Summary of Investigation Of the Impact Of Synthetic Training Data in the Industrial Application Of Terminal Strip Object Detection, by Nico Baumgart et al.
Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection
by Nico Baumgart, Markus Lange-Hegermann, Mike Mücke
First submitted to arxiv on: 6 Mar 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 The paper introduces a novel method for training object detectors on complex industrial applications like terminal strip detection, which currently relies on manual or classical image processing methods. The authors address the challenge of gathering sufficient labeled data by developing an image synthesis pipeline that combines domain randomization and domain knowledge. They generate synthetic training data from 3D models and annotate them automatically to bridge the sim-to-real domain gap. The results show that under optimized scaling conditions, the sim-to-real performance difference in mean average precision amounts to 2.69% for RetinaNet and 0.98% for Faster R-CNN, making this approach suitable for industrial requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using special computer models to help machines detect objects on assembly lines. Right now, people are doing this job by hand or using old computer methods. But the problem is that it’s hard to get enough pictures of what they’re looking for and label them correctly. To solve this, the researchers created a way to make fake pictures from 3D models and automatically label them. They tested this on finding terminal strips (small metal pieces) and found that with some adjustments, their method was almost as good as using real pictures. |
Keywords
* Artificial intelligence * Cnn * Image synthesis * Mean average precision
