Automatic ladybird beetle detection using deep-learning models

Pablo Venegas, Francisco Calderon, Daniel Riofrío, Diego Benítez, Giovani Ramón, Diego Cisneros-Heredia, Miguel Coimbra, José Luis Rojo-Álvarez, Noel Pérez

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

11 Citas (Scopus)

Resumen

Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics. Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first stage towards an automated classification system. First, an image processing module composed of a saliency map representation, simple linear iterative clustering superpixels segmentation, and active contour methods allowed us to generate bounding boxes with possible ladybird beetles locations within an image. Subsequently, a deep convolutional neural network-based classifier selects only the bounding boxes with ladybird beetles as the final output. This method was validated on a 2, 300 ladybird beetle image data set from Ecuador and Colombia obtained from the iNaturalist project. The proposed approach achieved an accuracy score of 92% and an area under the receiver operating characteristic curve of 0.977 for the bounding box generation and classification tasks. These successful results enable the proposed detector as a valuable tool for helping specialists in the ladybird beetle detection problem.

Idioma originalInglés
Número de artículoe0253027
PublicaciónPLoS ONE
Volumen16
N.º6 June
DOI
EstadoPublicada - jun. 2021

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