Coccinellidae Beetle Specimen Detection Using Convolutional Neural Networks

Mateo Vega; Diego S. Benítez; Noel Pérez; Daniel Riofrío; Giovani Ramón; Diego Cisneros-Heredia

doi:10.1109/ColCACI52978.2021.9469588

Coccinellidae Beetle Specimen Detection Using Convolutional Neural Networks

Mateo Vega, Diego S. Benítez, Noel Pérez, Daniel Riofrío, Giovani Ramón, Diego Cisneros-Heredia

Universidad San Francisco de Quito

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

3 Citas (Scopus)

Resumen

In this work, we propose a ladybird beetle detector based on a deep learning classifier and the weighted Hausdorff distance as a loss function. The detector was trained and validated using ten-fold cross-validation method on a database composed of 2,633 wildlife images with ladybird beetles. Despite the detector performance was assessed using four metrics, the higher detection result of 98.25% was obtained using the precision metric. This result highlighted the successful performance of the implemented detector, and also, its competence for detecting ladybird beetles in different environments.

Idioma original	Inglés
Título de la publicación alojada	2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings
Editores	Alvaro David Orjuela-Canon
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781665435345
DOI	https://doi.org/10.1109/ColCACI52978.2021.9469588
Estado	Publicada - 26 may. 2021
Evento	2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Virtual, Online, Colombia Duración: 26 may. 2021 → 28 may. 2021

Serie de la publicación

Nombre	2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings

Conferencia

Conferencia	2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021
País/Territorio	Colombia
Ciudad	Virtual, Online
Período	26/05/21 → 28/05/21

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10.1109/ColCACI52978.2021.9469588

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Vega, M., Benítez, D. S., Pérez, N., Riofrío, D., Ramón, G., & Cisneros-Heredia, D. (2021). Coccinellidae Beetle Specimen Detection Using Convolutional Neural Networks. En A. D. Orjuela-Canon (Ed.), 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings Artículo 9469588 (2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ColCACI52978.2021.9469588

Vega, Mateo ; Benítez, Diego S. ; Pérez, Noel et al. / Coccinellidae Beetle Specimen Detection Using Convolutional Neural Networks. 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings. editor / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings).

@inproceedings{f06fcb4b6ce74b11ac2f210f100dbfaf,

title = "Coccinellidae Beetle Specimen Detection Using Convolutional Neural Networks",

abstract = "In this work, we propose a ladybird beetle detector based on a deep learning classifier and the weighted Hausdorff distance as a loss function. The detector was trained and validated using ten-fold cross-validation method on a database composed of 2,633 wildlife images with ladybird beetles. Despite the detector performance was assessed using four metrics, the higher detection result of 98.25% was obtained using the precision metric. This result highlighted the successful performance of the implemented detector, and also, its competence for detecting ladybird beetles in different environments.",

keywords = "deep learning, fully convolutional neural network, heat map, ladybird beetle detection, weighted Hausdorff distance",

author = "Mateo Vega and Ben{\'i}tez, {Diego S.} and Noel P{\'e}rez and Daniel Riofr{\'i}o and Giovani Ram{\'o}n and Diego Cisneros-Heredia",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 ; Conference date: 26-05-2021 Through 28-05-2021",

year = "2021",

month = may,

day = "26",

doi = "10.1109/ColCACI52978.2021.9469588",

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series = "2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings",

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booktitle = "2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings",

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Vega, M, Benítez, DS , Pérez, N , Riofrío, D, Ramón, G & Cisneros-Heredia, D 2021, Coccinellidae Beetle Specimen Detection Using Convolutional Neural Networks. En AD Orjuela-Canon (ed.), 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings., 9469588, 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021, Virtual, Online, Colombia, 26/05/21. https://doi.org/10.1109/ColCACI52978.2021.9469588

Coccinellidae Beetle Specimen Detection Using Convolutional Neural Networks. / Vega, Mateo; Benítez, Diego S.; Pérez, Noel et al.
2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings. ed. / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2021. 9469588 (2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Coccinellidae Beetle Specimen Detection Using Convolutional Neural Networks

AU - Vega, Mateo

AU - Benítez, Diego S.

AU - Pérez, Noel

AU - Riofrío, Daniel

AU - Ramón, Giovani

AU - Cisneros-Heredia, Diego

PY - 2021/5/26

Y1 - 2021/5/26

N2 - In this work, we propose a ladybird beetle detector based on a deep learning classifier and the weighted Hausdorff distance as a loss function. The detector was trained and validated using ten-fold cross-validation method on a database composed of 2,633 wildlife images with ladybird beetles. Despite the detector performance was assessed using four metrics, the higher detection result of 98.25% was obtained using the precision metric. This result highlighted the successful performance of the implemented detector, and also, its competence for detecting ladybird beetles in different environments.

AB - In this work, we propose a ladybird beetle detector based on a deep learning classifier and the weighted Hausdorff distance as a loss function. The detector was trained and validated using ten-fold cross-validation method on a database composed of 2,633 wildlife images with ladybird beetles. Despite the detector performance was assessed using four metrics, the higher detection result of 98.25% was obtained using the precision metric. This result highlighted the successful performance of the implemented detector, and also, its competence for detecting ladybird beetles in different environments.

KW - deep learning

KW - fully convolutional neural network

KW - heat map

KW - ladybird beetle detection

KW - weighted Hausdorff distance

UR - http://www.scopus.com/inward/record.url?scp=85114207250&partnerID=8YFLogxK

U2 - 10.1109/ColCACI52978.2021.9469588

DO - 10.1109/ColCACI52978.2021.9469588

M3 - Contribución a la conferencia

AN - SCOPUS:85114207250

T3 - 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings

BT - 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings

A2 - Orjuela-Canon, Alvaro David

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021

Y2 - 26 May 2021 through 28 May 2021

ER -

Vega M, Benítez DS , Pérez N , Riofrío D, Ramón G, Cisneros-Heredia D. Coccinellidae Beetle Specimen Detection Using Convolutional Neural Networks. En Orjuela-Canon AD, editor, 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. 9469588. (2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings). doi: 10.1109/ColCACI52978.2021.9469588

Coccinellidae Beetle Specimen Detection Using Convolutional Neural Networks

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