Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23896-3_11 | AICI (3) |
Keywords | Field | DocType |
threshold segmentation method,automatic computer vision,common fabric defect,automatic fabric defect inspection,high inspection speed,missing pick,manual fabric detection,broken end,fabric defect detection method,computer vision | Computer vision,Pattern recognition,Segmentation,Computer science,Artificial intelligence,Feature based,Noise immunity | Conference |
Volume | ISSN | Citations |
7004 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Sun | 1 | 0 | 0.34 |
| Zhiyu Zhou | 2 | 18 | 5.32 |