面向织物疵点检测的深度学习技术应用研究进展  

Research progress in deep learning technology for fabric defect detection

作　　者：刘燕萍 郭佩瑶 吴莹 LIU Yanping;GUO Peiyao;WU Ying(School of Fashion Design&Engineering,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;Zhejiang Sci-Tech University Shengzhou Innovation Research Institute,Shengzhou,Zhejiang 312400,China;Zhejiang Engineering Research Center for Green and Low Carbon Technology and Industrialization of Modern Logistics,Wenzhou,Zhejiang 325000,China)

机构地区：[1]浙江理工大学服装学院,浙江杭州310018 [2]浙江理工大学嵊州创新研究院,浙江嵊州312400 [3]现代物流绿色低碳技术及产业化浙江省工程研究中心,浙江温州325000

出　　处：《纺织学报》2024年第12期234-242,共9页Journal of Textile Research

基　　金：浙江理工大学优秀研究生学位论文培育基金项目(LW-YP2024037);浙江省教育厅一般科研项目(Y202352846);浙江理工大学嵊州创新研究院科研项目(SYY2023B000003);浙江理工大学基本科研业务费青年创新专项项目(22076229-Y)。

摘　　要：为提高深度学习技术在疵点检测中的应用效率,推动纺织行业质量控制自动化与智能化发展。首先,对现有公开的疵点数据集进行整理,剖析织物疵点数据的现状及困境。其次,从监督学习、半监督学习和无监督学习三方面梳理了面向织物疵点检测的深度学习技术原理,对比各自的优缺点及适用场景。此外,对疵点检测领域常用的速度和精度评价指标进行了总结。最后,基于背景、检测方法及评价指标等多个维度,对深度学习各类网络在疵点检测任务中的实验结果进行了对比分析。结果表明,数据集质量是影响算法性能的关键因素。认为未来研究重点将是生成有织物纹理特性的高质量疵点,可自动标注的监督学习算法,以及提升无监督和半监督学习算法的检测性能。Significance Automatic fabric defect detection is one of the key aspects of digital quality control in the textile industry.At present,the domestic fabric defect detection is mostly based on manual detection,but the traditional manual detection success rate of only 60%-75%,indicating that the method can′t meet the demand for high-quality products.To overcome the drawbacks of manual defect detection,researchers have proposed a variety of learning-based defect detection algorithms.Compared with the manual detection,machine learning methods demonstrate a high detection rate,good stability and other characteristics.Bacause of the superiority of deep learning technology in defect detection,this technology is also used for fabric defect detection.In order to improve the efficiency of the application of deep learning technology in defect detection and to achieve digital quality control in the textile industry,the current status of research on deep learning technology in defect detection is discussed.Progress Although traditional algorithms have achieved imroved results in some specific applications,there are still limitations when dealing with complex fabric textures.With the upgrading of computer hardware,the technology is superior in the fields of target detection and image classification,and is utilized in textile quality inspection.Since the introduction of deep learning,great breakthroughs have been made in target detection,which can be categorized into one-phase detection model and two-phase detection model in textile defect detection,both achieving better results in detection speed and detection accuracy.Due to the excellent feature extraction capability of neural networks,convolutional neural network(CNN)based classification networks are widely used for surface defect detection and classification,which can automatically learn different types of fabric defects and accurately categorize them into different classes.Various deep learning methods are superior to manual detection.Due to the difficulty in obtaining f

关 键 词：织物疵点检测 深度学习 目标检测 疵点分类 图像分割 织物质量控制 

分 类 号：TS101.9[轻工技术与工程—纺织工程]