An automated inspection system for textile fabrics based on Gabor filters
Robotics and Computer-Integrated Manufacturing
A Simplified pulse-coupled neural network for adaptive segmentation of fabric defects
Machine Vision and Applications
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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.