Fabric Defect Detection Based on SRG-PCNN

Abstract:

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Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors. According to the different features between the normal fabric image and defect image, this paper presents an adaptive image segmentation method based on a simplified region growing pulse coupled neural network (SRG-PCNN) for detecting fabric defects. The validation tests on the developed algorithms were performed with fabric images, and results showed that SRG-PCNN is a feasible and efficient method for defect detection.

Info:

Periodical:

Advanced Materials Research (Volumes 148-149)

Edited by:

Xianghua Liu, Zhengyi Jiang and Jingtao Han

Pages:

1319-1326

DOI:

10.4028/www.scientific.net/AMR.148-149.1319

Citation:

X. S. Si et al., "Fabric Defect Detection Based on SRG-PCNN", Advanced Materials Research, Vols. 148-149, pp. 1319-1326, 2011

Online since:

October 2010

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Price:

$35.00

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