Fish abundance estimation from multi-beam sonar by improved MCNN-Reference-Cited by-同舟云学术

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Fish abundance estimation from multi-beam sonar by improved MCNN

Published:2023-03-08 Issue:4 Volume:57 Page:895-911
ISSN:1386-2588
Container-title:Aquatic Ecology
language:en
Short-container-title:Aquat Ecol

Author:

Feng Yihan,Wei Yaoguang,Sun Shuo,Liu Jincun,An Dong,Wang Jia

Funder

National Key R&D Programs of China

National Natural Science Foundation of China

Research Fund of Hebei University of Economics and Business

Publisher

Springer Science and Business Media LLC

Subject

Aquatic Science,Ecology, Evolution, Behavior and Systematics

Link

https://link.springer.com/content/pdf/10.1007/s10452-023-10007-z.pdf

Reference37 articles.

1. Bai, S, He, Z, Qiao Y, Hu H, Wu W, Yan J (2020) Adaptive dilated network with self-correction supervision for counting. In: 2020 IEEE/cvf conference on computer vision and pattern recognition (CVPR), 4593–4602. https://doi.org/10.1109/cvpr42600.2020.00465

2. Boominathan L, Kruthiventi, SSS, Babu RV (2016) CrowdNet: a deep convolutional network for dense crowd counting. In: Proceedings of the 24th ACM international conference on Multimedia, Amsterdam, The Netherlands. https://doi.org/10.1145/2964284.2967300

3. Cai K, Hu T, Cao W, Li G (2019) Classifying power quality disturbances based on phase space reconstruction and a convolutional neural network. Appl Sci Basel. https://doi.org/10.3390/app9183681

4. Chang C, Fang W, Jao R-C, Shyu C, Liao I-C (2005) Development of an intelligent feeding controller for indoor intensive culturing of eel. Aquac Eng 32(2):343–353. https://doi.org/10.1016/j.aquaeng.2004.07.004

5. Chen J, Wang Z (2021) Crowd counting with segmentation attention convolutional neural network. IET Image Proc 15(6):1221–1231. https://doi.org/10.1049/ipr2.12099

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4. Applications and analytical approaches using imaging sonar for quantifying behavioural interactions among aquatic organisms and their environment;ICES Journal of Marine Science;2023-11-30

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