A Review of Vision Based Defect Detection Using Image Processing Techniques for Beverage Manufacturing Industry

Rahman, Nor Nabilah Syazana Abdul; Saad, Norhashimah Mohd; Abdullah, Abdul Rahim; Ahmat, Norunnajjah

doi:10.11113/jt.v81.12505

Cited by 10 publications

(7 citation statements)

References 89 publications

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“…Computer vision-based detection is one of the most widely used methods for automatic defect detection [2], [3], [4], [5], [6], [7]. This technique involves image acquisition, defect detection, and classication.…”

Section: Introductionmentioning

confidence: 99%

“…Jing Yang et al [31] conducted a study that examines state-of-the-art deep learning methods in defect detection. The study classied defects in various products, such as electronic components, pipes, 1,2,3,4,5,6,7,8,9,10 The authors are with the VNU University of Engineering and Technology, Vietnam, Email: anh.ph@vnu.edu.vn, duongtanrb@gmail.com, nguyenmaicg03@gmail.com, 147anhnt@gmail.com, thuhangkt01@gmail.com, thangluu3333@gmail.com, hieuvandang3210@gmail.com, dongtran.robotics@gmail.com, vanntt@vnu.edu.vn and tungbt@vnu.edu.vn 10 Corresponding author: tungbt@vnu.edu.vn welded parts, and textile materials, into categories and reviewed the main techniques and deep learning methods for defects, highlighting their characteristics, strengths, and shortcomings. Tian Wang et al [32] proposed a deep convolutional neural network (CNN) for automated quality visual inspection to control product quality for increased efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of an Autonomous Component Testing System with Reliability Improvement Using Computer Vision and Machine Learning

Phan,

Duong,

Thi

et al. 2024

ECTI-CIT Transactions

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This study evaluated computer vision-based models, including Histogram Analysis, Logistic Regression, Sift-SVM, and Deep learning models, in an autonomous testing system developed for smartphone camera modules. System performance was assessed in a practical factory setting with workers operating the system, and metrics such as processing time, sensitivity, specificity, accuracy, and defect rate were evaluated. Based on the results, the Sift-SVM model demonstrated the greatest potential for enhancing the reliability of the system with a processing time of 0.01578 seconds, a sensitivity of 99.811%, and a reduction in the failure rate to 1888 PPM. The study findings suggest that Sift-SVM has the potential to be practically applied in the industry, thus improving the speed and accuracy of automatic defect detection in manufacturing and reducing the defect rate.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of an Autonomous Component Testing System with Reliability Improvement Using Computer Vision and Machine Learning

Phan,

Duong,

Thi

et al. 2024

ECTI-CIT Transactions

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“…Photogrammetry with stereo vision can be divided into two categories according to the field of view. The first category is to measure indoors with small field of view, such as defect analysis of products in a factory [1]- [5]. Generally, the measuring error of this category is below 1cm [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of measuring accuracy for planar and non-planar scenes in photogrammetry

Guo

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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Photogrammetry with stereo vision is widely used in computer vision and SLAM (simultaneous localization and mapping), whose key steps are calibration and intersection measurement. Calibration is to obtain the intrinsic and extrinsic parameters, including the principal point, focal length and pose. Intersection measurement is to obtain the 3D information after calibration, including position, velocity and rotation. In some cases, such as visual monitoring cameras (VMCs), photogrammetry uses large field of view, and has the characteristics of long distance from camera to target and wide measuring range, which increase the difficulty of calibration and is unable to place 3D control points arbitrarily. What's more, the distance from the target area to 3D control point area has a great influence on the measuring accuracy of intersection measurement. In this paper, we proposed a new method to place 3D control points, including planar and non-planar scenes and this method can distinguish the two scenes. Then the planar and non-planar methods can be used to calibrate in different cases respectively. In addition, we analyzed the layout of 3D control points to obtain relation between the measuring accuracy and the distance from the target area to 3D control point area. Experimental results show the longer the distance, the greater the measuring error in synthetic data and real images, and to improve the measuring accuracy, the 3D control points should be planar or non-planar strictly, not quasi-planar.

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“…Inspection process has been widely used in real-world applications to detect defect on the product, especially in color concentration defects [1]. The high demand for the product in the market has made the industry to produce a product in a shorter time with less product quality.…”

Section: Introductionmentioning

confidence: 99%

“…Then, all of these histograms will be combined into one histogram for the classification process. In determining the distance value of two histograms, bin 1 to bin 27 is taken to be calculated using Euclidian distance formula as indicated in (1). The distance value will differentiate and classify the color of the images either pass or failed.…”

mentioning

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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A Review of Vision Based Defect Detection Using Image Processing Techniques for Beverage Manufacturing Industry

Cited by 10 publications

References 89 publications

Development of an Autonomous Component Testing System with Reliability Improvement Using Computer Vision and Machine Learning

Development of an Autonomous Component Testing System with Reliability Improvement Using Computer Vision and Machine Learning

Analysis of measuring accuracy for planar and non-planar scenes in photogrammetry

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

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