Automatic defect detection inspectacles and glass bottles based on Fuzzy C Means Clustering

George, Jaina; Janardhana, S.; Jain, Jaya; Sabareesaan, K. J.

doi:10.1109/icctet.2013.6675901

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…Similarly, machine vision-based product surface defect detection has applications in many industries, such as steel plates [40], glass [41], printing [42], parts [43], rail [44], fruit [45] and so on.…”

Section: A Product Surface Defect Detectionmentioning

confidence: 99%

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Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Yang

Wang

Jiang

et al. 2021

IEEE Trans. Ind. Inf.

195

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Visual perception refers to the process of organizing, identifying and interpreting visual information in environmental awareness and understanding. With the rapid progress of multimedia acquisition technology, research on visual perception has been a hot topic in the academical field and industrial applications. Especially after the introduction of artificial intelligence theory, intelligent visual perception has been widely used to promote the development of industrial production towards intelligence. In this paper, we review the previous research and application of visual perception in different industrial fields such as product surface defect detection, intelligent agricultural production, intelligent driving, image synthesis and event reconstruction. The applications basically cover most of the intelligent visual perception processing technologies. Through this survey, it will provide a comprehensive reference for research on this direction. Finally, this paper also summarizes the current challenges of visual perception and predicts its future development trends.

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Section: A Product Surface Defect Detectionmentioning

confidence: 99%

“…Textile [27], [29]- [31], [33]- [35], [37]- [39] Transportation [44] Food [45] Printing [42] Industrial manufacturing [40], [41], [43] Object detection [29]- [31] Object classification [32]- [35] CNN-based object detection and classification [37]- [39] Agricultural Production Intelligence…”

Section: A Vision Acquisitionmentioning

confidence: 99%

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Yang

Wang

Jiang

et al. 2021

IEEE Trans. Ind. Inf.

195

View full text Add to dashboard Cite

show abstract

“…In this technique, the rate of defect detection is 100%, 91.6%, and 94.4% respectively for the types of samples selected which includes vials with crack, missing edges in vials and dirty vials. In the technique proposed by Jaina George et al [11] has the idea of using Fuzzy C Means Clustering for detecting glass bottle flaws. It has unsupervised model building and data analysis.…”

Section: Introductionmentioning

confidence: 99%

Crack Detection of Pharmaceutical Vials Using Agglomerative Clustering Technique

Vishwanatha

Asha

2023

IoT, Cloud and Data Science

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Pharmaceutical industries remain very profitable but defects in medicine vials are causing losses and adding extra overhead in quality management. In order to minimize these losses and overheads, companies need to find new ways of doing quality management for every vial produced. This paper presents a method for finding cracks on the vials using Agglomerative Clustering Technique. The technique successfully detects all types of cracks on the vials. The algorithm has achieved 100% accuracy in detection of cracks on the Pharmaceutical Vials and can have potential application in pharmaceutical industries in quality control.

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“…A number of different methods have been employed in glass defect analysis and many studies for effective control are present in literature. In order to detect the surface defects on the glass, Markov random field [6] Otsu threshold method [7], canny edge detection method [8], binary feature histogram method [9], Fuzzy C Means clustering algorithm [10] have been employed. In this study, the images taken from homogenously illuminated medium are first preprocessed to be denoised, processed via wavelet transform and transformed into binary form by Shannon method.…”

Section: Introductionmentioning

confidence: 99%

Glass Surface Defects Detection with Wavelet Transforms

Akdemïr¹,

Öztürk²

2015

IJMMM

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Abstract-Nowadays, the increasing population increases the consumption as well. Manufacturing systems are developing in a fast pace to meet increasing demand of consumption. However, very quick increase of production has surpassed the development speed of currently existing control systems. In manufacturing, since the quality is a very important issue as well as the quantity, the operation of quality control systems must be accelerated and must be accomplished by machines. The idea of our study is based on this thinking. Especially defect detection on widely used glass material that is extremely difficult to accomplish by humans can be implemented in a quick, accurate and stable way. In the presented method, various defects like scratches, bubbles, cracks, corrosion on the glass surface can be identified. Glass images obtained from a homogenously illuminated medium are processed by wavelet transform and obtained images from the wavelet transform are denoised. Finally, Shannon threshold method is applied to the images. From the obtained results, defects like scratches, bubbles on the surface of the glass can be detected successfully.Index Terms-Glass defect detection, texture analysis, image processing, wavelet transform, machine vision.

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Automatic defect detection inspectacles and glass bottles based on Fuzzy C Means Clustering

Cited by 7 publications

References 5 publications

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Crack Detection of Pharmaceutical Vials Using Agglomerative Clustering Technique

Glass Surface Defects Detection with Wavelet Transforms

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