# Welding Image Data Augmentation Method Based on LRGAN Model
## 1 Introduction  
Welding, as a core process in modern industrial manufacturing, plays a crucial role in the accuracy and reliability of quality inspection, which directly impacts product performance, production efficiency, and economic benefits [1]. In recent years, deep learning (DL) technology has demonstrated significant application potential in the field of quality inspection for complex welding components, owing to its powerful features including self-learning capabilities, complex pattern recognition performance, and excellent generalization characteristics [2]. However, the performance of deep learning models is highly dependent on large-scale, high-quality training data. In industrial welding scenarios, there is a common challenge in data acquisition: on one hand, the collection of welding samples faces significant challenges due to harsh environmental factors such as strong arc light interference, high temperatures, and high humidity; on the other hand, the distribution of welding defect samples is imbalanced (e.g., there is a scarcity of defect samples like porosity, cracks, etc. ), making it difficult to meet the deep learning models' need for data diversity and representativeness. The aforementioned data bottleneck severely restricts the accuracy and robustness of welding quality detection models, impacting their detection precision [3]. Therefore, overcoming the scarcity of welding data in deep learning models and expanding the original dataset to provide a solid data foundation for the subsequentconstruction of high-performance welding quality detection models has become a key issue that urgently needs to be addressed in the current field of welding automation. Currently, the main approach to solving the issue of insufficient welding samples is based on generative adversarial networks (GANs) [4]. Although GANs have been widely applied in fields such as medicine [5] and computer vision [6], they still face several challenges due to the adversarial mechanism and the limitations of the cross-entropy loss function. These challenges include model collapse, gradient explosion, slow convergence speed, and gradient disappearance [7; 8; 9]. In reference [7], [PERSON] et al. found that in the classification problem, the mean square error loss function suffers from gradient disappearance, resulting in a low learning rate in the early stage of model training. Therefore, it is necessary to improve the cross-entropy loss function to avoid the problem of gradient disappearance. In reference [8], [PERSON] et al. found that one of the reasons why the original GAN is prone to model collapse during training is that the input is random noise without limit. In reference [9], [PERSON] et al. found that during the training process of standard GANs, the discriminator converges faster than the generator, often reaching a convergence point quickly. At this point, it becomes difficult for the discriminator to continue guiding the generator correctly. Compared to the discriminator model, the generator model typically has a deeper network structure. Due to the lack of effective guidance, the generator often experiences gradient disappearance. To address the aforementioned issues and improve the image generation quality and training efficiency of the GAN across different industrial datasets, researchers have systematically improved GANs in two key dimensions: loss function optimization and network architecture innovation. By introducing a conditional constraint mechanism, the conditional generative adversarial network (CGAN) was proposed, enabling the directed generation of images belonging to specific categories [10]. StarGAN (star generative adversarial network), developed based on the concept of domain adaptation, significantly enhances the capability of cross-domain image generation [11].