Multilabel Video Classification Model of Navigation Mark’s Lights Based on Deep Learning Article Swipe
Xu Han
,
Mingyang Pan
,
Haipeng Ge
,
Shaoxi Li
,
Jingfeng Hu
,
Lining Zhao
,
Yu Li
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/6794202
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/6794202
At night, buoys and other navigation marks disappear to be replaced by fixed or flashing lights. Navigation marks are seen as a set of lights in various colors rather than their familiar outline. Deciphering that the meaning of the lights is a burden to navigators, it is also a new challenging research direction of intelligent sensing of navigation environment. The study studied initiatively the intelligent recognition of lights on navigation marks at night based on multilabel video classification methods. To capture effectively the characteristics of navigation mark’s lights, including both color and flashing phase, three different multilabel classification models based on binary relevance, label power set, and adapted algorithm were investigated and compared. According to the experiment’s results performed on a data set with 8000 minutes video, the model based on binary relevance, named NMLNet, has highest accuracy about 99.23% to classify 9 types of navigation mark’s lights. It also has the fastest computation speed with least network parameters. In the NMLNet, there are two branches for the classifications of color and flashing, respectively, and for the flashing classification, an improved MobileNet‐v2 was used to capture the brightness characteristic of lights in each video frame, and an LSTM is used to capture the temporal dynamics of lights. Aiming to run on mobile devices on vessel, the MobileNet‐v2 was used as backbone, and with the improvement of spatial attention mechanism, it achieved the accuracy near Resnet‐50 while keeping its high speed.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.1155/2021/6794202
- https://downloads.hindawi.com/journals/cin/2021/6794202.pdf
- OA Status
- hybrid
- Cited By
- 3
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3214287764
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3214287764Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2021/6794202Digital Object Identifier
- Title
-
Multilabel Video Classification Model of Navigation Mark’s Lights Based on Deep LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
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2021-01-01Full publication date if available
- Authors
-
Xu Han, Mingyang Pan, Haipeng Ge, Shaoxi Li, Jingfeng Hu, Lining Zhao, Yu LiList of authors in order
- Landing page
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https://doi.org/10.1155/2021/6794202Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/cin/2021/6794202.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/cin/2021/6794202.pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Flashing, Set (abstract data type), Relevance (law), Computer vision, Brightness, Frame (networking), Pattern recognition (psychology), Telecommunications, Physics, Optics, Metallurgy, Programming language, Law, Political science, Materials scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2024: 2, 2022: 1Per-year citation counts (last 5 years)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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