Open Access
Issue |
JNWPU
Volume 40, Number 4, August 2022
|
|
---|---|---|
Page(s) | 739 - 745 | |
DOI | https://doi.org/10.1051/jnwpu/20224040739 | |
Published online | 30 September 2022 |
- LOPERA Tellez. Underwater threat recognition: are automatic target classification algorithms going to replace expert human operators in the near future?[C]//OCEANS 2019-Marseille, Marseille, France, 2019: 1-4 [Google Scholar]
- KANG Chunyu, XIA Zhijun. Tensor feature extraction of underwater passive sonar target based on auditory model[J]. Acta Acustica, 2020, 45(6): 824–829 [Article] (in Chinese) [Google Scholar]
- HINTON G E, SAlAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504–507 [Article] [NASA ADS] [CrossRef] [Google Scholar]
- WILLIAMS D. Underwater target classification in synthetic aperture sonar imagery using deep convolutional neural networks[C]//International Conference on Pattern Recognition, 2017: 2497-2502 [Google Scholar]
- JI Wentao. Sonar image recognition method based on deep transfer learning[D]. Dalian: Dalian University of Technology, 2021 (in Chinese) [Google Scholar]
- WEI Z, YANG J, MIN S. A method of underwater acoustic signal classification based on deep neural network[C]//2018 5th International Conference on Information Science and Control Engineering, Zhengzhou, China, 2019: 46-50 [Google Scholar]
- WANG Lei, CHEN Yuechao, WANG Qincui, et al. Research on underwater active target echo image classification method based on convolution residual network[J]. Acoustics and Electronic Engineering, 2021(1): 1–4 [Article] (in Chinese) [Google Scholar]
- GV A, SUS A, VS A, et al. CNN algorithm for plant classification in deep learning[J]. Materials Today: Proceedings, 2021, 46(9): 3684–3689 [CrossRef] [Google Scholar]
- AKAY Metin, DU Yong, SERSHEN C L, et al. Deep learning classification of systemic sclerosis skin using the mobilenetV2 model[J]. IEEE Open Journal of Engineering in Medicine and Biology, 2021, 2: 104–110 [Article] [CrossRef] [Google Scholar]
- JIN Leilei, LIANG Hong, YANG Changsheng. Sonar image recognition of underwater target based on convolutional neural network[J]. Journal of Northwestern Polytechnical University, 2021, 39(2): 285–291 (in Chinese) [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- LI Z, QI B, LI C. 3D sonar image reconstruction based on multilayered mesh search and triangular connection[C]//2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2018: 60-63 [Google Scholar]
- FEIFEI L, FERGUS R, PERONA P. One-shot learning of object categories[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2006, 28(4): 594–561 [Article] [CrossRef] [Google Scholar]
- GONG Wenjing, TIAN Jie, LI Baoqi, et al. Acoustic-optical image fusion underwater target classification method based on improved MobilenetV2[J]. Journal of Applied Acoustics, 2021, 41(3): 462–470 [Article] (in Chinese) [Google Scholar]
- SHENG Ziqi, HUO Guanying. Detection of underwater mine target in sidescan sonar image based on sample simulation and transfer learning[J]. CAAI Transactions on Intelligent Systems, 2021, 16(2): 385–392 [Article] (in Chinese) [Google Scholar]
- HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016: 770-778 [Google Scholar]
- RAFAEL Muller, SIMON Kornblith, GEOFFREY E Hinton. When does label smoothing help[C]//Advances in Neural Information Processing Systems, 2019: 4696-4705 [Google Scholar]
- NIU S, LIU Y, WANG J, et al. A decade survey of transfer learning(2010-2020)[J]. IEEE Trans on Artificial Intelligence, 2020, 1(2): 151–166 [Article] [CrossRef] [Google Scholar]
- TAN C, SUN F, KONG T, et al. A survey on deep transfer learning[C]//International Conference on Artificial Neural Networks and Machine Learning, 2018: 270-279 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.