Efficient Cutting Tool Wear Segmentation Based on Segment Anything Model
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909298780536832 |
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| author | Li, Zongshuo Huo, Ding Meurer, Markus Bergs, Thomas |
| author_facet | Li, Zongshuo Huo, Ding Meurer, Markus Bergs, Thomas |
| contents | Tool wear conditions impact the surface quality of the workpiece and its final geometric precision. In this research, we propose an efficient tool wear segmentation approach based on Segment Anything Model, which integrates U-Net as an automated prompt generator to streamline the processes of tool wear detection. Our evaluation covered three Point-of-Interest generation methods and further investigated the effects of variations in training dataset sizes and U-Net training intensities on resultant wear segmentation outcomes. The results consistently highlight our approach's advantage over U-Net, emphasizing its ability to achieve accurate wear segmentation even with limited training datasets. This feature underscores its potential applicability in industrial scenarios where datasets may be limited. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01211 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Cutting Tool Wear Segmentation Based on Segment Anything Model Li, Zongshuo Huo, Ding Meurer, Markus Bergs, Thomas Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image and Video Processing Tool wear conditions impact the surface quality of the workpiece and its final geometric precision. In this research, we propose an efficient tool wear segmentation approach based on Segment Anything Model, which integrates U-Net as an automated prompt generator to streamline the processes of tool wear detection. Our evaluation covered three Point-of-Interest generation methods and further investigated the effects of variations in training dataset sizes and U-Net training intensities on resultant wear segmentation outcomes. The results consistently highlight our approach's advantage over U-Net, emphasizing its ability to achieve accurate wear segmentation even with limited training datasets. This feature underscores its potential applicability in industrial scenarios where datasets may be limited. |
| title | Efficient Cutting Tool Wear Segmentation Based on Segment Anything Model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2407.01211 |