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Proceedings Paper

Fabric surface detection using small sample learning based on naive Bayes
Author(s): Song Lin; Zhiyong He; Hao Zhang
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Paper Abstract

Fabric defect detection, a popular topic in automation, is a necessary and essential step of quality control in the textile manufacturing industry. Traditional machine learning algorithms such as deep learning always require a large number of training samples in fabric defect detection. However, fabric defect rate has been greatly decreased because of production technology has been developed further. An algorithm called Bayesian Small Sample Learning (BSSL) based on Naive Bayes was proposed to solve the problem of lack of training samples. Firstly, it is important to remove the noise in the image which collected from experiment platform. After that, the reference values are obtained by learning few samples of different defective fabrics and defect-free fabrics. Finally, the feature values need to be extracted from the fabric to be detected and Bayesian algorithm is used to calculate the posterior probability which the reference values to the feature values when the learning process completed. The fabric was detected as defective or not determined by maximum posterior probability. Experimental results show that the proposed algorithm BSSL requires few defective samples for learning and also can achieve high accuracy of detection.

Paper Details

Date Published: 9 August 2018
PDF: 9 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061E (9 August 2018); doi: 10.1117/12.2503184
Show Author Affiliations
Song Lin, Soochow Univ. (China)
Zhiyong He, Soochow Univ. (China)
Hao Zhang, Soochow Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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