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Proceedings Paper

Nonwoven fabric crease defects detection based on Gabor filter
Author(s): Shaoqing Han; Feifei Gu
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Paper Abstract

The defect detection of nonwoven fabrics is one of the most important steps of fabric quality assurance on production lines. For a long time, fabric defects detection has been carried out manually by human vision with an accuracy of about 60–70%, which not only affects the health of the inspectors, but also has high inspection cost. How to automatically detect crease defects of various forms at a high accuracy has been a challenging task in the field of machine vision. At present, Fourier transform and wavelet transform have been adopted to solve this problem. However, both of them can hardly detect stochastic textured in local region from different scales and directions. This paper adopted a 2D Gabor filter-based method to detect the crease defects, which has tunable angular and axial frequency bandwidth, tunable center frequencies, and could achieve optimal joint resolution in spatial and frequency domain. Firstly the fabric crease images are transformed from the spatial domain to the frequency domain. Secondly the frequency domain images are filtered by the Gabor filter with adjustable central frequency, bandwidth and azimuth, and the frequency domain images of the crease pattern are selected in the frequency domain image. Then they are reversed to the spatial domain. Finally the crease area of nonwoven fabric is obtained by the blob analysis. Experiments conducted on various forms of crease defects have shown that by adopting the proposed method, the nonwoven fabric’s crease defects can be detected effectively and accurately.

Paper Details

Date Published: 21 June 2019
PDF: 7 pages
Proc. SPIE 11061, Automated Visual Inspection and Machine Vision III, 110610H (21 June 2019); doi: 10.1117/12.2525959
Show Author Affiliations
Shaoqing Han, Shenzhen Govison Intelligent Vision Technology CO.,LTD (China)
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (China)
Feifei Gu, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 11061:
Automated Visual Inspection and Machine Vision III
Jürgen Beyerer; Fernando Puente León, Editor(s)

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