Data · dataset · 2024
The data of the article“Automatic fabrication system of optical micro-/nanofiber based on deep learning”
Listed in ScienceDB
This article utilizes image segmentation methods in computer vision to create a high-quality multi-scale micro/nanofiber dataset.
Description
The YOLOv8-FD algorithm based on small object detection improvement is used to automatically detect the diameter of micro/nanofibers. The system can achieve measurement and automated preparation for micro/nano fiber with a diameter of 462 nm − 125 μm within an error of 2.95%, and with the increase of fiber diameter, the error gradually decreases.
The optical imaging resolution of a single pixel in the system is 65.97 nm, and the average detection time is 9.6 ms. This work is suitable for high-precision real-time measurement and automatic precise preparation of micro/nano fibers. Figure 1 shows the network structure of micro/nanofiber diameter detection based on deep learning.
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Figure 2 shows the automatic preparation system of micro/nanofiber based on deep learning. Figure 3 shows the original image of Loss and mAP changing with epoch during the training process, where the opju file can be opened using Origin software. Figure 4 shows the visualization and results of the deep learning model.
Figure 5 shows the comparison of the segmentation results of the original YOLOv8 and YOLOv8-FD micro/nano fiber images. Figure 6 shows the AFM scanning image of the micro/nano fiber. The ibw file can be opened using Gwyddion.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.j00213.00021 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Computer vision and multimedia computation 72% · Image 75% · Imaging 75%
Provenance · 1 source records, 14 field assertions
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|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.j00213.00021 | 9 d ago | JSON v1 |
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