Generative AI in Industrial Machine Vision -- A Review
<|reference_start|>Generative AI in Industrial Machine Vision -- A Review: 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, applications, and research trends. Thus, a literature review based on the PRISMA guidelines was conducted, analyzing over 1,200 papers on generative AI in industrial machine vision. Our findings reveal various patterns in current research, with the primary use of generative 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 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.<|reference_end|>
@article{zhou2024generative,
    title={Generative AI in Industrial Machine Vision -- A Review},
    author={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},
    journal={arXiv preprint arXiv:2408.10775},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10775},
    primaryClass={cs.CV cs.LG eess.IV}
}