Domain randomization-enhanced deep learning models for bird detection

Abstract Automatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance t...

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Autores principales:	Xin Mao, Jun Kang Chow, Pin Siang Tan, Kuan-fu Liu, Jimmy Wu, Zhaoyu Su, Ye Hur Cheong, Ghee Leng Ooi, Chun Chiu Pang, Yu-Hsing Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d86c15a5955843a886c09d5595dd32d7
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id	oai:doaj.org-article:d86c15a5955843a886c09d5595dd32d7
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spelling	oai:doaj.org-article:d86c15a5955843a886c09d5595dd32d72021-12-02T14:12:43ZDomain randomization-enhanced deep learning models for bird detection10.1038/s41598-020-80101-x2045-2322https://doaj.org/article/d86c15a5955843a886c09d5595dd32d72021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80101-xhttps://doaj.org/toc/2045-2322Abstract Automatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.Xin MaoJun Kang ChowPin Siang TanKuan-fu LiuJimmy WuZhaoyu SuYe Hur CheongGhee Leng OoiChun Chiu PangYu-Hsing WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xin Mao Jun Kang Chow Pin Siang Tan Kuan-fu Liu Jimmy Wu Zhaoyu Su Ye Hur Cheong Ghee Leng Ooi Chun Chiu Pang Yu-Hsing Wang Domain randomization-enhanced deep learning models for bird detection
description	Abstract Automatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.
format	article
author	Xin Mao Jun Kang Chow Pin Siang Tan Kuan-fu Liu Jimmy Wu Zhaoyu Su Ye Hur Cheong Ghee Leng Ooi Chun Chiu Pang Yu-Hsing Wang
author_facet	Xin Mao Jun Kang Chow Pin Siang Tan Kuan-fu Liu Jimmy Wu Zhaoyu Su Ye Hur Cheong Ghee Leng Ooi Chun Chiu Pang Yu-Hsing Wang
author_sort	Xin Mao
title	Domain randomization-enhanced deep learning models for bird detection
title_short	Domain randomization-enhanced deep learning models for bird detection
title_full	Domain randomization-enhanced deep learning models for bird detection
title_fullStr	Domain randomization-enhanced deep learning models for bird detection
title_full_unstemmed	Domain randomization-enhanced deep learning models for bird detection
title_sort	domain randomization-enhanced deep learning models for bird detection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d86c15a5955843a886c09d5595dd32d7
work_keys_str_mv	AT xinmao domainrandomizationenhanceddeeplearningmodelsforbirddetection AT junkangchow domainrandomizationenhanceddeeplearningmodelsforbirddetection AT pinsiangtan domainrandomizationenhanceddeeplearningmodelsforbirddetection AT kuanfuliu domainrandomizationenhanceddeeplearningmodelsforbirddetection AT jimmywu domainrandomizationenhanceddeeplearningmodelsforbirddetection AT zhaoyusu domainrandomizationenhanceddeeplearningmodelsforbirddetection AT yehurcheong domainrandomizationenhanceddeeplearningmodelsforbirddetection AT gheelengooi domainrandomizationenhanceddeeplearningmodelsforbirddetection AT chunchiupang domainrandomizationenhanceddeeplearningmodelsforbirddetection AT yuhsingwang domainrandomizationenhanceddeeplearningmodelsforbirddetection
_version_	1718391815510425600

Domain randomization-enhanced deep learning models for bird detection

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