Fabric defect detection based on computer vision

2Citations
Citations of this article
8Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Broken ends, missing picks, oil stain and holes are the most common fabric defects. To deal with the situation that manual fabric detection will affected by the subjective factors of inspectors, an automatic computer vision based fabric defect detection method is introduced in this paper. The system uses threshold segmentation method to identify if there are any defects existed in the fabric, adopts image feature based approach to recognize oil stain and holes, and uses training based technique to detect broken ends and missing picks. Experimental results show that the proposed approach has the advantage of easy implementation, high inspection speed, good noise immunity, greatly meeting the needs for automatic fabric defect inspection. © 2011 Springer-Verlag.

Cite

CITATION STYLE

APA

Sun, J., & Zhou, Z. (2011). Fabric defect detection based on computer vision. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7004 LNAI, pp. 86–91). https://doi.org/10.1007/978-3-642-23896-3_11

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free