Image-based Agarwood Resinous Area Segmentation using Deep Learning
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917633278869504 |
|---|---|
| author | Hipiny, Irwandi Abdullah, Johari Bolhassan, Noor Alamshah |
| author_facet | Hipiny, Irwandi Abdullah, Johari Bolhassan, Noor Alamshah |
| contents | The manual extraction method of Agarwood resinous compound is laborious work, requires skilled workers, and is subject to human errors. Commercial Agarwood industries have been actively exploring using Computer Numerical Control (CNC) machines to replace human effort for this particular task. The CNC machine accepts a G-code script produced from a binary image in which the wood region that needs to be chiselled off is marked with (0, 0, 0) as its RGB value. Rather than requiring a human expert to perform the region marking, we propose using a Deep learning image segmentation method instead. Our setup involves a camera that captures the cross-section image and then passes the image file to a computer. The computer performs the automated image segmentation and feeds the CNC machine with a G-code script. In this article, we report the initial segmentation results achieved using a state-of-the-art Deep learning segmentation method and discuss potential improvements to refine the segmentation accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05129 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Image-based Agarwood Resinous Area Segmentation using Deep Learning Hipiny, Irwandi Abdullah, Johari Bolhassan, Noor Alamshah Computer Vision and Pattern Recognition The manual extraction method of Agarwood resinous compound is laborious work, requires skilled workers, and is subject to human errors. Commercial Agarwood industries have been actively exploring using Computer Numerical Control (CNC) machines to replace human effort for this particular task. The CNC machine accepts a G-code script produced from a binary image in which the wood region that needs to be chiselled off is marked with (0, 0, 0) as its RGB value. Rather than requiring a human expert to perform the region marking, we propose using a Deep learning image segmentation method instead. Our setup involves a camera that captures the cross-section image and then passes the image file to a computer. The computer performs the automated image segmentation and feeds the CNC machine with a G-code script. In this article, we report the initial segmentation results achieved using a state-of-the-art Deep learning segmentation method and discuss potential improvements to refine the segmentation accuracy. |
| title | Image-based Agarwood Resinous Area Segmentation using Deep Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.05129 |