adversarial network, self-attention mechanism learning, spectrum normalization, and data augmentation via two scale-time update rules. This synthesis results in the autonomous creation of novel defect sample data, without relying on external information from other domains.