Open AccessAutomatic ladybird beetle detection using deep-learning models
Author(s) -
Pablo Venegas,
Francisco Calderón,
Daniel Riofrío,
Diego S. Benítez,
Giovani Ramón,
Diego F. CisnerosHeredia,
Miguel Coimbra,
José Luis Rojo-Álvarez,
Noel Pérez
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0253027
Subject(s) - artificial intelligence , bounding overwatch , computer science , convolutional neural network , pattern recognition (psychology) , classifier (uml) , random forest , deep learning , contextual image classification , cluster analysis , minimum bounding box , receiver operating characteristic , segmentation , machine learning , image (mathematics)
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.