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2003, Pattern Recognition
In the textile manufacturing industry, fabric defect detection becomes a necessary and essential step in quality control. The investment in this field is more than economical when reduction in labor cost and associated benefits are considered. Moreover, the development of a wholly automated inspection system requires efficient and robust algorithms. To overcome this problem, in this paper, we present a new fabric defect detection scheme which uses the local homogeneity and neural network. Its first step consists in computing a new homogeneity image denoted as í µí°»-image. The second step is devoted to the application of the discrete cosine transform (DCT) to the í µí°»-image and the extraction of different representative energy features of each DCT block. These energy features are used by the back-propagation neural network to judge the existence of fabric defect. Simulations on different fabric images and different defect aspects show that the proposed method achieves an average accuracy of 97.35%.
Journal of Multimedia
Distinguishing Feature Selection for Fabric Defect Classification Using Neural Network2011 •
In this paper, it is aimed to compare the performance of spectral based fault detection methods in quality control by testing on the same environment. The most widely used spectral based approaches as Fourier Transform, Wavelet Transform, Gabor Transform were used to extract features of the faulty fabric samples. By using statistical functions feature selection was done so huge dimensionality of features was decreased. The selected features are taken as inputs for feed-forward network (with the back propagation algorithm) to classify faulty fabrics in categories; weft, wrap and oil. All computations were performed in Matlab program so as to satisfy all conditions as the same. The analyses' results show the Wavelet transform in the classification for three type defects was more efficient than the others, on other hand; Fourier transform in terms of processing time is faster than the others.
Textile industry is one of the largest and oldest sectors in the India and has a formidable presence in national economy in terms of output, investment and employment. Due to increasing demand for quality fabrics it is thus important to produce the defect free high quality fabric. Visual inspection system consumes a lot of time and are error prone. The price of the fabric is reduced to 45%-65% due to presence of various defects. The purpose of this paper is to automate the detection and classification of texture defects by computerize software. The proposed method uses a statistical based approach for the inspection and detection of the defect on woven/knitted fabric collected from the textile industry. In this the images are acquired, pre-processed, restored and normalized to extract the statistical feature using computer vision. The extracted features are given an input to the artificial neural network decision tree classifier to compute the weighted factor for detecting and classifying the type of defects. An automatic defect detection system can increase the texture defect detection percentage and will reduce the fabrication and labour cost and improves the quality of the product. Keywords: Defect detection, Statistical approach, Computer vision, Decision tree classifier, neural network. Call for Papers: https://sites.google.com/site/ijcsis/
Machine Vision and Applications
Classification and segmentation of vector flow fields using a neural network1997 •
The global market for textile industry is highly competitive nowadays. Quality control in production process in textile industry has been a key factor for retaining existence in such competitive market. Automated textile inspection systems are very useful in this respect, because manual inspection is time consuming and not accurate enough. Hence, automated textile inspection systems have been drawing plenty of attention of the researchers of different countries in order to replace manual inspection. Defect detection and defect classification are the two major problems that are posed by the research of automated textile inspection systems. In this paper, we perform an extensive investigation on the applicability of genetic algorithm (GA) in the context of textile defect classification using neural network (NN). We observe the effect of tuning different network parameters and explain the reasons. We empirically find a suitable NN model in the context of textile defect classification. We compare the performance of this model with that of the classification models implemented by others.
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