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
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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 -