Paper Info
Title
UNetGE: A U-Net-Based Software at Automatic Grain Extraction for Image Analysis of the Grain Size and Shape Characteristics
Abstract
The shape and the size of grains in sediments and soils have a significant influence on their engineering properties. Image analysis of grain shape and size has been increasingly applied in geotechnical engineering to provide a quantitative statistical description for grain morphologies. The statistic robustness and the era of big data in geotechnical engineering require the quick and efficient acquirement of large data sets of grain morphologies. In the past publications, some semi-automation algorithms in extracting grains from images may cost tens of minutes. With the rapid development of deep learning networks applied to earth sciences, we develop UNetGE software that is based on the U-Net architecture-a fully convolutional network-to recognize and segregate grains from the matrix using the electron and optical microphotographs of rock and soil thin sections or the photographs of their hand specimen and outcrops. Resultantly, it shows that UNetGE can extract approximately 300 similar to 1300 grains in a few seconds to a few minutes and provide their morphologic parameters, which will ably assist with analyses on the engineering properties of sediments and soils (e.g., permeability, strength, and expansivity) and their hydraulic characteristics.
Year
DOI
Venue
2022
10.3390/s22155565
SENSORS
Keywords
DocType
Volume
grain extraction, software, U-Net algorithm, image analysis, grain shape and size
Journal
22
Issue
ISSN
Citations 
15
1424-8220
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Ling Zeng100.34
Tianbin Li200.68
Xiekang Wang300.34
Lei Chen46239395.84
Peng Zeng500.34
Jason Scott Herrin600.34