TY - GEN
T1 - Cecid Fly Defect Detection in Mangoes Using Object Detection Frameworks
AU - Baculo, Maria Jeseca C.
AU - Ruiz, Conrado
AU - Aran, Oya
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Convolutional neural networks
KW - Defect detection
KW - Image processing
KW - Region-based CNN
UR - http://www.scopus.com/inward/record.url?scp=85118329508&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-89029-2_16
DO - 10.1007/978-3-030-89029-2_16
M3 - Conference contribution
AN - SCOPUS:85118329508
SN - 9783030890285
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 205
EP - 216
BT - Advances in Computer Graphics - 38th Computer Graphics International Conference, CGI 2021, Proceedings
A2 - Magnenat-Thalmann, Nadia
A2 - Magnenat-Thalmann, Nadia
A2 - Interrante, Victoria
A2 - Thalmann, Daniel
A2 - Papagiannakis, George
A2 - Sheng, Bin
A2 - Kim, Jinman
A2 - Gavrilova, Marina
PB - Springer Science and Business Media Deutschland GmbH
T2 - 38th Computer Graphics International Conference, CGI 2021
Y2 - 6 September 2021 through 10 September 2021
ER -