基于近红外光谱和残差神经网络的异性纤维分类识别  

Classification and identification of foreign fibers based on near-infrared spectroscopy and ResNet

作　　者：李学良 杜玉红[1,2] 任维佳 左恒力 LI Xueliang;DU Yuhong;REN Weijia;ZUO Hengli(School of Mechanical Engineering,Tiangong University,Tianjin 300387,China;Key Laboratory of Modern Mechanical and Electrical Equipment Technology,Tiangong University,Tianjin 300387,China)

机构地区：[1]天津工业大学机械工程学院,天津300387 [2]天津工业大学天津市现代机电装备技术重点实验室,天津300387

出　　处：《纺织学报》2023年第5期84-92,共9页Journal of Textile Research

基　　金：国家自然科学基金项目(51205288);天津市自然科学基金项目(17JCYBJC19400)。

摘　　要：针对传统图像处理方法对棉层中异性纤维检测效果不佳的问题,基于近红外光谱和残差神经网络提出一种对棉层中异性纤维的分类识别方法。采用Savitzky-Golay法对异性纤维的近红外光谱数据进行平滑处理,结合F检验和LightGBM分类算法实现特征波长优选,并将优选后的光谱数据经格拉姆角场转换成保留波长序列之间时序性的格拉姆角和场图像;构建残差深度卷积神经网络模型,将转换后的格拉姆角和场图像作为训练样本对残差网络模型进行训练。实验结果表明,该方法能够有效地对复杂环境下棉层中的异性纤维进行分类,分类准确率达到99.69%,与其它数据转换方式和分类模型相比提高了棉层中异性纤维的分类识别精度,为复杂环境下异性纤维分类识别研究提供了新思路。Objective It has been shown that image processing methods can not clearly acquire image characteristics of foreign fibers in cotton layers.In order to solve the problem associated to conventional image processing methods,this paper proposed a classification and identification method for foreign fibers in cotton layers based on near-infrared(NIR)spectroscopy and residual neural networks(ResNet).Method In this study,500 groups of foreign fibers spectral data were collected by experiments,including five types of foreign fibers.The spectral collection instrument was a UH4150 spectrophotometer.Savitzky-Golay method was adopted to smooth the spectral data,and F-test and LightGBM classification algorithm was adopted to determine the optimal feature wavelength.The optimal spectral data were converted into Garmian angular summation fields(GASF)images by the Garmian angular field(GAF)method,which preserved the temporal sequences between wavelength sequences.Eventually,the ResNet model was constructed.The GASF images were used as training samples to train the ResNet model.Results The foreign fibers′spectral data was smoother than the original spectrum by the Savitzky-Golay method.Noisy data at both ends of the spectrum and near the peaks of functional groups were eliminated(Fig.2).After F-test and LightGBM classification algorithm wavelength optimization,75 optimal wavelengths were selected.When the number of wavelengths was greater than 200,important information was deleted from the foreign fibers′spectral data(Fig.3(a)).When the number of wavelengths was 75,the optimal performance of the optimized model was the best,and the accuracy reached 98.99%(Fig.3(b)).The accuracy of applying the GASF image to the ResNet model is 99.69%(Fig.7(a)).The loss of the training set showed a sharp drop for the first 50 iterations(Figs.7(b)and(c)).When the number of iterations reached 70,the training set started to converge.When the number of epochs reached 200,the training set tended to be stable.The classification accuracy of gray-scal

关 键 词：棉 异性纤维检测 近红外光谱 残差神经网络 光谱数据 图像检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TS117[自动化与计算机技术—计算机科学与技术]