Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification

TL;DR

This paper introduces a deep learning pipeline that automates moth detection and species classification in images, significantly enhancing biodiversity monitoring efficiency and accuracy.

Contribution

It presents a novel deep learning system for automated moth detection and classification, achieving high precision and expanding species identification capabilities in biodiversity research.

Findings

Detector achieves 99.01% mean average precision

Classifier distinguishes 200 species with 93.13% accuracy

Pipeline improves species identification accuracy from 79.62% to 88.05%

Abstract

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%.