@InProceedings{HenryVelesaca2020,
author="Henry Velesaca
and Raul Mira
and Patricia Suarez
and Christian X. Larrea
and Angel Sappa",
title="Deep Learning Based Corn Kernel Classification",
booktitle="1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges \& Opportunities for Computer Vision in Agriculture",
year="2020",
abstract="This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learningbased approach, the Mask R-CNN architecture, while the classification is performed hrough a novel-lightweight network specially designed for this task---good corn kernel, defective corn kernel and impurity categories are considered. As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have beenperformed and comparisons with other approaches are provided showing improvements with the proposed pipeline.",
optnote="MSIAU; 600.130; 600.122",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3430), last updated on Tue, 25 Apr 2023 14:55:31 +0200",
opturl="https://ieeexplore.ieee.org/document/9150684",
file=":http://refbase.cvc.uab.es/files/VMS2020.pdf:PDF"
}