基于深度信念网络的织物疵点检测  

Fabric defect detection based on deep-belief network

作　　者：李杨 彭来湖[1,2] 李建强[2] 刘建廷 郑秋扬 胡旭东[1] LI Yang;PENG Laihu;LI Jianqiang;LIU Jianting;ZHENG Qiuyang;HU Xudong(Key Laboratory of Modern Textile Machinery&Technology of Zhejiang Province,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;Zhejiang Sci-Tech University Longgang Research Institute,Wenzhou,Zhejiang 325000,China)

机构地区：[1]浙江理工大学浙江省现代纺织装备技术重点实验室,浙江杭州310018 [2]浙江理工大学龙港研究院有限公司,浙江温州325000

出　　处：《纺织学报》2023年第2期143-150,共8页Journal of Textile Research

基　　金：浙江省博士后科研项目特别资助项目(ZJ2020004)。

摘　　要：为提高织物疵点检测精度和效率,提出了一种基于深度信念网络的织物疵点检测方法。用改进的受限玻尔兹曼机模型对深度信念网络进行训练,完成模型识别参数的构建。利用同态滤波方法对图像进行预处理,使疵点图像更加清晰,同时抑制了背景图像。以Python语言,基于TensorFlow框架构建深度信念网络模型,对织物疵点图像进行处理得到学习样本,确定模型激活函数后,分析了各模型参数对织物疵点检测准确率的影响规律,得到激活函数为Relu, Dropout值为0.3,预训练学习率为0.1,微调学习率为0.000 1,批训练个数为64时,模型参数值达到最优。最后,利用在无缝内衣机上采集到的各类疵点图像,对深度信念网络织物疵点检测模型进行验证。结果表明:所提出的织物疵点检测方法能够快速、有效地对织物疵点进行检测和分类识别,准确率达到98%。Objective In order to improve the quality of textile products, increase economic benefits, and reduce production costs in order to improve production efficiency, it is of great significance to achieve intelligent detection of fabric defects. A fabric defect detection method based on deep-belief network(DBN) is proposed, and the deep-belief network is trained by the improved restricted Boltzmann machine model to complete the construction of model recognition parameters, which can not only independently extract fabric image data features, screen effective information for transmission, but also have a short training time and fast model convergence speed.Method In order to make the best training effect of the model, this paper enriches the samples by using the data augmentation method to meet the training requirements of the DBN model. The homomorphic filtering method is used to preprocess the image to reduce the low frequency and increase the high frequency, and sharpen the details of the image edges, making the defective image clearer and suppressing the background image. In order to solve the overfitting problem of the model and improve the generalization ability of the model, the DBN-Dropout model is used to set the output information in the network to 0, the contrastive divergence method is employed to initialize the visual layer of the training sample, the activation probability of neurons in the hidden layer of the model is calculated, and the activation status of neurons in the hidden layer and the visual layer is assessed. In Python language, a DBN model is built based on the TensorFlow framework, and the learning samples are obtained by processing the fabric defect images. In the weft knitting laboratory of the Knitting Engineering Technology Research Center at Zhejiang Sci-Tech University, the area scan CCD camera was combined with the 6 mm focal length lens(FL-HC0614-2M) produced by Ricoh Corporation of Japan, and 200 images were collected of different types of plain weft knitted fabrics produced by the R

关 键 词：织物疵点检测 深度学习 深度信念网络 受限玻尔兹曼机 图像处理 

分 类 号：TS181.9[轻工技术与工程—纺织材料与纺织品设计] TP391.4[轻工技术与工程—纺织科学与工程]