Recognition of Multiscale Dense Gel Filament-Droplet Field in Digital Holography With Mo-U-Net release_ay7f7wrrtrg4fdtwby67nlf6c4

by Zhentao Pang, Hang Zhang, Yu Wang, Letian Zhang, Yingchun Wu, Xuecheng Wu

Published in Frontiers in Physics by Frontiers Media SA.

2021  

Abstract

Accurate particle detection is a common challenge in particle field characterization with digital holography, especially for gel secondary breakup with dense complex particles and filaments of multi-scale and strong background noises. This study proposes a deep learning method called Mo-U-net which is adapted from the combination of U-net and Mobilenetv2, and demostrates its application to segment the dense filament-droplet field of gel drop. Specially, a pruning method is applied on the Mo-U-net, which cuts off about two-thirds of its deep layers to save its training time while remaining a high segmentation accuracy. The performances of the segmentation are quantitatively evaluated by three indices, the positive intersection over union (PIOU), the average square symmetric boundary distance (ASBD) and the diameter-based prediction statistics (DBPS). The experimental results show that the area prediction accuracy (PIOU) of Mo-U-net reaches 83.3<jats:italic>%</jats:italic>, which is about 5<jats:italic>%</jats:italic> higher than that of adaptive-threshold method (ATM). The boundary prediction error (ASBD) of Mo-U-net is only about one pixel-wise length, which is one third of that of ATM. And Mo-U-net also shares a coherent size distribution (DBPS) prediction of droplet diameters with the reality. These results demonstrate the high accuracy of Mo-U-net in dense filament-droplet field recognition and its capability of providing accurate statistical data in a variety of holographic particle diagnostics. Public model address: <jats:ext-link>https://github.com/Wu-Tong-Hearted/Recognition-of-multiscale-dense-gel-filament-droplet-field-in-digital-holography-with-Mo-U-net</jats:ext-link>.
In application/xml+jats format

Archived Files and Locations

application/pdf  4.5 MB
file_e4pc2g4skrd3ln3wgqqz6p5pca
fjfsdata01prod.blob.core.windows.net (publisher)
web.archive.org (webarchive)

Web Captures

https://www.frontiersin.org/articles/10.3389/fphy.2021.742296/full
2021-09-18 11:54:46 | 38 resources
webcapture_2frxi7apnjakbpe45fqmr4wjly
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2021-09-16
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  2296-424X
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 96bb6618-4170-4a86-83f9-23bd7cfe7468
API URL: JSON