Survey on Deep Learning-Based Marine Object Detection
We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fun...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5808206 |
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| _version_ | 1849409258402086912 |
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| author | Ruolan Zhang Shaoxi Li Guanfeng Ji Xiuping Zhao Jing Li Mingyang Pan |
| author_facet | Ruolan Zhang Shaoxi Li Guanfeng Ji Xiuping Zhao Jing Li Mingyang Pan |
| author_sort | Ruolan Zhang |
| collection | DOAJ |
| description | We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed. |
| format | Article |
| id | doaj-art-7e3729edf2db49e98cf61ea551041c5e |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-7e3729edf2db49e98cf61ea551041c5e2025-08-20T03:35:33ZengWileyJournal of Advanced Transportation2042-31952021-01-01202110.1155/2021/5808206Survey on Deep Learning-Based Marine Object DetectionRuolan Zhang0Shaoxi Li1Guanfeng Ji2Xiuping Zhao3Jing Li4Mingyang Pan5Navigation CollegeNavigation CollegeNavigation CollegeNavigation CollegeNavigation CollegeNavigation CollegeWe present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.http://dx.doi.org/10.1155/2021/5808206 |
| spellingShingle | Ruolan Zhang Shaoxi Li Guanfeng Ji Xiuping Zhao Jing Li Mingyang Pan Survey on Deep Learning-Based Marine Object Detection Journal of Advanced Transportation |
| title | Survey on Deep Learning-Based Marine Object Detection |
| title_full | Survey on Deep Learning-Based Marine Object Detection |
| title_fullStr | Survey on Deep Learning-Based Marine Object Detection |
| title_full_unstemmed | Survey on Deep Learning-Based Marine Object Detection |
| title_short | Survey on Deep Learning-Based Marine Object Detection |
| title_sort | survey on deep learning based marine object detection |
| url | http://dx.doi.org/10.1155/2021/5808206 |
| work_keys_str_mv | AT ruolanzhang surveyondeeplearningbasedmarineobjectdetection AT shaoxili surveyondeeplearningbasedmarineobjectdetection AT guanfengji surveyondeeplearningbasedmarineobjectdetection AT xiupingzhao surveyondeeplearningbasedmarineobjectdetection AT jingli surveyondeeplearningbasedmarineobjectdetection AT mingyangpan surveyondeeplearningbasedmarineobjectdetection |