CNN-based minor fabric defects detection

Abstract

Purpose

The purpose of this paper is to present a novel method for minor fabric defects detection.

Design/methodology/approach

This paper proposes a PETM-CNN algorithm. PETM-CNN is designed based on self-similar estimation algorithm and Convolutional Neural Network. The PE (Patches Extractor) algorithm extracts patches that are possible to be defective patches to preprocess the fabric image. Then a TM-CNN (Triplet Metric CNN) method is designed to predict labels of the patches and the final label of the image. The TM-CNN can perform better than normal CNN.

Findings

This algorithm is superior to other algorithms on the data set of fabric images with minor defects. The proposed method achieves accurate classification of fabric images whether it has minor defects or not. The experimental results show that the approach is effective.

Originality/value

Traditional fabric defects detection is not effective as minor defects detection, so this paper develops a method of minor fabric images classification based on self-similar estimation and CNN. This paper offers the first investigation of minor fabric defects.

Citation

Wen, Z., Zhao, Q. and Tong, L. (2021), "CNN-based minor fabric defects detection", International Journal of Clothing Science and Technology, Vol. 33 No. 1, pp. 1-12. https://doi.org/10.1108/IJCST-11-2019-0177