Fabric surface detection using small sample learning based on naive Bayes

Song Lin; Zhiyong He; Hao Zhang

doi:10.1117/12.2503184

9 August 2018 Fabric surface detection using small sample learning based on naive Bayes

Song Lin, Zhiyong He, Hao Zhang

Author Affiliations +

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108061E (2018) https://doi.org/10.1117/12.2503184
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

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.

Citation Download Citation

Song Lin, Zhiyong He, and Hao Zhang "Fabric surface detection using small sample learning based on naive Bayes", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061E (9 August 2018); https://doi.org/10.1117/12.2503184

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