Generative AI in Industrial Machine Vision -- A Review
Hans Aoyang Zhou, Dominik Wolfschl\"ager, Constantinos Florides, Jonas, Werheid, Hannes Behnen, Jan-Henrick Woltersmann, Tiago C. Pinto, Marco, Kemmerling, Anas Abdelrazeq, Robert H. Schmitt

TL;DR
This review paper analyzes over 1,200 studies to summarize how generative AI is used in industrial machine vision, focusing on data augmentation, applications, and research challenges to guide future developments.
Contribution
It provides a comprehensive overview of recent advancements, applications, and research trends of generative AI in industrial machine vision based on extensive literature analysis.
Findings
Primary use of generative AI for data augmentation in machine vision tasks
Identification of key challenges such as data diversity and validation
Compilation of application requirements and research opportunities
Abstract
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 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 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 AI in industrial machine vision, focusing on recent advancements,…
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