Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect
  Localization and Species Classification
Dimitri Korsch Paul Bodesheim Joachim Denzler
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%.