Research on Water Surface Environment Perception Method Based on Visual and Positional Information Fusion

Qin Na; Zhe Zuo; Ning Xu; Zhen Yu Zhang; Yi Lu

doi:10.1007/978-981-99-7590-7_5

Research on Water Surface Environment Perception Method Based on Visual and Positional Information Fusion

Qin Na, Zhe Zuo, Ning Xu, Zhen Yu Zhang^*, Yi Lu

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The water surface environment characterised by complexity and variability, is heavily influenced by weather. To address this problem, this paper proposes a water surface environment perception network based on the fusion of visual and positional information, and proposes an encoder-decoder based semantic segmentation neural network for classifying the pixel points of the input image into three categories: water, sky and environment (obstacles).

Original language	English
Title of host publication	Advanced Computational Intelligence and Intelligent Informatics - 8th International Workshop, IWACIII 2023, Proceedings
Editors	Bin Xin, Naoyuki Kubota, Kewei Chen, Fangyan Dong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	50-62
Number of pages	13
ISBN (Print)	9789819975891
DOIs	https://doi.org/10.1007/978-981-99-7590-7_5
Publication status	Published - 2024
Event	8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023 - Beijing, China Duration: 3 Nov 2023 → 5 Nov 2023

Publication series

Name	Communications in Computer and Information Science
Volume	1931 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023
Country/Territory	China
City	Beijing
Period	3/11/23 → 5/11/23

Keywords

Attentional mechanisms
Positional information
Semantic segmentation

Access to Document

10.1007/978-981-99-7590-7_5

Cite this

Na, Q., Zuo, Z., Xu, N., Zhang, Z. Y., & Lu, Y. (2024). Research on Water Surface Environment Perception Method Based on Visual and Positional Information Fusion. In B. Xin, N. Kubota, K. Chen, & F. Dong (Eds.), Advanced Computational Intelligence and Intelligent Informatics - 8th International Workshop, IWACIII 2023, Proceedings (pp. 50-62). (Communications in Computer and Information Science; Vol. 1931 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-7590-7_5

Na, Qin ; Zuo, Zhe ; Xu, Ning et al. / Research on Water Surface Environment Perception Method Based on Visual and Positional Information Fusion. Advanced Computational Intelligence and Intelligent Informatics - 8th International Workshop, IWACIII 2023, Proceedings. editor / Bin Xin ; Naoyuki Kubota ; Kewei Chen ; Fangyan Dong. Springer Science and Business Media Deutschland GmbH, 2024. pp. 50-62 (Communications in Computer and Information Science).

@inproceedings{0b222821c8ba4653b33350a48a11f05e,

title = "Research on Water Surface Environment Perception Method Based on Visual and Positional Information Fusion",

abstract = "The water surface environment characterised by complexity and variability, is heavily influenced by weather. To address this problem, this paper proposes a water surface environment perception network based on the fusion of visual and positional information, and proposes an encoder-decoder based semantic segmentation neural network for classifying the pixel points of the input image into three categories: water, sky and environment (obstacles).",

keywords = "Attentional mechanisms, Positional information, Semantic segmentation",

author = "Qin Na and Zhe Zuo and Ning Xu and Zhang, {Zhen Yu} and Yi Lu",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023 ; Conference date: 03-11-2023 Through 05-11-2023",

year = "2024",

doi = "10.1007/978-981-99-7590-7_5",

language = "English",

isbn = "9789819975891",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "50--62",

editor = "Bin Xin and Naoyuki Kubota and Kewei Chen and Fangyan Dong",

booktitle = "Advanced Computational Intelligence and Intelligent Informatics - 8th International Workshop, IWACIII 2023, Proceedings",

address = "Germany",

}

Na, Q, Zuo, Z, Xu, N, Zhang, ZY & Lu, Y 2024, Research on Water Surface Environment Perception Method Based on Visual and Positional Information Fusion. in B Xin, N Kubota, K Chen & F Dong (eds), Advanced Computational Intelligence and Intelligent Informatics - 8th International Workshop, IWACIII 2023, Proceedings. Communications in Computer and Information Science, vol. 1931 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 50-62, 8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023, Beijing, China, 3/11/23. https://doi.org/10.1007/978-981-99-7590-7_5

Research on Water Surface Environment Perception Method Based on Visual and Positional Information Fusion. / Na, Qin; Zuo, Zhe; Xu, Ning et al.
Advanced Computational Intelligence and Intelligent Informatics - 8th International Workshop, IWACIII 2023, Proceedings. ed. / Bin Xin; Naoyuki Kubota; Kewei Chen; Fangyan Dong. Springer Science and Business Media Deutschland GmbH, 2024. p. 50-62 (Communications in Computer and Information Science; Vol. 1931 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Research on Water Surface Environment Perception Method Based on Visual and Positional Information Fusion

AU - Na, Qin

AU - Zuo, Zhe

AU - Xu, Ning

AU - Zhang, Zhen Yu

AU - Lu, Yi

PY - 2024

Y1 - 2024

N2 - The water surface environment characterised by complexity and variability, is heavily influenced by weather. To address this problem, this paper proposes a water surface environment perception network based on the fusion of visual and positional information, and proposes an encoder-decoder based semantic segmentation neural network for classifying the pixel points of the input image into three categories: water, sky and environment (obstacles).

AB - The water surface environment characterised by complexity and variability, is heavily influenced by weather. To address this problem, this paper proposes a water surface environment perception network based on the fusion of visual and positional information, and proposes an encoder-decoder based semantic segmentation neural network for classifying the pixel points of the input image into three categories: water, sky and environment (obstacles).

KW - Attentional mechanisms

KW - Positional information

KW - Semantic segmentation

UR - http://www.scopus.com/inward/record.url?scp=85176921629&partnerID=8YFLogxK

U2 - 10.1007/978-981-99-7590-7_5

DO - 10.1007/978-981-99-7590-7_5

M3 - Conference contribution

AN - SCOPUS:85176921629

SN - 9789819975891

T3 - Communications in Computer and Information Science

SP - 50

EP - 62

BT - Advanced Computational Intelligence and Intelligent Informatics - 8th International Workshop, IWACIII 2023, Proceedings

A2 - Xin, Bin

A2 - Kubota, Naoyuki

A2 - Chen, Kewei

A2 - Dong, Fangyan

PB - Springer Science and Business Media Deutschland GmbH

T2 - 8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

Y2 - 3 November 2023 through 5 November 2023

ER -

Na Q, Zuo Z, Xu N, Zhang ZY, Lu Y. Research on Water Surface Environment Perception Method Based on Visual and Positional Information Fusion. In Xin B, Kubota N, Chen K, Dong F, editors, Advanced Computational Intelligence and Intelligent Informatics - 8th International Workshop, IWACIII 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 50-62. (Communications in Computer and Information Science). doi: 10.1007/978-981-99-7590-7_5