Coccinellidae beetle specimen detection using convolutional neural networks

Mateo Vega; Diego S. Benitez; Noel Perez; Daniel Riofrio; Giovani Ramon; Diego Cisneros-Heredia

doi:10.1109/ColCACI52978.2021.9469588

Coccinellidae beetle specimen detection using convolutional neural networks

Mateo Vega^*, Diego S. Benitez, Noel Perez, Daniel Riofrio, Giovani Ramon, Diego Cisneros-Heredia

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	2021 IEEE Colombian Conference on Applications of Computational Intelligence – ColCACI
Editors	Alvaro David Orjuela-Canon
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781665435345
DOIs	https://doi.org/10.1109/ColCACI52978.2021.9469588
Publication status	Published - 2021
Externally published	Yes
Event	IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI) - Virtual Duration: 26 May 2021 → 28 May 2021

Publication series

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

Conference

Conference	IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI)
Period	26/05/21 → 28/05/21

Keywords

ladybird beetle detection
deep learning
fully convolutional neural network
weighted Hausdorff distance
heat map

Access to Document

10.1109/ColCACI52978.2021.9469588

Cite this

Vega, M., Benitez, D. S., Perez, N., Riofrio, D., Ramon, G., & Cisneros-Heredia, D. (2021). Coccinellidae beetle specimen detection using convolutional neural networks. In A. D. Orjuela-Canon (Ed.), 2021 IEEE Colombian Conference on Applications of Computational Intelligence – ColCACI (pp. 1-5). (2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ColCACI52978.2021.9469588

Vega, Mateo ; Benitez, Diego S. ; Perez, Noel et al. / Coccinellidae beetle specimen detection using convolutional neural networks. 2021 IEEE Colombian Conference on Applications of Computational Intelligence – ColCACI. editor / Alvaro David Orjuela-Canon. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2021. pp. 1-5 (2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings).

@inproceedings{21dc95d0bb544ad8896c793d9ed70670,

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

author = "Mateo Vega and Benitez, {Diego S.} and Noel Perez and Daniel Riofrio and Giovani Ramon and Diego Cisneros-Heredia",

year = "2021",

doi = "10.1109/ColCACI52978.2021.9469588",

language = "English",

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

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "1--5",

editor = "Orjuela-Canon, {Alvaro David}",

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

address = "United States",

note = "IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI) ; Conference date: 26-05-2021 Through 28-05-2021",

}

Vega, M, Benitez, DS, Perez, N, Riofrio, D, Ramon, G & Cisneros-Heredia, D 2021, Coccinellidae beetle specimen detection using convolutional neural networks. in AD Orjuela-Canon (ed.), 2021 IEEE Colombian Conference on Applications of Computational Intelligence – ColCACI. 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 1-5, IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI), 26/05/21. https://doi.org/10.1109/ColCACI52978.2021.9469588

Coccinellidae beetle specimen detection using convolutional neural networks. / Vega, Mateo; Benitez, Diego S.; Perez, Noel et al.
2021 IEEE Colombian Conference on Applications of Computational Intelligence – ColCACI. ed. / Alvaro David Orjuela-Canon. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2021. p. 1-5 (2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Coccinellidae beetle specimen detection using convolutional neural networks

AU - Vega, Mateo

AU - Benitez, Diego S.

AU - Perez, Noel

AU - Riofrio, Daniel

AU - Ramon, Giovani

AU - Cisneros-Heredia, Diego

PY - 2021

Y1 - 2021

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 - ladybird beetle detection

KW - deep learning

KW - fully convolutional neural network

KW - weighted Hausdorff distance

KW - heat map

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 - Conference proceeding contribution

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

SP - 1

EP - 5

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

A2 - Orjuela-Canon, Alvaro David

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI)

Y2 - 26 May 2021 through 28 May 2021

ER -

Vega M, Benitez DS, Perez N, Riofrio D, Ramon G, Cisneros-Heredia D. Coccinellidae beetle specimen detection using convolutional neural networks. In Orjuela-Canon AD, editor, 2021 IEEE Colombian Conference on Applications of Computational Intelligence – ColCACI. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2021. p. 1-5. (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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