Summary of Segmentation and Characterization Of Macerated Fibers and Vessels Using Deep Learning, by Saqib Qamar et al.
Segmentation and Characterization of Macerated Fibers and Vessels Using Deep Learning
by Saqib Qamar, Abu Imran Baba, Stéphane Verger, Magnus Andersson
First submitted to arxiv on: 30 Jan 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 proposed research develops an automatic deep learning approach for segmenting and characterizing macerated fiber and vessel form in microscopy images. The one-stage YOLOv8 model is utilized, which enables fast and accurate segmentation, achieving a mean Average Precision (mAP) of 78%. This method can analyze large-scale images, demonstrating effective cell detection and segmentation. The approach’s robustness is assessed using genetically modified tree lines, with comparable results to manual measurements. A user-friendly web application for image analysis is also provided, along with the code on Google Colab. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to study wood cells using computer vision. Scientists typically use microscopes to look at wood samples, but this can be time-consuming and difficult. The team developed a special AI model called YOLOv8 that can quickly and accurately identify different types of wood cells in images. This helps scientists understand how wood properties change based on the arrangement of these cells. The researchers also created an easy-to-use online tool to help others analyze their own wood samples. |
Keywords
* Artificial intelligence * Deep learning * Mean average precision
