Deep Learning Based Corn Kernel Classification

Henry O. Velesaca, Raúl Mira, Patricia L. Suárez, Christian X. Larrea, Ángel D. Sappa

CVPRW 2020 pp. 294-302

doi:10.1109/CVPRW50498.2020.00041 /cvprw/2020/velesaca2020cvprw-deep/

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 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 multitouching 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.

CVPRW Semantic Scholar

Cite

Text

Velesaca et al. "Deep Learning Based Corn Kernel Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00041

Markdown

[Velesaca et al. "Deep Learning Based Corn Kernel Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/velesaca2020cvprw-deep/) doi:10.1109/CVPRW50498.2020.00041

BibTeX

@inproceedings{velesaca2020cvprw-deep,
  title     = {{Deep Learning Based Corn Kernel Classification}},
  author    = {Velesaca, Henry O. and Mira, Raúl and Suárez, Patricia L. and Larrea, Christian X. and Sappa, Ángel D.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2020},
  pages     = {294-302},
  doi       = {10.1109/CVPRW50498.2020.00041},
  url       = {https://mlanthology.org/cvprw/2020/velesaca2020cvprw-deep/}
}