Abstract The diet of seabirds can yield important insights into the status of economically and ecologically important fish. By analyzing the otoliths found in the birds’ droppings, researchers can observe which fish the birds eat in which abundances. However, identifying the species based on an otolith image is quite labor-intensive and requires particular expertise. In this work, we show that a deep convolutional neural network can identify six fish species with high accuracy. We show that this deep learning approach outperforms more traditional methods and is also more accessible to set up in practice. By exploiting the hierarchy in the species labels, we impose a structure on the prediction probabilities, leading to a remarkable improvement compared to a conventional artificial neural network. Importantly, we can attain good results using only a modest dataset, demonstrating that such approaches are feasible for small-scale and specialized projects.

中文翻译：

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使用深度分层分类模型进行耳石识别

摘要 海鸟的饮食可以对经济和生态上重要的鱼类的状况产生重要的见解。通过分析在鸟类粪便中发现的耳石，研究人员可以观察鸟类在何种丰度下吃哪种鱼。然而，根据耳石图像识别物种是非常劳动密集型的，需要特殊的专业知识。在这项工作中，我们表明深度卷积神经网络可以高精度识别六种鱼类。我们表明，这种深度学习方法优于更传统的方法，并且在实践中也更易于设置。通过利用物种标签中的层次结构，我们在预测概率上强加了一个结构，与传统的人工神经网络相比，有了显着的改进。重要的，