基于深度学习的织物疵点检测研究进展  

Research progress in fabric defect detection based on deep learning

作　　者：王斌 李敏[2,3] 雷承霖 何儒汉[2,3] WANG Bin;LI Min;LEI Chenglin;HE Ruhan(Engineering Research Center of Hubei Province for Clothing Information,Wuhan Textile University,Wuhan,Hubei 430200,China;Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion,Wuhan Textile University,Wuhan,Hubei 430200,China;School of Computer Science and Artificial Intelligence,Wuhan Textile University,Wuhan,Hubei 430200,China)

机构地区：[1]武汉纺织大学湖北省服装信息化工程技术研究中心,湖北武汉430200 [2]武汉纺织大学计算机与人工智能学院,湖北武汉430200 [3]武汉纺织大学纺织服装智能化湖北省工程研究中心,湖北武汉430200

出　　处：《纺织学报》2023年第1期219-227,共9页Journal of Textile Research

基　　金：中国高校产学研创新基金项目(2020HYA02015)。

摘　　要：为提高疵点检测的准确性和通用性,实现使用简洁而有效的形式对织物图像的特点和疵点的本质特征进行综合表达,首先,介绍了深度学习技术,对引入了深度学习的疵点检测方法进行综述,同时对深度学习与疵点检测的内在关系进行阐述;然后,分析总结了深度学习的概念及代表性的计算模型,并对引入深度学习的疵点检测方法进行归纳、总结和分类;最后,对典型的方法进行了分析,讨论了各种方法的优缺点,并对未来的研究趋势进行了展望。指出:随着深度学习的发展,探索更加通用的检测方法是推进深度学习在织物疵点检测领域应用的努力方向。Significance With the development of science and technology,the improvement of product quality is highly demanded.Although the technologies used in producing textile products have undergone revolutionary advancement which contributes to the textile quality dramatically,defects in textile products such as fabrics remain to be a reality.Fabric defect detection plays an important role in textile industry,and fabric defect detection technology based on deep learning has been paid more and more attention.This paper reports a research and development progress in fabric defect detection based on deep learning.Progress Deep learning is mainly composed of four steps,i.e.,defining model and loss function,training the model,finding optimization method and loop iteration.The research focus for fabric defect detection method based on deep learning mainly includes deep learning models such as convolution neural network(CNN)and automatic encoder(AE).The stack denoising automatic encoder based on Fisher criterion introduces deep learning into this field for the first time,which provides a new idea for the application of deep learning to the field of defect detection.Convolution neural network has achieved good results in the field of image recognition because of its strong nonlinear fitting ability.More precision-based detection algorithms based on candidate regions and more speed-based algorithms based on regression analysis are present.While the advantages of convolution neural network is exploited,other methods are used for exploring the possibility of combined use of models,and provide new ways for defect detection.Conclusion and Prospect Fabric defect detection methods based on deep learning in recent years are reviewed and summarized,and the effects of different models are compared in detail.Advantages,disadvantages and applicable scope of each model are analyzed,and future development of fabric defect detection method based on deep learning model is prospected.Deep learning models can improve the detection efficiency,but

关 键 词：深度学习 疵点检测 纺织品 神经网络 图像分割 机器视觉 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TS101.9[自动化与计算机技术—控制科学与工程]