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An Embedded Computer Vision System for Beans Quality Inspection

by Robson A.G. Macedo, Peterson A. Belan, Sidnei A. Araújo
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 24
Year of Publication: 2020
Authors: Robson A.G. Macedo, Peterson A. Belan, Sidnei A. Araújo
10.5120/ijca2020920779

Robson A.G. Macedo, Peterson A. Belan, Sidnei A. Araújo . An Embedded Computer Vision System for Beans Quality Inspection. International Journal of Computer Applications. 175, 24 ( Oct 2020), 44-53. DOI=10.5120/ijca2020920779

@article{ 10.5120/ijca2020920779,
author = { Robson A.G. Macedo, Peterson A. Belan, Sidnei A. Araújo },
title = { An Embedded Computer Vision System for Beans Quality Inspection },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2020 },
volume = { 175 },
number = { 24 },
month = { Oct },
year = { 2020 },
issn = { 0975-8887 },
pages = { 44-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number24/31603-2020920779/ },
doi = { 10.5120/ijca2020920779 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:26:03.223415+05:30
%A Robson A.G. Macedo
%A Peterson A. Belan
%A Sidnei A. Araújo
%T An Embedded Computer Vision System for Beans Quality Inspection
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 24
%P 44-53
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Despite the importance of agricultural grains appearance for their choice by the consumers as well as for determining their selling price, the visual inspection of the quality of these products is usually conducted in a manual way and, therefore, susceptible to high operational costs, human errors and inaccurate results. Recently, a computer vision system for quality inspection of beans composed by a set of hardware and software, named the SIVQUAF, was proposed in the literature. However, the software of the SIVQUAF was designed for a personal computer, which makes its operation more complex, decreases its performance and raises the cost of the equipment. Thus, in this work we explored the customization and optimization of SIVQUAF aiming its running on a Raspberry Pi 3, keeping similar performance, generating the SIVQUAF(Compact. Besides redesigning and parallelizing algorithms, we proposed improvements in the classification and defect detection steps, and a new touch-sensitive interface. The experiments conducted with SIVQUAF(Compact embedded in a Raspberry Pi 3 demonstrated that in addition to reproducing high hit rates in the tasks of segmentation (97.50%), classification (97.06%) and detection of defects (74.78% ), there was a significant gain in terms of cost, operation and compaction of the equipment, increasing its operational, technical and economic viability.

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Index Terms

Computer Science
Information Sciences

Keywords

Embedded System Raspberry Computer Vision Visual Inspection Bean

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