Efficient CNN-Based System for Automated Beetle Elytra Coordinates Prediction

Hojin Yoo, Dhanyapriya Somasundaram, Hyunju Oh

2025

Abstract

Beetles represent nearly a quarter of all known animal species and play crucial roles in ecosystems. A key morphological feature, the elytra, provides essential protection and adaptability but measuring their size manually is labor-intensive and prone to errors, especially with large datasets containing multiple specimens per image. To address this, we introduce a deep learning-based framework that automates the detection and measurement of beetle elytra using Convolutional Neural Networks (CNN). Our system integrates advanced object detection techniques to accurately localize individual beetles and predict elytra coordinates, enabling precise measurement of elytra length and width. Additionally, we recreated an existing beetle dataset tailored for elytra coordinate prediction. Through comprehensive experiments and ablation studies, we optimized our framework to achieve a measurement accuracy with an error margin of only 0.1 cm. This automated approach significantly reduces manual effort and facilitates large-scale beetle trait analysis, thereby advancing biodiversity research and ecological assessments. Code is available at https://github.com/yoohj0416/predictbeetle.

Paper Citation

in Harvard Style

Yoo H., Somasundaram D. and Oh H. (2025). Efficient CNN-Based System for Automated Beetle Elytra Coordinates Prediction. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 934-941. DOI: 10.5220/0013264600003912

in Bibtex Style

@conference{visapp25,
author={Hojin Yoo and Dhanyapriya Somasundaram and Hyunju Oh},
title={Efficient CNN-Based System for Automated Beetle Elytra Coordinates Prediction},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={934-941},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013264600003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Efficient CNN-Based System for Automated Beetle Elytra Coordinates Prediction
SN - 978-989-758-728-3
AU - Yoo H.
AU - Somasundaram D.
AU - Oh H.
PY - 2025
SP - 934
EP - 941
DO - 10.5220/0013264600003912
PB - SciTePress