Machine vision enhances automation, quality control, and operational efficiency in industrial applications by enabling machines to interpret and act on visual data. While traditional computer vision algorithms and approaches remain widely utilized, machine learning has become pivotal in current research activities. In particular, generative \gls*{AI} demonstrates promising potential by improving pattern recognition capabilities, through data augmentation, increasing image resolution, and identifying anomalies for quality control. However, the application of generative \gls*{AI} in machine vision is still in its early stages due to challenges in data diversity, computational requirements, and the necessity for robust validation methods. A comprehensive literature review is essential to understand the current state of generative \gls*{AI} in industrial machine vision, focusing on recent advancements, applications, and research trends. Thus, a literature review based on the PRISMA guidelines was conducted, analyzing over 1,200 papers on generative \gls*{AI} in industrial machine vision. Our findings reveal various patterns in current research, with the primary use of generative \gls*{AI} being data augmentation, for machine vision tasks such as classification and object detection. Furthermore, we gather a collection of application challenges together with data requirements to enable a successful application of generative \gls*{AI} in industrial machine vision. This overview aims to provide researchers with insights into the different areas and applications within current research, highlighting significant advancements and identifying opportunities for future work.

翻译：机器视觉通过使机器能够解读视觉数据并据此采取行动，在工业应用中提升了自动化水平、质量控制能力和运营效率。尽管传统的计算机视觉算法与方法仍被广泛使用，机器学习已成为当前研究活动的关键所在。特别是生成式人工智能展现出巨大潜力，它通过数据增强、提高图像分辨率以及识别质量控制中的异常等方式，提升了模式识别能力。然而，由于数据多样性不足、计算需求高以及需要稳健的验证方法等挑战，生成式人工智能在机器视觉中的应用仍处于早期阶段。为了解生成式人工智能在工业机器视觉中的现状，聚焦最新进展、应用及研究趋势，进行全面的文献综述至关重要。为此，我们依据PRISMA指南开展了文献综述，分析了超过1,200篇关于生成式人工智能在工业机器视觉中应用的论文。我们的研究揭示了当前研究中的多种模式，其中生成式人工智能主要用于数据增强，以支持分类和目标检测等机器视觉任务。此外，我们汇总了一系列应用挑战及数据需求，以促进生成式人工智能在工业机器视觉中的成功应用。本综述旨在为研究人员提供当前研究各领域及应用方向的深入见解，突出重要进展并指明未来工作的机遇。