Robust defect detection in plain and twill fabric using directional Bollinger bands

Henry Y. T. Ngan; Grantham K. H. Pang

doi:10.1117/1.OE.54.7.073106

13 July 2015 Robust defect detection in plain and twill fabric using directional Bollinger bands

Henry Y. T. Ngan, Grantham K. H. Pang

Author Affiliations +

Optical Engineering, Vol. 54, Issue 7, 073106 (July 2015). https://doi.org/10.1117/1.OE.54.7.073106

Abstract

A directional Bollinger bands (BB) method for the detection of defects in plain and twill fabric is presented, whereas a previous BB method was for patterned Jacquard fabric. BB are constructed using the moving average and standard deviation to characterize any irregularities (i.e., defects) in a patterned texture. Every patterned texture constitutes a primitive unit that can be used to generate the texture by a translational rule. The regularity property for a patterned texture can be implicitly regarded as the periodic signals on the rows and columns of an image. To utilize such a regularity property, an embedded shift-invariant characteristic of BB is explored. The original BB method is further developed using directional rotation iterations, which enables the detection of directional defects in plain and twill fabric. The directional BB method is an efficient, fast, and shift-invariant approach that enables defective regions to be clearly outlined. This approach is also immune to the alignment problem that often arises in the original method. The detection accuracies for 77 defective images and 100 defect-free images are 96.1% and 96%, respectively. In a pixel-to-pixel evaluation comparing the detection results of the defective images with the ground-truth images, a 93.51% detection success rate is achieved.

Citation Download Citation

Henry Y. T. Ngan and Grantham K. H. Pang "Robust defect detection in plain and twill fabric using directional Bollinger bands," Optical Engineering 54(7), 073106 (13 July 2015). https://doi.org/10.1117/1.OE.54.7.073106

Published: 13 July 2015

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available