A Paper on Automatic Fabrics Fault Processing Using Image Processing Technique In MATLAB

Thilepa, R.; Thanikachalam, M

doi:10.5121/sipij.2010.1208

Cited by 8 publications

(5 citation statements)

References 2 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Monadjemi [7], [8] proposed the application of structurally matched eigenfilters generated by rotation, negation and mirroring for texture defect detection. In 2010, Thilepa [9] used histogram equalization with noise filtering and thresholding to detect fabric defect and 85% accuracy was attained.…”

Section: A Statistical Approachesmentioning

confidence: 99%

“…This is because in this system, unlike the traditional system, the operator is reviewing the detected defect while the system is stationary, and he knows the location of that defect. The results from Tables 8,9 show that the CTR method is the most effective one, as it was expected, because of the greater cooperation from the operator. In comparison, the RR method obtains less TABLE 3: Accuracy, loss, recall and precision of the proposed model and state-of-the-art defect detection techniques in different datasets with the respective average (between 0 and 1).…”

Section: Cnn Detects the Defective Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Fabric Defect Detection With Deep Learning and False Negative Reduction

2021

View full text Add to dashboard Cite

Quality control is an area of utmost importance for fabric production companies. By not detecting the defects present in the fabrics, companies are at risk of losing money and reputation with a damaged product. In a traditional system, an inspection accuracy of 60-75% is observed. In order to reduce these costs, a fast and automatic defect detection system, which can be complemented with the operator decision, is proposed in this paper. To perform the task of defect detection, a custom Convolutional Neural Network (CNN) was used in this work. To obtain a well-generalized system, in the training process, more than 50 defect types were used. Additionally, as an undetected defect (False Negative -FN) usually has a higher cost to the company than a non-defective fabric being classified as a defective one (false positive), FN reduction methods were used in the proposed system. In testing, when the system was in automatic mode, an average accuracy of 75% was attained; however, if the FN reduction method was applied, with intervention of the operator, an average of 95% accuracy can be achieved. These results demonstrate the ability of the system to detect many different types of defects with good accuracy whilst being faster and computationally simple.

show abstract

Section: A Statistical Approachesmentioning

confidence: 99%

Section: Cnn Detects the Defective Featuresmentioning

confidence: 99%

Fabric Defect Detection With Deep Learning and False Negative Reduction

2021

View full text Add to dashboard Cite

show abstract

“…Independent Component Analysis: ICA wishes to find a linear transformation of the original data such that the new depiction is one that reduces the reliance of the components present in the representation on the statistical basis. The representation achieved by ICA ease the analysis of the data experienced in such fields like, data compression, pattern recognition, de-noising [6]. 6.…”

Section: Model-based Approachesmentioning

confidence: 99%

Identification and Classification of Fabric Defects.

Singh¹,

Kaleka²

2016

IJAR

View full text Add to dashboard Cite

“…The process of traditional inspection is time-consuming and prone to errors because human inspectors are using primitive methods [1]. In order to cope these problems, object recognition technique has been used for recognizing paper fault [2] and inspection of mechanical parts [3]. In compact disc manufacturing, object recognition has been implemented for defect recognition application.…”

Section: Background Studymentioning

confidence: 99%

Compact Disc Visual Inspection Using Neural Network

Prabuwono

Zulkipli

Harahap

et al. 2012

AMR

View full text Add to dashboard Cite

Image processing is widely used in various fields of study including manufacturing as product inspection. In compact disc manufacturing, image processing has been implemented to recognize defect products. In this research, we implemented image processing technique as pre-processing processes. The aim is to acquire simple image to be processed and analyzed. In order to express the object from the image, the features were extracted using Invariant Moment (IM). Afterward, neural network was used to train the input from IM’s results. Thus, decision can be made whether the compact disc is accepted or rejected based on the training. Two experiments have been done in this research to evaluate 40 datasets of good and defective images of compact discs. The result shows that accuracy rate increased and can identify the quality of compact discs based on neural network training.

show abstract

A Paper on Automatic Fabrics Fault Processing Using Image Processing Technique In MATLAB

Cited by 8 publications

References 2 publications

Fabric Defect Detection With Deep Learning and False Negative Reduction

Fabric Defect Detection With Deep Learning and False Negative Reduction

Identification and Classification of Fabric Defects.

Compact Disc Visual Inspection Using Neural Network

Contact Info

Product

Resources

About