Eyebirds: Enabling the Public to Recognize Water Birds at Hand

文献类型： 外文期刊

第一作者： Zhou, Jiaogen

作者： Zhou, Jiaogen;Zhang, Caiyun;Wang, Yang;Wu, Wenbo;Ji, Yanzhu;Zou, Yeai

作者机构：

关键词： bird conservation; bird identification; deep learning; deep convolution neural network; attention; image feature

期刊名称：ANIMALS （ 影响因子：3.231； 五年影响因子：3.312 ）

ISSN： 2076-2615

年卷期： 2022 年 12 卷 21 期

页码：

收录情况： SCI

摘要： Simple Summary Enabling the public to easily recognize water birds at hand has a positive effect on wetland bird conservation. An attention mechanism-based deep convolution neural network model (AM-CNN) is developed for water bird recognition. The model employs an effective strategy that enhances the perception of shallow image features in convolutional layers, and achieves up to 86.4% classification accuracy on our self-constructed image dataset of 548 global water bird species. The model is implemented as the mobile app of EyeBirds for smart phones. The app offers three main functions of bird image recognition, bird information and bird field survey to users. Overall, EyeBirds is useful to assist the public to easily recognize water birds and acquire bird knowledge. Enabling the public to easily recognize water birds has a positive effect on wetland bird conservation. However, classifying water birds requires advanced ornithological knowledge, which makes it very difficult for the public to recognize water bird species in daily life. To break the knowledge barrier of water bird recognition for the public, we construct a water bird recognition system (Eyebirds) by using deep learning, which is implemented as a smartphone app. Eyebirds consists of three main modules: (1) a water bird image dataset; (2) an attention mechanism-based deep convolution neural network for water bird recognition (AM-CNN); (3) an app for smartphone users. The waterbird image dataset currently covers 48 families, 203 genera and 548 species of water birds worldwide, which is used to train our water bird recognition model. The AM-CNN model employs attention mechanism to enhance the shallow features of bird images for boosting image classification performance. Experimental results on the North American bird dataset (CUB200-2011) show that the AM-CNN model achieves an average classification accuracy of 85%. On our self-built water bird image dataset, the AM-CNN model also works well with classification accuracies of 94.0%, 93.6% and 86.4% at three levels: family, genus and species, respectively. The user-side app is a WeChat applet deployed in smartphones. With the app, users can easily recognize water birds in expeditions, camping, sightseeing, or even daily life. In summary, our system can bring not only fun, but also water bird knowledge to the public, thus inspiring their interests and further promoting their participation in bird ecological conservation.

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