Textiles and garment supply chains are enduring disruption due to the Covid-19 crises. Retailers have not been able to sell products through their bricks and mortar outlets. Supply chains are complex and globally dispersed. Production orders within the supply chains have been cancelled or curtailed, many of the geographic regions have not been able to offer government support for their factories. Manufacturing and Retail have been ranked by Statista as the 1st and 3rd most impacted sectors by Covid-19 in 2020 \[Statista-June-24th-2020\]. Our ecosystem combines both these sectors. Retail Brands create and control the supply chains. The dominant supply chain drivers are digitisation and sustainability. Retail Brands constantly visit their supply chain factories to monitor compliance, quality, delivery schedules, digitisation progress and sustainability processes. Digitisation enables remote monitoring, significantly decreasing their costs and travel carbon footprint. However the sector remains very traditional and labour intensive, so there has been resistance to change. Covid-19 is a catalyst for change, digitisation is accelerating rapidly. The second change driver is environmental sustainability. Most the supply chains are far from the end consumers meaning they are relatively out of sight. Societal awareness and pressure has been limited, however this is changing. Sustainability is now key for all retail brands. Quality Control (termed fabric inspection within the supply chain) is a key factor not only for digitisation, but also decreasing waste (sustainability) and improving yields (sustainability). Traditionally quality control has been very labour intensive and executed inconsistently (subjectively). c-tex and Shelton are global leaders in automated inline colour variation monitoring and defect detection. These are the core functions of fabric inspection. Since 2014 our technologies have been gaining traction among the pioneering textile producers and garment factories, enabling them to automate (digitise) fabric inspection. The sector technical leadership of both companies has already been furthered by InnovateUK to develop their technologies for automated quality checking of patterned fabrics in addition to solid colour fabrics. Therefore factories can now use c-tex and Shelton to digitally quality check their solid colour and patterned fabric. This technological offering is unique to c-tex and Shelton. However due to Covid-19 we now need to automate (digitise) our business model (customer journey). We have had a traditional business model of a face-to-face sales process and long duration onsite user on-boarding (installation, commissioning, training). We now need a non-travel automated customer journey so that our automated technology can be purchased, implemented and used. For our new customer journey we will use readily available remote working technologies where applicable, but we need to develop our proprietary technologies to enable remote installation, commissioning and machine learning. Key to this is to accelerate our AI capabilities for machine learning and self-commissioning of our sensor technologies. We recently started to collaborate at leadership level but we now need to move this onto collaboration between our technical teams. The development of a single interface combining both our technologies is also essential for our new customer journey.

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.

To develop an innovative, rapidly-deployable, self-configuring machine-vision inspection technology with unrivalled hardware/commissioning costs and performance for the wider textiles industry.

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