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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Veröffentlicht in:Control engineering practice Jg. 118; S. 104458
Hauptverfasser: Wang, Ning, Wang, Yuanyuan, Er, Meng Joo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.01.2022
Schlagworte:
Deep learning
Learning architecture
Marine object recognition
Marine vehicles
Deep learning
Marine object recognition
Marine vehicles
Learning architecture
ISSN:0967-0661, 1873-6939
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Zusammenfassung: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.
ISSN:0967-0661
1873-6939
DOI:10.1016/j.conengprac.2020.104458