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

arXiv: https://arxiv.org/abs/2307.15427 · HTML: https://mlsanity.com/paper/2307.15427/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 57 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Bilinear CNNs for Fine-grained Visual Recognition](https://mlsanity.com/paper/1504.07889/) (2015)
- [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](https://mlsanity.com/paper/1312.6229/) (2013)
- [Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning](https://mlsanity.com/paper/1806.06193/) (2018)
- [Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up](https://mlsanity.com/paper/1903.02827/) (2019)
- [The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition](https://mlsanity.com/paper/1511.06789/) (2015)
- [Scalable Object Detection using Deep Neural Networks](https://mlsanity.com/paper/1312.2249/) (2013)
- [Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks](https://mlsanity.com/paper/1504.08289/) (2015)
- [Visual Recognition of Great Ape Behaviours in the Wild](https://mlsanity.com/paper/2011.10759/) (2020)
- [Classification-Specific Parts for Improving Fine-Grained Visual Categorization](https://mlsanity.com/paper/1909.07075/) (2019)
- [Great Ape Detection in Challenging Jungle Camera Trap Footage via Attention-Based Spatial and Temporal Feature Blending](https://mlsanity.com/paper/1908.11240/) (2019)
- [Automated Visual Fin Identification of Individual Great White Sharks](https://mlsanity.com/paper/1609.06323/) (2016)
- [Automatic Moth Detection from Trap Images for Pest Management](https://mlsanity.com/paper/1602.07383/) (2016)
- [Towards Automatic Identification of Elephants in the Wild](https://mlsanity.com/paper/1812.04418/) (2018)
- [Active and Incremental Learning with Weak Supervision](https://mlsanity.com/paper/2001.07100/) (2020)
- [Fine-grained Recognition Datasets for Biodiversity Analysis](https://mlsanity.com/paper/1507.00913/) (2015)
- [Unsupervised Part Mining for Fine-grained Image Classification](https://mlsanity.com/paper/1902.09941/) (2019)

## Built on by (most cited first, 5 of 5)

- [Insect Identification in the Wild: The AMI Dataset](https://mlsanity.com/paper/2406.12452/) (2024)
- [Geometrically Constrained and Token-Based Probabilistic Spatial Transformers](https://mlsanity.com/paper/2509.11218/) (2025)
- [Saccadic Vision for Fine-Grained Visual Classification](https://mlsanity.com/paper/2509.15688/) (2025)
- [A machine learning pipeline for automated insect monitoring](https://mlsanity.com/paper/2406.13031/) (2024)
- [Robust Canonicalization through Bootstrapped Data Re-Alignment](https://mlsanity.com/paper/2510.08178/) (2025)

## Abstract (arXiv)

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%.