Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

doi:10.3390/ani11051263

	Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks
	Wang, Zhaojun 1,2; Wang, Jiangning 1; Lin, Congtian 1,2; Han, Yan 1; Wang, Zhaosheng 3; Ji, Liqiang 1
刊名	ANIMALS
	2021-05-01
卷号	11 期号:5 页码:21
关键词	bird images deep convolutional neural networks habitat elements
ISSN号	2076-2615
DOI	10.3390/ani11051263
通讯作者	Ji, Liqiang(ji@ioz.ac.cn)
英文摘要	Simple Summary To assist researchers in processing large amounts of bird image data, many algorithms have been proposed, but almost all of them aim at solving the problems of bird identification and counting. We turn our attention to the recognition of habitat elements in bird images, which will help with automatically extracting habitat information from such images. To achieve this goal, we formed a dataset and implemented our proposed method with four kinds of deep convolutional neural networks, and the recognition rate reached a minimum of 89.48% and a maximum of 95.52%. The use of this method will supplement the extraction of bird image information and promote the study of the relationships between birds and habitat elements. With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19050202] ; 13th Five-year Informatization Plan of the Chinese Academy of Sciences[XXH13503] ; National R&D Infrastructure and Facility Development Program of China[DKA2017-12-02-10]
WOS关键词	CAMERA TRAPS ; CLASSIFICATION ; DISTANCE ; DENSITY
WOS研究方向	Agriculture ; Veterinary Sciences
语种	英语
出版者	MDPI
WOS记录号	WOS:000653377400001
资助机构	Strategic Priority Research Program of the Chinese Academy of Sciences ; 13th Five-year Informatization Plan of the Chinese Academy of Sciences ; National R&D Infrastructure and Facility Development Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/163999]
专题	中国科学院地理科学与资源研究所
通讯作者	Ji, Liqiang
作者单位	1.Chinese Acad Sci, Inst Zool, Key Lab Anim Ecol & Conservat Biol, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Natl Ecosyst Sci Data Ctr, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Wang, Zhaojun,Wang, Jiangning,Lin, Congtian,et al. Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks[J]. ANIMALS,2021,11(5):21.
APA	Wang, Zhaojun,Wang, Jiangning,Lin, Congtian,Han, Yan,Wang, Zhaosheng,&Ji, Liqiang.(2021).Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks.ANIMALS,11(5),21.
MLA	Wang, Zhaojun,et al."Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks".ANIMALS 11.5(2021):21.