Novel real-time algorithms and system architecture for defect detection on patterned textiles

The global textile industry produces \>170 billion metres of fabric generating a market value of \>£640 Billion. \>25% of textiles are patterned fabrics.

Defect detection represents a major industry challenge. Failure to provide textiles within defect tolerance limits can lead to whole batch recalls, resulting in costly customer claims and downstream production delays impacting on retailer stock management. Poor defect management is also a major source of industrial waste.

Traditional methods for defect detection rely on human inspection which is ineffective (<65% detection). Whilst machine vision systems offer sophisticated platforms for automated defect detection (\>95%) and management; these systems are restricted to plain textiles.

Whilst pattern matching and neural network approaches have previously been tried for patterned textiles; all have failed to provide a practical solution due to the extreme complexities associated with pattern matching on deformable substrates (textiles) and the time required to train a neural network for each pattern type. Manufacturers of patterned textiles are therefore limited to manual inspection.

Building on a market leading vision system for plain textiles, the project will develop novel recursive template matching algorithms for the resolution of complex pattern deformations, enabling efficient pattern subtraction and thereby revealing underlying defects.

The vision system will offer: i) camera and lighting system for optimum image capture at high speed (\>100m/min); ii) self-training software utilising statistical analysis to automate system configuration for new textile products; iii) advanced image analysis for detection of \>95% defects; iv) defect classification system able to learn and automate client decision rules; v) system for recording and retrieval of complete roll map images for subsequent review and quality control; and vi) generation of textile roll maps with complete defect data, for optimised textile cut plan (increased yield), improved downstream processing, and quality assurance. No vision system on the market offers these features for patterned textiles.

The main areas of project focus lie in development of the novel recursive template matching algorithms. These will be integrated into the existing vision system platform and validated through factory trials with leading textile and clothing manufacturer Burberry. An important milestone in the project will be demonstration of a prototype system at the leading sector exhibition (ITMA - Barcelona).

The potential addressable market for patterned textile vision systems is ~£1 billion. The partnership targets ~£8.48 million business growth within a 5 year period (~£16.9 million cumulative sales) thereby creating \>42 new jobs and generating a \>30-fold ROI.