Open AccessLow-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary Pattern (LBP)
Author(s) -
Noor Maizatulshima Binti Muhammad Sabri,
Hadadi Hamed,
Muzamir Isa,
N S Ghazali,
Zainah Ibrahim
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2129/1/012052
Subject(s) - low density polyethylene , local binary patterns , support vector machine , artificial intelligence , confusion matrix , pattern recognition (psychology) , random forest , computer science , sensitivity (control systems) , automation , sample (material) , histogram , machine learning , data mining , materials science , engineering , image (mathematics) , polyethylene , mechanical engineering , chemistry , chromatography , electronic engineering , composite material
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.