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

用于图案纺织品缺陷检测的新颖实时算法和系统架构

• 批准号: 104388
• 金额: $ 53.53万
• 依托单位: SHELTON MACHINES LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=104388
• 关键词: Novel; real; time; algorithms; system

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.

全球纺织业生产1700亿米的织物，市场价值6400亿英镑。25%的纺织品都是花纹织物，缺陷检测是一个主要的行业挑战。未能在缺陷容限内提供纺织品可能导致整批召回，导致昂贵的客户索赔和下游生产延迟，影响零售商的库存管理。不良的缺陷管理也是工业废物的主要来源。传统的缺陷检测方法依赖于无效的人工检查（<65%的检测）。虽然机器视觉系统为自动缺陷检测（>95%）和管理提供了复杂的平台，但这些系统仅限于平纹纺织品。由于与可变形衬底上的图案匹配相关联的极端复杂性（纺织品）和为每种图案类型训练神经网络所需的时间。因此，图案纺织品的制造商仅限于人工检测。该项目将在市场领先的平纹纺织品视觉系统的基础上，开发新的递归模板匹配算法，用于解决复杂的图案变形问题，实现有效的图案减法，从而揭示潜在的缺陷。视觉系统将提供：i）摄像机和照明系统，用于高速拍摄最佳图像（2）利用统计分析自动化系统配置新纺织产品的自我训练软件;（3）先进的图像分析检测缺陷>95%; iv）能够学习和自动化客户决策规则的缺陷分类系统; v）用于记录和检索完整的卷图图像以用于后续检查和质量控制的系统;以及vi）生成具有完整缺陷数据的纺织品卷图，以用于优化纺织品切割计划（增加产量）、改进的下游处理和质量保证。目前市场上还没有一种视觉系统能为图案化的纺织品提供这些功能。项目的主要重点在于开发新的递归模板匹配算法。这些将被集成到现有的视觉系统平台中，并通过与领先的纺织和服装制造商Burberry的工厂试验进行验证。该项目的一个重要里程碑将是在领先行业展览会（ITMA -巴塞罗那）上展示原型系统。图案化纺织品视觉系统的潜在可寻址市场约为10亿英镑。该合作伙伴关系的目标是在5年内实现约848万英镑的业务增长（累计销售额约为1690万英镑），从而创造42个新工作岗位，并产生30倍的投资回报率。