Auflistung nach Autor:in "Korsch, Dimitri"
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- TextdokumentDeep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification(INFORMATIK 2021, 2021) Korsch, Dimitri; Bodesheim, Paul; Denzler, JoachimBiodiversity 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%.
- TextdokumentExploiting Web Images for Moth Species Classification(INFORMATIK 2021, 2021) Böhlke,Julia; Korsch, Dimitri; Bodesheim, Paul; Denzler, JoachimDue to shrinking habitats, moth populations are declining rapidly. An automated moth population monitoring tool is needed to support conservationists in making informed decisions for counteracting this trend. A non-invasive tool would involve the automatic classification of images of moths, a fine-grained recognition problem. Currently, the lack of images annotated by experts is the main hindrance to such a classification model. To understand how to achieve acceptable predictive accuracies, we investigate the effect of differently sized datasets and data acquired from the Internet. We find the use of web data immensely beneficial and observe that few images from the evaluation domain are enough to mitigate the domain shift in web data. Our experiments show that counteracting the domain shift may yield a relative reduction of the error rate of over 60%. Lastly, the effect of label noise in web data and proposed filtering techniques are analyzed and evaluated.