## 1 Introduction
Specifically, these approaches do not significantly alter the target features, leading to a limited enhancement of the image information within the datasets, which results in inadequate generalization ability of the model. Unsupervised data augmentation can substantially increase data diversity by learning its feature distribution, transforming and expanding the original data to generate new samples. Currently, the main unsupervised data augmentation methods include reinforcement learning-based data enhancement and GAN. Reinforcement learning-based data augmentation is represented by AutoAugment, an automated data augmentation method proposed by Google (Mountain View, United States) in 2018 [21]. However, reinforcement learning is a complex training method that is slow to stabilize and converge, requiring significant computational resources and time. GAN learn the distribution of data through adversarial training between generators and discriminators. They are widely used in image generation tasks due to their ability to produce high-quality, realistic data samples [22; 23]. Given that the performance of the generator is typically weaker than that of the discriminator, [PERSON] et al. [24]. modified the generator of DCGAN by incorporating a two-branch structure, which improves the training stability of the generator network while keeping the output image's dimensional size and channel count unchanged. However, this improvement significantly increases the complexity of the model structure, affecting the training cost and efficiency. [PERSON] et al. [25] improved the diversity of images generated from rail defects by incorporating both the SAM and the CAM into DCGAN, but the quality of the generated images was low. [PERSON] et al. [26] applied DCGAN to augment the datasets of traffic signs, combining both original and generated images for training, which resulted in a detection accuracy of 92%. However, the diversity and quality of the generated images are limited. [PERSON] et al. [27] utilized CGAN to generate new samples for addressing the class imbalance issue in a corrosion datasets of oil and gas metallic pipelines. After training on the CGAN-enhanced datasets, the test accuracy of the artificial neural network model improved by 9%. However, like other GAN models, CGAN is susceptible to mode collapse issues. Despite the widespread use of GAN and their variants in data augmentation, with notable successes, there is still potential for further improvement in preventing model collapse and increasing the diversity of generated images [28]. To address these issues, an improved DCGAN is proposed to generate defect images of gas-fired polyethylene pipelines. The key improvements are as follows:  
* The Minibatch Discrimination, Self-Attention Mechanism, and Spectrum Normalization are integrated into the DCGAN model to address the limitations of the original DCGAN, such as training instability, mode collapse, and poor quality, less diverse generated samples. These enhancements also help resolve the issue of sample scarcity in machine vision. * Fine-tuning the network architecture using the Two-Timescale Update Rule to ensure stability during the early stages of model training. * A more comprehensive evaluation was conducted to compare the validity of the generated defective images of gas PE pipes. The improved DCGAN notably improves classification accuracy, particularly for small sample sizes. The rest of this paper is structured as follows. Section 2 introduces the relevant theories of the applied methods in this paper. Section 3 proposes the improved DCGAN network and specifies the algorithmic flow and the improved network structure. Section 4 demonstrates the superior performance of the improved DCGAN through a series of experiments. Finally, Section 5 provides a summary of the key findings and an outlook on future research.