Summary of Coarse-to-fine Detection Of Multiple Seams For Robotic Welding, by Pengkun Wei et al.
Coarse-to-Fine Detection of Multiple Seams for Robotic Welding
by Pengkun Wei, Shuo Cheng, Dayou Li, Ran Song, Yipeng Zhang, Wei Zhang
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed framework for multiple weld seams extraction uses both RGB images and 3D point clouds, leveraging a pre-trained deep learning model to ensure efficiency and generalization. The approach involves approximately localizing weld seams using RGB images and then achieving fine-edge extraction using region growth on the resulting region of interest from 3D point clouds. This method is tested on various workpieces with linear and curved weld seams, showcasing its potential for real-world industrial applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in autonomous welding by finding multiple weld seams quickly and accurately. The team developed a new way to use both pictures and 3D data to find the weld seams. They tested their method on different shapes and sizes of workpieces and showed that it works well for real-world applications. |
Keywords
* Artificial intelligence * Deep learning * Generalization
