Summary of Generative Ai in Industrial Machine Vision — a Review, by Hans Aoyang Zhou et al.
Generative AI in Industrial Machine Vision – A Review
by Hans Aoyang Zhou, Dominik Wolfschläger, Constantinos Florides, Jonas Werheid, Hannes Behnen, Jan-Henrick Woltersmann, Tiago C. Pinto, Marco Kemmerling, Anas Abdelrazeq, Robert H. Schmitt
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper reviews the current state of generative AI in industrial machine vision, focusing on its applications, trends, and challenges. It discusses how generative AI can improve pattern recognition capabilities, data augmentation, image resolution, and anomaly detection for quality control. However, it also highlights the limitations and requirements for robust validation methods. The review analyzed over 1,200 papers and found that data augmentation is the primary application of generative AI in machine vision tasks such as classification and object detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine vision uses artificial intelligence to help machines understand and act on visual information. This technology can improve automation, quality control, and efficiency in industries. The paper looks at a type of AI called generative AI and how it’s being used in machine vision. It finds that generative AI is mostly being used to make data more useful for training machine learning models. This can help machines learn better and make decisions faster. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Data augmentation » Machine learning » Object detection » Pattern recognition
