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Automated Vision Based Defect Detection Using Gray Level Co-Occurrence Matrix For Beverage Manufacturing Industry

Defect inspection emerged as an important role for product quality monitoring process since it is a requirement of International Organization for Standardization (ISO) 9001. The used of manual inspection is impractical because of time consuming, human error, tiredness, repetitive and low productivit...

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Main Authors: Mohd Saad, Norhashimah, Abdullah, Abdul Rahim, Wan Hassan, Wan Haszerila, Abdul Rahman, Nor Nabilah Syazana, Ali, Nur Hasanah, Abdullah, I. N.
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2021
Online Access:http://eprints.utem.edu.my/id/eprint/25654/2/20771-39562-1-PB%20IJAI.PDF
http://eprints.utem.edu.my/id/eprint/25654/
https://ijai.iaescore.com/index.php/IJAI/article/view/20771/13233
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spelling my.utem.eprints.256542022-03-14T16:12:11Z http://eprints.utem.edu.my/id/eprint/25654/ Automated Vision Based Defect Detection Using Gray Level Co-Occurrence Matrix For Beverage Manufacturing Industry Mohd Saad, Norhashimah Abdullah, Abdul Rahim Wan Hassan, Wan Haszerila Abdul Rahman, Nor Nabilah Syazana Ali, Nur Hasanah Abdullah, I. N. Defect inspection emerged as an important role for product quality monitoring process since it is a requirement of International Organization for Standardization (ISO) 9001. The used of manual inspection is impractical because of time consuming, human error, tiredness, repetitive and low productivity. Small and medium enterprises (SMEs) are industries that having problems in maintaining the quality of their products due to small capital provided. Therefore, automatic inspection is a promising approach to maintain product quality as well as to resolve the existing problems related to delay outputs and cost burden. This article presents a computerized analysis to detect color concentration defects that occur in beverage production based on texture information provided by gray level co-occurrence matrix (GLCM). Based on the texture information, GLCM cross-section is computed to extract the parameters for features of color concentration. The distance value between two colors is then computed using co-occurrence histogram. The defect results either pass or reject is determined using Euclidean distance and rule-based classification. The experimental results show 100% accuracy which makes the proposed technique can implimented for beverage manufacturing inspection process. Institute of Advanced Engineering and Science 2021-12 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/25654/2/20771-39562-1-PB%20IJAI.PDF Mohd Saad, Norhashimah and Abdullah, Abdul Rahim and Wan Hassan, Wan Haszerila and Abdul Rahman, Nor Nabilah Syazana and Ali, Nur Hasanah and Abdullah, I. N. (2021) Automated Vision Based Defect Detection Using Gray Level Co-Occurrence Matrix For Beverage Manufacturing Industry. IAES International Journal of Artificial Intelligence, 10 (4). pp. 818-829. ISSN 2252-8938 https://ijai.iaescore.com/index.php/IJAI/article/view/20771/13233 10.11591/ijai.v10.i4.pp818-829
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Defect inspection emerged as an important role for product quality monitoring process since it is a requirement of International Organization for Standardization (ISO) 9001. The used of manual inspection is impractical because of time consuming, human error, tiredness, repetitive and low productivity. Small and medium enterprises (SMEs) are industries that having problems in maintaining the quality of their products due to small capital provided. Therefore, automatic inspection is a promising approach to maintain product quality as well as to resolve the existing problems related to delay outputs and cost burden. This article presents a computerized analysis to detect color concentration defects that occur in beverage production based on texture information provided by gray level co-occurrence matrix (GLCM). Based on the texture information, GLCM cross-section is computed to extract the parameters for features of color concentration. The distance value between two colors is then computed using co-occurrence histogram. The defect results either pass or reject is determined using Euclidean distance and rule-based classification. The experimental results show 100% accuracy which makes the proposed technique can implimented for beverage manufacturing inspection process.
format Article
author Mohd Saad, Norhashimah
Abdullah, Abdul Rahim
Wan Hassan, Wan Haszerila
Abdul Rahman, Nor Nabilah Syazana
Ali, Nur Hasanah
Abdullah, I. N.
spellingShingle Mohd Saad, Norhashimah
Abdullah, Abdul Rahim
Wan Hassan, Wan Haszerila
Abdul Rahman, Nor Nabilah Syazana
Ali, Nur Hasanah
Abdullah, I. N.
Automated Vision Based Defect Detection Using Gray Level Co-Occurrence Matrix For Beverage Manufacturing Industry
author_facet Mohd Saad, Norhashimah
Abdullah, Abdul Rahim
Wan Hassan, Wan Haszerila
Abdul Rahman, Nor Nabilah Syazana
Ali, Nur Hasanah
Abdullah, I. N.
author_sort Mohd Saad, Norhashimah
title Automated Vision Based Defect Detection Using Gray Level Co-Occurrence Matrix For Beverage Manufacturing Industry
title_short Automated Vision Based Defect Detection Using Gray Level Co-Occurrence Matrix For Beverage Manufacturing Industry
title_full Automated Vision Based Defect Detection Using Gray Level Co-Occurrence Matrix For Beverage Manufacturing Industry
title_fullStr Automated Vision Based Defect Detection Using Gray Level Co-Occurrence Matrix For Beverage Manufacturing Industry
title_full_unstemmed Automated Vision Based Defect Detection Using Gray Level Co-Occurrence Matrix For Beverage Manufacturing Industry
title_sort automated vision based defect detection using gray level co-occurrence matrix for beverage manufacturing industry
publisher Institute of Advanced Engineering and Science
publishDate 2021
url http://eprints.utem.edu.my/id/eprint/25654/2/20771-39562-1-PB%20IJAI.PDF
http://eprints.utem.edu.my/id/eprint/25654/
https://ijai.iaescore.com/index.php/IJAI/article/view/20771/13233
_version_ 1728055205194366976
score 13.211869

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