Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification
This addresses the problem of manual biodiversity monitoring for researchers by providing an automated system, though it is incremental as it applies existing deep learning methods to a new domain.
The paper tackles automated visual monitoring of moth species by developing a deep learning pipeline that localizes insects and classifies species, achieving up to 99.01% mAP for detection and 93.13% accuracy for classifying 200 species, with the pipeline improving species identification accuracy from 79.62% to 88.05%.
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%.