Cecid Fly Defect Detection in Mangoes Using Object Detection Frameworks

Maria Jeseca C. Baculo, Conrado Ruiz, Oya Aran

Producción científica: Capítulo del libroContribución a congreso/conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

Mango export has experienced rapid growth in global trade over the past few years, however, they are susceptible to surface defects that can affect their market value. This paper investigates the automated detection of a mango defect caused by cecid flies, which can affect a significant portion of the production yield. Object detection frameworks using CNN were used to localize and detect multiple defects present in a single mango image. This paper also proposes modified versions of R-CNN and FR-CNN replacing its region search algorithms with segmentation-based region extraction. A dataset consisting of 1329 cecid fly surface blemishes was used to train the object detection models. The results of the experiments show comparable performance between the modified and existing state-of-the-art object detection frameworks. Results show that Faster R-CNN achieved the highest average precision of 0.901 at aP50 while the Modified FR-CNN has the highest average precision of 0.723 at aP75.

Idioma originalInglés
Título de la publicación alojadaAdvances in Computer Graphics - 38th Computer Graphics International Conference, CGI 2021, Proceedings
EditoresNadia Magnenat-Thalmann, Nadia Magnenat-Thalmann, Victoria Interrante, Daniel Thalmann, George Papagiannakis, Bin Sheng, Jinman Kim, Marina Gavrilova
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas205-216
Número de páginas12
ISBN (versión impresa)9783030890285
DOI
EstadoPublicada - 2021
Publicado de forma externa
Evento38th Computer Graphics International Conference, CGI 2021 - Virtual, Online
Duración: 6 sept 202110 sept 2021

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen13002 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia38th Computer Graphics International Conference, CGI 2021
CiudadVirtual, Online
Período6/09/2110/09/21

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