REAL-TIME DETECTION OF KNITTING FABRIC DEFECTS USING SHEARLET TRANSFORM

Year 2019, Volume: 29 Issue: 1, 1 - 10, 26.03.2019

Kazim Hanbay , Muhammed Fatih Talu Ömer Faruk Özgüven Dursun Öztürk

https://doi.org/10.32710/tekstilvekonfeksiyon.482888

Cited By: 6

Abstract

This paper proposes a vision-based
fabric inspection system for the circular knitting machine. Firstly, a
comprehensive fabric database called Fabric Defect Detection Database (FDDD) are
constructed. To extract significant features of fabric images, shearlet
transform is used. Means and variances are calculated from all subbands and
combined into a high-dimensional feature vector. The proposed system is evaluated
on a circular knitting machine in a textile factory. The real-time performance
analysis is only carried out by inspecting single jersey knitted fabric. Our
proposed system achieves the highest accuracy of 94.0% in the detection of single
jersey knitting fabric defects.

Keywords

Fabric defect detection, real-time inspection, texture classification

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