A Method for Defect Detection of Yarn-Dyed Fabric Based on Frequency Domain Filtering and Similarity Measurement

Open access

Abstract

The detection of defects in yarn-dyed fabric is one of the most difficult problems among the present fabric defect detection methods. The difficulty lies in how to properly separate patterns, textures, and defects in the yarn-dyed fabric. In this paper, a novel automatic detection algorithm is presented based on frequency domain filtering and similarity measurement. First, the separation of the pattern and yarn texture structure of the fabric is achieved by frequency domain filtering technology. Subsequently, segmentation of the periodic units of the pattern is achieved by using distance matching function to measure the fabric pattern. Finally, based on the similarity measurement technology, the pattern’s periodic unit is classified, and thus, automatic detection of the defects in the yarn-dyed fabric is accomplished.

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Autex Research Journal

The Journal of Association of Universities for Textiles (AUTEX)

Journal Information


IMPACT FACTOR 2017: 0.957
5-year IMPACT FACTOR: 1.027

CiteScore 2017: 1.18

SCImago Journal Rank (SJR) 2017: 0.448
Source Normalized Impact per Paper (SNIP) 2017: 1.465

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