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Feasibility of Automated Black Ice Segmentation in Various Climate Conditions Using Deep Learning.

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Title: Feasibility of Automated Black Ice Segmentation in Various Climate Conditions Using Deep Learning.
Authors: Lee, Sang-Yum1, Jeon, Je-Sung1, Le, Tri Ho Minh2, lhmtri@ntt.edu.vn
Source: Buildings (2075-5309); Mar2023, Vol. 13 Issue 3, p767, 19p
Database: Applied Science & Technology Source
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  Data: Feasibility of Automated Black Ice Segmentation in Various Climate Conditions Using Deep Learning.
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  Data: <searchLink fieldCode="AU" term="%22Lee%2C+Sang-Yum%22">Lee, Sang-Yum</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Jeon%2C+Je-Sung%22">Jeon, Je-Sung</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Le%2C+Tri+Ho+Minh%22">Le, Tri Ho Minh</searchLink><relatesTo>2</relatesTo>, <i>lhmtri@ntt.edu.vn</i>
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  Data: <searchLink fieldCode="JN" term="%22Buildings+%282075-5309%29%22">Buildings (2075-5309)</searchLink>; Mar2023, Vol. 13 Issue 3, p767, 19p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=162748988
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      – Type: doi
        Value: 10.3390/buildings13030767
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 767
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      – TitleFull: Feasibility of Automated Black Ice Segmentation in Various Climate Conditions Using Deep Learning.
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            NameFull: Lee, Sang-Yum
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            NameFull: Jeon, Je-Sung
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            NameFull: Le, Tri Ho Minh
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              Text: Mar2023
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              Y: 2023
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