Shape defect detection for product quality inspection and monitoring system

Saad, Norhashimah Mohd; Rahman, Nor Nabilah Syazana Abdul; Abdullah, Abdul Rahim; Wahab, Farhan Abdul

doi:10.1109/eecsi.2017.8239109

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…The image intensity value which has above threshold value is characterized as a foreground value and the remaining pixels are a background value. Morphological operation is the process that utilizing of erosion and dilation [21]. The operations of morphological are computed to remove small pixels and to create the same size of the output image.…”

Section: Image Acquisition and Pre-processing Stagementioning

confidence: 99%

Automated vision based defect detection using gray level co-occurrence matrix for beverage manufacturing industry

Saad

Abdullah

Hasan

et al. 2021

IJ-AI

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<span>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).</span><span> 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.</span>

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Section: Image Acquisition and Pre-processing Stagementioning

confidence: 99%