Flood video segmentation on remotely sensed UAV using improved Efficient Neural Network

Naili Suri Inthizami; M. Anwar Ma'sum; Machmud R. Alhamidi; Ahmad Gamal; Ronni Ardhianto; Kurnianingsih; Wisnu Jatmiko

doi:10.1016/j.icte.2022.01.016

Flood video segmentation on remotely sensed UAV using improved Efficient Neural Network

Naili Suri Inthizami, M. Anwar Ma'sum, Machmud R. Alhamidi, Ahmad Gamal, Ronni Ardhianto, Kurnianingsih, Wisnu Jatmiko

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

Abstract

Semantic segmentation can be used to analyze the video data taken by UAV in the flood monitoring system. An accurate analysis can help rescue teams to assess and mitigate flood disasters. This paper proposed an improved Efficient Neural Network architecture to segment the UAV video of flood disaster. The proposed method consists of atrous separable convolution as the encoder and depth-wise separable convolution as the decoder. The experimental results reveal that the proposed method outperforms Efficient Neural Networks’ other architecture and gives the highest frame per second.

Original language	English
Pages (from-to)	347-351
Number of pages	5
Journal	ICT Express
Volume	8
Issue number	3
DOIs	https://doi.org/10.1016/j.icte.2022.01.016
Publication status	Accepted/In press - 2022

Keywords

Atrous separable convolution
Depth-wise separable convolution
Efficient Neural Network
Semantic segmentation

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10.1016/j.icte.2022.01.016

Cite this

@article{48f1d7d3e057431096aebc1f6ed0f666,

title = "Flood video segmentation on remotely sensed UAV using improved Efficient Neural Network",

abstract = "Semantic segmentation can be used to analyze the video data taken by UAV in the flood monitoring system. An accurate analysis can help rescue teams to assess and mitigate flood disasters. This paper proposed an improved Efficient Neural Network architecture to segment the UAV video of flood disaster. The proposed method consists of atrous separable convolution as the encoder and depth-wise separable convolution as the decoder. The experimental results reveal that the proposed method outperforms Efficient Neural Networks{\textquoteright} other architecture and gives the highest frame per second.",

keywords = "Atrous separable convolution, Depth-wise separable convolution, Efficient Neural Network, Semantic segmentation",

author = "Inthizami, {Naili Suri} and Ma'sum, {M. Anwar} and Alhamidi, {Machmud R.} and Ahmad Gamal and Ronni Ardhianto and Kurnianingsih and Wisnu Jatmiko",

note = "Funding Information: This work was supported by Indonesia National Research and Innovation Agency 2020 , No: NKB-2879/UN2.RST/HKP.05.00/2020 . Publisher Copyright: {\textcopyright} 2022 The Author(s)",

year = "2022",

doi = "10.1016/j.icte.2022.01.016",

language = "English",

volume = "8",

pages = "347--351",

journal = "ICT Express",

issn = "2405-9595",

publisher = "Korean Institute of Communications Information Sciences",

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TY - JOUR

T1 - Flood video segmentation on remotely sensed UAV using improved Efficient Neural Network

AU - Inthizami, Naili Suri

AU - Ma'sum, M. Anwar

AU - Alhamidi, Machmud R.

AU - Gamal, Ahmad

AU - Ardhianto, Ronni

AU - Kurnianingsih,

AU - Jatmiko, Wisnu

PY - 2022

Y1 - 2022

N2 - Semantic segmentation can be used to analyze the video data taken by UAV in the flood monitoring system. An accurate analysis can help rescue teams to assess and mitigate flood disasters. This paper proposed an improved Efficient Neural Network architecture to segment the UAV video of flood disaster. The proposed method consists of atrous separable convolution as the encoder and depth-wise separable convolution as the decoder. The experimental results reveal that the proposed method outperforms Efficient Neural Networks’ other architecture and gives the highest frame per second.

AB - Semantic segmentation can be used to analyze the video data taken by UAV in the flood monitoring system. An accurate analysis can help rescue teams to assess and mitigate flood disasters. This paper proposed an improved Efficient Neural Network architecture to segment the UAV video of flood disaster. The proposed method consists of atrous separable convolution as the encoder and depth-wise separable convolution as the decoder. The experimental results reveal that the proposed method outperforms Efficient Neural Networks’ other architecture and gives the highest frame per second.

KW - Atrous separable convolution

KW - Depth-wise separable convolution

KW - Efficient Neural Network

KW - Semantic segmentation

UR - http://www.scopus.com/inward/record.url?scp=85124515811&partnerID=8YFLogxK

U2 - 10.1016/j.icte.2022.01.016

DO - 10.1016/j.icte.2022.01.016

M3 - Article

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SN - 2405-9595

VL - 8

SP - 347

EP - 351

JO - ICT Express

JF - ICT Express

IS - 3

ER -

Flood video segmentation on remotely sensed UAV using improved Efficient Neural Network

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Keywords

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