Summary of A Comparative Study Of Open Source Computer Vision Models For Application on Small Data: the Case Of Cfrp Tape Laying, by Thomas Fraunholz et al.
A Comparative Study of Open Source Computer Vision Models for Application on Small Data: The Case of CFRP Tape Laying
by Thomas Fraunholz, Dennis Rall, Tim Köhler, Alfons Schuster, Monika Mayer, Lars Larsen
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 In this paper, researchers explore the potential of Transfer Learning to develop Artificial Intelligence (AI) models in small data contexts, which is particularly relevant for industrial manufacturing processes like quality control of Carbon Fiber Reinforced Polymer (CFRP) tape laying. They investigate how different open-source computer vision models perform with a continuous reduction of training data and find that the amount of data required can be significantly reduced without sacrificing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI helps in industrial manufacturing, but smaller processes with limited data are a challenge. Researchers used Transfer Learning to see if AI models could work with small amounts of data. They tested computer vision models on CFRP tape laying quality control and found that less data is needed than expected, even for smaller models. |
Keywords
» Artificial intelligence » Transfer learning
