A review of intelligent ship marine object detection based on RGB camera
| Title: | A review of intelligent ship marine object detection based on RGB camera |
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| Authors: | Defu Yang, Mahmud Iwan Solihin, Yawen Zhao, Benchun Yao, Chaoran Chen, Bingyu Cai, Affiani Machmudah |
| Source: | IET Image Processing, Vol 18, Iss 2, Pp 281-297 (2024) |
| Publisher Information: | Wiley, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Computer software |
| Subject Terms: | intelligent transportation systems, learning (artificial intelligence), marine navigation, object detection, Photography, TR1-1050, Computer software, QA76.75-76.765 |
| More Details: | Abstract The article presents a comprehensive summary of Intelligent Ship Marine Object Detection (ISMOD) based on the RGB Camera. Marine object detection plays a pivotal role in enabling intelligent ships to acquire crucial data and security assurances for autonomous navigation. Among the various detection sensors, the RGB Camera is an informative and cost‐effective tool with a wide range of civil applications. In the beginning, the ISMOD metrics based on the RGB camera is analyzed from three significant aspects, namely accuracy, speed, and robustness. Subsequently, the latest research status and comparative overview are presented, encompassing three mainstream detection methods: traditional detection, deep learning detection, and sensor fusion detection. Finally, the existing challenges of ISMOD are discussed and future development trends are recommended. The results demonstrate that forthcoming development will predominantly concentrate on deep learning approaches, complemented by other techniques. It is imperative to advance detection performance in domains such as deep fusion, multi‐feature extraction, multi‐fusion technology, and lightweight detection architecture. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1751-9667 1751-9659 |
| Relation: | https://doaj.org/toc/1751-9659; https://doaj.org/toc/1751-9667 |
| DOI: | 10.1049/ipr2.12959 |
| Access URL: | https://doaj.org/article/50713572588c47f6bde1ff2b9b6ae765 |
| Accession Number: | edsdoj.50713572588c47f6bde1ff2b9b6ae765 |
| Database: | Directory of Open Access Journals |
| ISSN: | 17519667 17519659 |
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| DOI: | 10.1049/ipr2.12959 |
| Published in: | IET Image Processing |
| Language: | English |