TY - GEN
T1 - BeetleID
T2 - 5th IEEE Ecuador Technical Chapters Meeting, ETCM 2021
AU - Muriel, Ricardo
AU - Pérez, Noel
AU - Benítez, Diego S.
AU - Riofrío, Daniel
AU - Ramón, Giovani
AU - Peñaherrera, Emilia
AU - Cisneros-Heredia, Diego
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/10/12
Y1 - 2021/10/12
N2 - In this work, an Android mobile application named BeetleID was developed to detect ladybird beetles through image pre-processing methods and a deep learning convolutional neural network model. The image pre-processing module consists of three main algorithms: saliency map, active contour, and superpixel segmentation. The used convolutional neural network was validated with a 2611 image set of ladybird beetle species with a five-fold cross-validation method. It achieved accuracy and area under the curve of the receiver operating characteristic scores of 0.92 and 0.98, respectively. Furthermore, the application's feasibility was assessed by the mean execution time and battery consumption metrics of mobile emulators, phone Pixel 3a XL and tablet Pixel C, which obtained 16.32 and 18.43 seconds 0.07 and 0.11 milliampere-hour, respectively. These results prove that the proposed application is an excellent solution, with a few optimization issues, for specialists to detect ladybird beetles in wildlife environments accurately.
AB - In this work, an Android mobile application named BeetleID was developed to detect ladybird beetles through image pre-processing methods and a deep learning convolutional neural network model. The image pre-processing module consists of three main algorithms: saliency map, active contour, and superpixel segmentation. The used convolutional neural network was validated with a 2611 image set of ladybird beetle species with a five-fold cross-validation method. It achieved accuracy and area under the curve of the receiver operating characteristic scores of 0.92 and 0.98, respectively. Furthermore, the application's feasibility was assessed by the mean execution time and battery consumption metrics of mobile emulators, phone Pixel 3a XL and tablet Pixel C, which obtained 16.32 and 18.43 seconds 0.07 and 0.11 milliampere-hour, respectively. These results prove that the proposed application is an excellent solution, with a few optimization issues, for specialists to detect ladybird beetles in wildlife environments accurately.
KW - Android application
KW - Coccinelidae species detection
KW - Deep learning models
KW - image pre-processing
UR - https://www.scopus.com/pages/publications/85119424749
U2 - 10.1109/ETCM53643.2021.9590826
DO - 10.1109/ETCM53643.2021.9590826
M3 - Contribución a la conferencia
AN - SCOPUS:85119424749
T3 - ETCM 2021 - 5th Ecuador Technical Chapters Meeting
BT - ETCM 2021 - 5th Ecuador Technical Chapters Meeting
A2 - Huerta, Monica Karel
A2 - Quevedo, Sebastian
A2 - Monsalve, Carlos
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 October 2021 through 15 October 2021
ER -