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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
N. N. S. A. Rahman
Universiti Teknikal Malaysia Melaka
Computer Science & IT Engineering

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Automated vision based defect detection using gray level co-occurrence matrix for beverage manufacturing industry Norhashimah Mohd Saad; A. R. Abdullah; W. H. W. Hasan; N. N. S. A. Rahman; N. H. Ali; I. N. Abdullah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp818-829

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.
Real-Time LCD Digit Recognition System N. M. Saad; N. S. M. Noor; A. R. Abdullah; O. Y. Fong; N. N. S. A. Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 2: May 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i2.pp402-411

Abstract

In recent years, the utilization of digital instruments in industries is quickly expanding. This is because digital instruments are typically more exact than the analog instruments, and easier to be read as they are hooked up to a liquid-crystal display (LCD). However, manual data entry from LCD display is tedious and less accurate. This paper proposes a real-time LCD digit recognition system for the industrial purposes. The system is interfaced with an IP webcam to capture the video frames from the LCD display. The digital data is pre-processed into grayscale and being cropped into a selected region of interest (ROI). Adaptive thresholding and morphological operation are applied for the digit segmentation process. Data extraction and characterization are done by utilizing neural network classifier. Finally, all the information are logged out to Microsoft Excel spreadsheet. The 90% accuracy is accomplished for 50 test images of various LCD display.
Co-Authors A. R. Abdullah I. N. Abdullah N. H. Ali N. M. Saad N. S. M. Noor Norhashimah Mohd Saad O. Y. Fong W. H. W. Hasan