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

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

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Automatic inspection, Classification, Co-occurrence matrix, Color analysis, Texture analysis
Divisions:	Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Sabariah Ismail
Date Deposited:	14 Mar 2022 16:12
Last Modified:	14 Mar 2022 16:12
URI:	http://eprints.utem.edu.my/id/eprint/25654
Statistic Details:	View Download Statistic

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