Cecid Fly Defect Detection in Mangoes Using Object Detection Frameworks

Maria Jeseca C. Baculo, Conrado Ruiz, Oya Aran

Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts

1 Citació (Scopus)

Resum

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 originalAnglès
Títol de la publicacióAdvances in Computer Graphics - 38th Computer Graphics International Conference, CGI 2021, Proceedings
EditorsNadia Magnenat-Thalmann, Nadia Magnenat-Thalmann, Victoria Interrante, Daniel Thalmann, George Papagiannakis, Bin Sheng, Jinman Kim, Marina Gavrilova
EditorSpringer Science and Business Media Deutschland GmbH
Pàgines205-216
Nombre de pàgines12
ISBN (imprès)9783030890285
DOIs
Estat de la publicacióPublicada - 2021
Publicat externament
Esdeveniment38th Computer Graphics International Conference, CGI 2021 - Virtual, Online
Durada: 6 de set. 202110 de set. 2021

Sèrie de publicacions

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volum13002 LNCS
ISSN (imprès)0302-9743
ISSN (electrònic)1611-3349

Conferència

Conferència38th Computer Graphics International Conference, CGI 2021
CiutatVirtual, Online
Període6/09/2110/09/21

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