Multilabel Video Classification Model of Navigation Mark’s Lights Based on Deep Learning

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 resea...

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Autores principales:	Xu Han, Mingyang Pan, Haipeng Ge, Shaoxi Li, Jingfeng Hu, Lining Zhao, Yu Li
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/5bb8b6c740f64191b0573bac0156eceb
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id	oai:doaj.org-article:5bb8b6c740f64191b0573bac0156eceb
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spelling	oai:doaj.org-article:5bb8b6c740f64191b0573bac0156eceb2021-11-22T01:10:09ZMultilabel Video Classification Model of Navigation Mark’s Lights Based on Deep Learning1687-527310.1155/2021/6794202https://doaj.org/article/5bb8b6c740f64191b0573bac0156eceb2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6794202https://doaj.org/toc/1687-5273At 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.Xu HanMingyang PanHaipeng GeShaoxi LiJingfeng HuLining ZhaoYu LiHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Xu Han Mingyang Pan Haipeng Ge Shaoxi Li Jingfeng Hu Lining Zhao Yu Li Multilabel Video Classification Model of Navigation Mark’s Lights Based on Deep Learning
description	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.
format	article
author	Xu Han Mingyang Pan Haipeng Ge Shaoxi Li Jingfeng Hu Lining Zhao Yu Li
author_facet	Xu Han Mingyang Pan Haipeng Ge Shaoxi Li Jingfeng Hu Lining Zhao Yu Li
author_sort	Xu Han
title	Multilabel Video Classification Model of Navigation Mark’s Lights Based on Deep Learning
title_short	Multilabel Video Classification Model of Navigation Mark’s Lights Based on Deep Learning
title_full	Multilabel Video Classification Model of Navigation Mark’s Lights Based on Deep Learning
title_fullStr	Multilabel Video Classification Model of Navigation Mark’s Lights Based on Deep Learning
title_full_unstemmed	Multilabel Video Classification Model of Navigation Mark’s Lights Based on Deep Learning
title_sort	multilabel video classification model of navigation mark’s lights based on deep learning
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/5bb8b6c740f64191b0573bac0156eceb
work_keys_str_mv	AT xuhan multilabelvideoclassificationmodelofnavigationmarkslightsbasedondeeplearning AT mingyangpan multilabelvideoclassificationmodelofnavigationmarkslightsbasedondeeplearning AT haipengge multilabelvideoclassificationmodelofnavigationmarkslightsbasedondeeplearning AT shaoxili multilabelvideoclassificationmodelofnavigationmarkslightsbasedondeeplearning AT jingfenghu multilabelvideoclassificationmodelofnavigationmarkslightsbasedondeeplearning AT liningzhao multilabelvideoclassificationmodelofnavigationmarkslightsbasedondeeplearning AT yuli multilabelvideoclassificationmodelofnavigationmarkslightsbasedondeeplearning
_version_	1718418402023833600

Multilabel Video Classification Model of Navigation Mark’s Lights Based on Deep Learning

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