Polarized skylight orientation determination artificial neural network
This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarizati...
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| Veröffentlicht in: | arXiv.org 2021-07 |
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| creator | Liang, Huaju Bai, Hongyang Hu, Ke Lv, Xinbo |
| description | This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect's encoding of polarization information, and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network. |
| doi_str_mv | 10.48550/arxiv.2107.02328 |
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This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect's encoding of polarization information, and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2107.02328</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Artificial neural networks ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Convolution ; Exponential functions ; Insects ; Luminous intensity ; Neural networks ; Orientation ; Polarization ; Skylights</subject><ispartof>arXiv.org, 2021-07</ispartof><rights>2021. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). 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This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect's encoding of polarization information, and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.</description><subject>Artificial neural networks</subject><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Convolution</subject><subject>Exponential functions</subject><subject>Insects</subject><subject>Luminous intensity</subject><subject>Neural networks</subject><subject>Orientation</subject><subject>Polarization</subject><subject>Skylights</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><sourceid>GOX</sourceid><recordid>eNotj1FLwzAUhYMgOOZ-gE8WfG5NbnKb9FGG08FAH_Ze0ibRbF0701Sdv966-nQ4cPg4HyE3jGZCIdJ7Hb79ZwaMyowCB3VBZsA5S5UAuCKLvt9RSiGXgMhnZPXaNTr4H2uSfn9q_Nt7TLrgbRt19F2bGBttOPh2ajpE73ztdZO0dgjniF9d2F-TS6eb3i7-c062q8ft8jndvDytlw-bVCPwFHSluJKaonHKFaJQlXIODEeBTGIhlXGsqiV1KFiBTKgaq6LOdc5rA7bic3I7Yc-S5TH4gw6n8k-2PMuOi7tpcQzdx2D7WO66IbTjpxJQFBRGKue_ZMlYKg</recordid><startdate>20210706</startdate><enddate>20210706</enddate><creator>Liang, Huaju</creator><creator>Bai, Hongyang</creator><creator>Hu, Ke</creator><creator>Lv, Xinbo</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20210706</creationdate><title>Polarized skylight orientation determination artificial neural network</title><author>Liang, Huaju ; Bai, Hongyang ; Hu, Ke ; Lv, Xinbo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a523-2ab8387a05df8f9498b8ff2d3545175978df1bc70f54195148c5b9c6a63cd2eb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial neural networks</topic><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Convolution</topic><topic>Exponential functions</topic><topic>Insects</topic><topic>Luminous intensity</topic><topic>Neural networks</topic><topic>Orientation</topic><topic>Polarization</topic><topic>Skylights</topic><toplevel>online_resources</toplevel><creatorcontrib>Liang, Huaju</creatorcontrib><creatorcontrib>Bai, Hongyang</creatorcontrib><creatorcontrib>Hu, Ke</creatorcontrib><creatorcontrib>Lv, Xinbo</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Computer Science</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liang, Huaju</au><au>Bai, Hongyang</au><au>Hu, Ke</au><au>Lv, Xinbo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Polarized skylight orientation determination artificial neural network</atitle><jtitle>arXiv.org</jtitle><date>2021-07-06</date><risdate>2021</risdate><eissn>2331-8422</eissn><abstract>This paper proposes an artificial neural network to determine orientation using polarized skylight. 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| subjects | Artificial neural networks Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Convolution Exponential functions Insects Luminous intensity Neural networks Orientation Polarization Skylights |
| title | Polarized skylight orientation determination artificial neural network |
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