Underwater Image Classification Based on EfficientnetB0 and Two-Hidden-Layer Random Vector Functional Link-Reference-Cited by-同舟云学术

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Underwater Image Classification Based on EfficientnetB0 and Two-Hidden-Layer Random Vector Functional Link

Published:2024-04 Issue:2 Volume:23 Page:392-404
ISSN:1672-5182
Container-title:Journal of Ocean University of China
language:en
Short-container-title:J. Ocean Univ. China

Author:

Zhou Zhiyu,Liu Mingxuan,Ji Haodong,Wang Yaming,Zhu Zefei

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11802-024-5472-9.pdf

Reference34 articles.

1. An, Z. F., Zhang, J., and Xing, L., 2020. Inversion of oceanic parameters represented by CTD utilizing seismic multi-attributes based on convolutional neural network. Journal of Ocean University of China, 19 (6): 1283–1291.

2. Beijbom, O., Edmunds, P. J., Kline, D. I., Mitchell, B. G., and Kriegman, D., 2012. Automated annotation of coral reef survey images. 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 1170–1177.

3. Chen, Y., Zhu, J., Wan, L., Fang, X., Tong, F., and Xu, X. M., 2022. Routing failure prediction and repairing for AUV-assisted underwater acoustic sensor networks in uncertain ocean environments. Applied Acoustics, 186: 108479.

4. Duan, S., Lin, Y., Zhang, C. Y., Li, Y. H., Zhu, D., Wu, J., et al., 2022. Machine-learned, waterproof MXene fiber-based glove platform for underwater interactivities. Nano Energy, 91: 106650.

5. Durden, J. M., Hosking, B., Bett, B. J., Cline, D., and Ruhl, H. A., 2021. Automated classification of fauna in seabed photographs: The impact of training and validation dataset size, with considerations for the class imbalance. Progress in Oceanography, 196: 102612.

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