The manufacturing industry is always exploring techniques to optimize processes, increase product quality, and more accurately identify defects. The technique of deep learning is the strategy that will be used to handle the issues presented. However, the challenge of using AI in this domain is the small and imbalanced dataset for training affected by the severe shortage of defective data. Moreover, data acquisition requires significant labor, time, and resources. In response to these demands, this research presents an intelligent internet of things (IoT) framework enriched by generative adversarial network (GAN). This framework was developed in response to the needs outlined above. The framework applies the IoT for real-time data collection and communication, while GAN are utilized to synthesize high-fidelity images of manufacturing defects. The quality of the GAN-synthesized image is quantified by the average FID score of 8.312 for non-defective images and 7.459 for defective images. As evidenced by the similarity between the distributions of synthetic and real images, the proposed generative model can generate visually authentic and high-fidelity images. As demonstrated by the results of the defect detection experiments, the accuracy can be enhanced to the maximum of 96.5% by integrating the GAN-synthesized images with the real images. Concurrently, this integration reduces the occurrence of false alarms.

中文翻译：

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具有 GAN 合成图像的智能物联网框架，用于增强制造中的缺陷检测

制造业一直在探索优化流程、提高产品质量以及更准确地识别缺陷的技术。深度学习技术是将用于处理所提出问题的策略。然而，在该领域使用人工智能的挑战是，由于缺陷数据严重短缺，训练数据集小且不平衡。此外，数据采集需要大量的劳动力、时间和资源。为了满足这些需求，本研究提出了一种由生成对抗网络（GAN）丰富的智能物联网（IoT）框架。该框架是为了满足上述需求而开发的。该框架应用物联网进行实时数据收集和通信，同时利用生成对抗网络来合成制造缺陷的高保真图像。 GAN 合成图像的质量通过无缺陷图像的平均 FID 分数 8.312 和有缺陷图像的平均 FID 分数 7.459 来量化。正如合成图像和真实图像分布之间的相似性所证明的那样，所提出的生成模型可以生成视觉上真实且高保真的图像。缺陷检测实验的结果表明，通过将 GAN 合成的图像与真实图像相结合，准确率最高可以提高到 96.5%。同时，这种集成减少了误报的发生。