Biodiversity monitoring is crucial for tracking and counteracting adverse trends in population fluctuations. However, automatic recognition systems are rarely applied so far, and experts evaluate the generated data masses manually. Especially the support of deep learning methods for visual monitoring is not yet established in biodiversity research, compared to other areas like advertising or entertainment. In this paper, we present a deep learning pipeline for analyzing images captured by a moth scanner, an automated visual monitoring system of moth species developed within the AMMOD project. We first localize individuals with a moth detector and afterward determine the species of detected insects with a classifier. Our detector achieves up to 99.01% mean average precision and our classifier distinguishes 200 moth species with an accuracy of 93.13% on image cutouts depicting single insects. Combining both in our pipeline improves the accuracy for species identification in images of the moth scanner from 79.62% to 88.05%.

翻译：生物多样性监测对于追踪和应对种群波动的负面趋势至关重要。然而，目前自动化识别系统很少得到应用，专家仍需手动评估海量数据。与广告或娱乐等其他领域相比，深度学习方法在视觉监测方面的支持尚未在生物多样性研究中得到确立。本文提出了一种深度学习流水线，用于分析飞蛾扫描仪捕获的图像——这是AMMOD项目中开发的飞蛾物种自动化视觉监测系统。我们首先通过飞蛾检测器定位个体，随后利用分类器确定被检测昆虫的物种。我们的检测器平均精度均值达到99.01%，分类器在包含单个昆虫的图像切块上可区分200种飞蛾物种，准确率达93.13%。将两者结合于流水线中，可将飞蛾扫描仪图像中物种识别的准确率从79.62%提升至88.05%。