Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Zhaojun Wang; Jiangning Wang; Congtian Lin; Yan Han; Zhaosheng Wang; Liqiang Ji

doi:10.3390/ani11051263

Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Animals (Basel). 2021 Apr 27;11(5):1263. doi: 10.3390/ani11051263.

Authors

Zhaojun Wang^{1

2}, Jiangning Wang¹, Congtian Lin^{1

2}, Yan Han¹, Zhaosheng Wang³, Liqiang Ji¹

Affiliations

¹ Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.
² College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China.
³ National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.

Abstract

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.

Keywords: bird images; deep convolutional neural networks; habitat elements.