Zhou Jiaogen, Wang Yang, Zhang Caiyun, Wu Wenbo, Ji Yanzhu, Zou Yeai
Jiangsu Provincial Engineering Research Center for Intelligent Monitoring and Ecological Management of Pond and Reservoir Water Environment, Huaiyin Normal University, Huaian 223300, China.
Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.
Animals (Basel). 2022 Nov 1;12(21):3000. doi: 10.3390/ani12213000.
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.
让公众轻松识别水鸟对湿地鸟类保护具有积极作用。然而，对水鸟进行分类需要先进的鸟类学知识，这使得公众在日常生活中很难识别水鸟物种。为了打破公众识别水鸟的知识障碍，我们利用深度学习构建了一个水鸟识别系统（Eyebirds），该系统以智能手机应用程序的形式实现。Eyebirds由三个主要模块组成：（1）一个水鸟图像数据集；（2）一个基于注意力机制的用于水鸟识别的深度卷积神经网络（AM-CNN）；（3）一个面向智能手机用户的应用程序。水鸟图像数据集目前涵盖全球48个科、203个属和548种水鸟，用于训练我们的水鸟识别模型。AM-CNN模型采用注意力机制来增强鸟类图像的浅层特征，以提高图像分类性能。在北美鸟类数据集（CUB200-2011）上的实验结果表明，AM-CNN模型的平均分类准确率达到85%。在我们自建的水鸟图像数据集上，AM-CNN模型在科、属和种三个层面的分类准确率分别为94.0%、93.6%和86.4%，效果也很好。用户端应用程序是一个部署在智能手机上的微信小程序。有了这个应用程序，用户可以在探险、露营、观光甚至日常生活中轻松识别水鸟。总之，我们的系统不仅能给公众带来乐趣，还能传播水鸟知识，从而激发他们的兴趣，进一步促进他们参与鸟类生态保护。
