Low-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary Pattern (LBP)

The motivation of this research is to automate the current food packaging inspection process by implementing the non-destructive approach. The current practices require human intervention where human vision tends to overlook the faulty on the package resulting in accuracy dilemma. Human also may be...

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Published in:Journal of Physics: Conference Series
Main Author: Sabri N.; Hamed H.N.A.; Isa M.A.; Ghazali N.S.; Ibrahim Z.
Format: Conference paper
Language:English
Published: IOP Publishing Ltd 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123734678&doi=10.1088%2f1742-6596%2f2129%2f1%2f012052&partnerID=40&md5=9dfc55c6b00b52a0115d8c5c250aa564
id 2-s2.0-85123734678
spelling 2-s2.0-85123734678
Sabri N.; Hamed H.N.A.; Isa M.A.; Ghazali N.S.; Ibrahim Z.
Low-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary Pattern (LBP)
2021
Journal of Physics: Conference Series
2129
1
10.1088/1742-6596/2129/1/012052
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123734678&doi=10.1088%2f1742-6596%2f2129%2f1%2f012052&partnerID=40&md5=9dfc55c6b00b52a0115d8c5c250aa564
The motivation of this research is to automate the current food packaging inspection process by implementing the non-destructive approach. The current practices require human intervention where human vision tends to overlook the faulty on the package resulting in accuracy dilemma. Human also may be exhausted due to repeated activities. This paper provides the primary phase for effective automation of image classification solution implemented using Weka software. An evaluation of the performance of the Support Vector Machine (SVM), K-nearest Neighbour (KNN) and Random Forest (RF) classification models for Low-Density Polyethylene (LDPE) food packaging defect image classification using a small sample of dataset and Linear Binary Pattern (LBP) as feature extraction algorithm is investigated. Four criteria have been used to evaluate the performance of each classification model which is accuracy, sensitivity, specificity and precision obtained from the confusion matrix table. The results indicate that SVM performs better than RF and KNN with 95% accuracy, 95% sensitivity, 72% specificity and 95% precision in classifying LDPE food packaging defect images. © 2021 Institute of Physics Publishing. All rights reserved.
IOP Publishing Ltd
17426588
English
Conference paper
All Open Access; Gold Open Access
author Sabri N.; Hamed H.N.A.; Isa M.A.; Ghazali N.S.; Ibrahim Z.
spellingShingle Sabri N.; Hamed H.N.A.; Isa M.A.; Ghazali N.S.; Ibrahim Z.
Low-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary Pattern (LBP)
author_facet Sabri N.; Hamed H.N.A.; Isa M.A.; Ghazali N.S.; Ibrahim Z.
author_sort Sabri N.; Hamed H.N.A.; Isa M.A.; Ghazali N.S.; Ibrahim Z.
title Low-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary Pattern (LBP)
title_short Low-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary Pattern (LBP)
title_full Low-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary Pattern (LBP)
title_fullStr Low-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary Pattern (LBP)
title_full_unstemmed Low-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary Pattern (LBP)
title_sort Low-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary Pattern (LBP)
publishDate 2021
container_title Journal of Physics: Conference Series
container_volume 2129
container_issue 1
doi_str_mv 10.1088/1742-6596/2129/1/012052
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123734678&doi=10.1088%2f1742-6596%2f2129%2f1%2f012052&partnerID=40&md5=9dfc55c6b00b52a0115d8c5c250aa564
description The motivation of this research is to automate the current food packaging inspection process by implementing the non-destructive approach. The current practices require human intervention where human vision tends to overlook the faulty on the package resulting in accuracy dilemma. Human also may be exhausted due to repeated activities. This paper provides the primary phase for effective automation of image classification solution implemented using Weka software. An evaluation of the performance of the Support Vector Machine (SVM), K-nearest Neighbour (KNN) and Random Forest (RF) classification models for Low-Density Polyethylene (LDPE) food packaging defect image classification using a small sample of dataset and Linear Binary Pattern (LBP) as feature extraction algorithm is investigated. Four criteria have been used to evaluate the performance of each classification model which is accuracy, sensitivity, specificity and precision obtained from the confusion matrix table. The results indicate that SVM performs better than RF and KNN with 95% accuracy, 95% sensitivity, 72% specificity and 95% precision in classifying LDPE food packaging defect images. © 2021 Institute of Physics Publishing. All rights reserved.
publisher IOP Publishing Ltd
issn 17426588
language English
format Conference paper
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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