Abstract
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 \(aP_{50}\) while the Modified FR-CNN has the highest average precision of 0.723 at \(aP_{75}\).
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Baculo, M.J.C., Ruiz, C., Aran, O. (2021). Cecid Fly Defect Detection in Mangoes Using Object Detection Frameworks. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2021. Lecture Notes in Computer Science(), vol 13002. Springer, Cham. https://doi.org/10.1007/978-3-030-89029-2_16
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DOI: https://doi.org/10.1007/978-3-030-89029-2_16
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