Automatic Unsupervised Fabric Defect Detection Based on Self-Feature Comparison

Zhengrui Peng,Xinyi Gong,Xiangyi Xu,Bengang Wei,Shixiong Meng

doi:10.3390/electronics10212652

Abstract

Due to the huge demand for textile production in China, fabric defect detection is particularly attractive. At present, an increasing number of supervised deep-learning methods are being applied in surface defect detection. However, the annotation of datasets in industrial settings often depends on professional inspectors. Moreover, the methods based on supervised learning require a lot of annotation, which consumes a great deal of time and costs. In this paper, an approach based on self-feature comparison (SFC) was employed that accurately located and segmented fabric texture images to find anomalies with unsupervised learning. The SFC architecture contained the self-feature reconstruction module and the self-feature distillation. Accurate fiber anomaly location and segmentation were generated based on these two modules. Compared with the traditional methods that operate in image space, the comparison of feature space can better locate the anomalies of fiber texture surfaces. Evaluations were performed on the three publicly available databases. The results indicated that our method performed well compared with other methods, and had excellent defect detection ability in the collected textile images. In addition, the visual results showed that our results can be used as a pixel-level candidate label.

Highlights

Compared with the traditional methods operating in the image space, the comparison of feature space can better locate the anomalies of fiber texture surfaces
A unified model for texture defect inspection in industrial applications was designed in this paper, and was driven in an unsupervised learning fashion
The anomalies were detected via a combined anomaly score based on feature reconstruction and feature distillation

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The textile industry occupies a significant proportion of China’s industry, and its products are widely used in homes, clothing, construction, and even aerospace. The surface quality of products is an important factor that affects their price and grade evaluation [1]. In the manufacturing process, the industry arranges an inspection process to ensure that flawless products are delivered to merchants or consumers. The traditional detection methods use a human to detect the surface defects, which is slow, and cannot ensure the consistency of the detection effect. With the popularization of automatic product lines, automatic fabric defect detection equipment based on machine vision is increasingly being applied in fabric defect detection

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