Deep Learning based Corn Kernel Classification

被引：25

作者：

Velesaca, Henry O. ^{[1
]}

Mira, Raul ^{[1
]}

Suarez, Patricia L. ^{[1
]}

Larrea, Christian X. ^{[1
]}

Sappa, Angel D. ^{[1
,2
]}

机构：

[1] Escuela Super Politecn Litoral, ESPOL, Fac Ingn Elect & Comp, CIDIS, Campus Gustavo Galindo 09-01-5863, Guayaquil, Ecuador

[2] Comp Vis Ctr, Edifici 0,Campus UAB, Barcelona 08193, Spain

来源：

2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2020) | 2020年

关键词：

D O I：

10.1109/CVPRW50498.2020.00041

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 learning-based approach, the Mask R-CNN architecture, while the classification is performed through 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 multi-touching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have been performed and comparisons with other approaches are provided showing improvements with the proposed pipeline.

引用

页码：294 / 302

页数：9

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