An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine
Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detecti...
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Online Access: | https://doi.org/10.1177/0020294019858175 |
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doaj-bcb41e5e7b9b47e39f7b3a2f79a8ec692020-11-25T04:03:35ZengSAGE PublishingMeasurement + Control0020-29402019-09-015210.1177/0020294019858175An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machineYu Wu0Yanjie Lu1Laboratory Center, Guangzhou University, Guangzhou, P.R. ChinaSchool of Physics and Electronic Engineering, Guangzhou University, Guangzhou, P.R. ChinaDefects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%.https://doi.org/10.1177/0020294019858175 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yu Wu Yanjie Lu |
spellingShingle |
Yu Wu Yanjie Lu An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine Measurement + Control |
author_facet |
Yu Wu Yanjie Lu |
author_sort |
Yu Wu |
title |
An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine |
title_short |
An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine |
title_full |
An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine |
title_fullStr |
An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine |
title_full_unstemmed |
An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine |
title_sort |
intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine |
publisher |
SAGE Publishing |
series |
Measurement + Control |
issn |
0020-2940 |
publishDate |
2019-09-01 |
description |
Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%. |
url |
https://doi.org/10.1177/0020294019858175 |
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