Review on deep learning techniques for marine object recognition: Architectures and algorithms

• Published in: Control engineering practice Vol. 118; p. 104458
• Main Authors: Wang, Ning, Wang, Yuanyuan, Er, Meng Joo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2022
• Subjects: Deep learning; Learning architecture; Marine object recognition; Marine vehicles; Deep learning; Marine object recognition; Marine vehicles; Learning architecture
• ISSN: 0967-0661; 1873-6939
• DOI: 10.1016/j.conengprac.2020.104458

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.