Review on deep learning techniques for marine object recognition: Architectures and algorithms
Due to the rapid development of deep learning techniques, numerous frameworks including convolutional neural networks (CNNs), deep belief networks (DBNs) and auto-encoder (AE), etc., have been established. In this context, advances in marine object recognition have been dramatically boosted, especia...
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| Published in | Control engineering practice Vol. 118; p. 104458 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.01.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0967-0661 1873-6939 |
| DOI | 10.1016/j.conengprac.2020.104458 |
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| Abstract | Due to the rapid development of deep learning techniques, numerous frameworks including convolutional neural networks (CNNs), deep belief networks (DBNs) and auto-encoder (AE), etc., have been established. In this context, advances in marine object recognition have been dramatically boosted, especially in the past decade. In this paper, we exclusively focus on an intensive review on deep-learning-based object recognition for both surface and underwater targets. To facilitate a comprehensive review, key concepts and typical architectures are firstly summarized in a unified framework. Accordingly, popular/benchmark datasets for marine object recognition are thoroughly collected and deep learning methodologies are comprehensively analyzed with intensive comparisons. Moreover, experimental results and futuristic trends in marine object recognition are intensively discussed. Finally, conclusions on state-of-the-art marine object recognition using deep learning techniques are drawn. |
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| AbstractList | Due to the rapid development of deep learning techniques, numerous frameworks including convolutional neural networks (CNNs), deep belief networks (DBNs) and auto-encoder (AE), etc., have been established. In this context, advances in marine object recognition have been dramatically boosted, especially in the past decade. In this paper, we exclusively focus on an intensive review on deep-learning-based object recognition for both surface and underwater targets. To facilitate a comprehensive review, key concepts and typical architectures are firstly summarized in a unified framework. Accordingly, popular/benchmark datasets for marine object recognition are thoroughly collected and deep learning methodologies are comprehensively analyzed with intensive comparisons. Moreover, experimental results and futuristic trends in marine object recognition are intensively discussed. Finally, conclusions on state-of-the-art marine object recognition using deep learning techniques are drawn. |
| ArticleNumber | 104458 |
| Author | Wang, Yuanyuan Wang, Ning Er, Meng Joo |
| Author_xml | – sequence: 1 givenname: Ning surname: Wang fullname: Wang, Ning email: n.wang@ieee.org – sequence: 2 givenname: Yuanyuan surname: Wang fullname: Wang, Yuanyuan – sequence: 3 givenname: Meng Joo surname: Er fullname: Er, Meng Joo |
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