|Table of Contents|

Citation:
　Jun Ye,Chengxi Li,Weisong Wen,et al.Deep Learning in Maritime Autonomous Surface Ships: Current Development and Challenges[J].Journal of Marine Science and Application,2023,(3):584-601.[doi:10.1007/s11804-023-00367-1]
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Deep Learning in Maritime Autonomous Surface Ships: Current Development and Challenges

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Title:
Deep Learning in Maritime Autonomous Surface Ships: Current Development and Challenges
Author(s):
Jun Ye1;  Chengxi Li2;  Weisong Wen3;  Ruiping Zhou1;  Vasso Reppa4
Affilations:
Author(s):
Jun Ye1;  Chengxi Li2;  Weisong Wen3;  Ruiping Zhou1;  Vasso Reppa4
1. School of Naval Architecture, Ocean, and Energy Power Engineering, Wuhan University of Technology, China;
2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong;
3. Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong;
4. Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, the Netherlands
Keywords:
Maritime autonomous surface ships; Deep learning (DL); Artificial intelligence (AI); Review
分类号:
-
DOI:
10.1007/s11804-023-00367-1
Abstract:
Autonomous surface ships have become increasingly interesting for commercial maritime sectors. Before deep learning (DL) was proposed, surface ship autonomy was mostly model-based. The development of artificial intelligence (AI) has prompted new challenges in the maritime industry. A detailed literature study and examination of DL applications in autonomous surface ships are still missing. Thus, this article reviews the current progress and applications of DL in the field of ship autonomy. The history of different DL methods and their application in autonomous surface ships is briefly outlined. Then, the previously published works studying DL methods in ship autonomy have been categorized into four groups, i.e., control systems, ship navigation, monitoring system, and transportation and logistics. The state-of-the-art of this review paper majorly lies in presenting the existing limitations and innovations of different applications. Subsequently, the current issues and challenges for DL application in autonomous surface ships are discussed. In addition, we have proposed a comparative study of traditional and DL algorithms in ship autonomy and also provided the future research scope as well.

References:

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Memo

Memo:
Received date:2023-1-31;Accepted date:2023-5-6。
Foundation item:This work was financially supported by the National Natural Science Foundation of China (Grant No. 52101388).
Corresponding author:Jun Ye,E-mail:j.ye@whut.edu.cn
Last Update: 2023-10-10