Coccinellidae Beetle Specimen Detection Using Convolutional Neural Networks

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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 originalInglés
Título de la publicación alojada2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings
EditoresAlvaro David Orjuela-Canon
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665435345
DOI
EstadoPublicada - 26 may. 2021
Evento2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Virtual, Online, Colombia
Duración: 26 may. 202128 may. 2021

Serie de la publicación

Nombre2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings

Conferencia

Conferencia2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021
País/TerritorioColombia
CiudadVirtual, Online
Período26/05/2128/05/21

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Profundice en los temas de investigación de 'Coccinellidae Beetle Specimen Detection Using Convolutional Neural Networks'. En conjunto forman una huella única.

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