摘要
为解决纬编针织物组织结构自动分类时现有方法计算量偏大的问题,基于轻量化卷积神经网络,提出了一种改进的纬编针织物组织结构分类方法。采集纬编针织物组织双面的图像,以准确判断其结构类型。在特征提取步骤中,引入了注意力机制模块,修正各个层次特征在通道域和空间域的权重。构建的双分支网络架构能并行提取织物双面的特征信息。在分类阶段,采用了串行策略来融合高维特征向量,以确定纬编针织物组织所属类别。使用准确率、宏精确率、宏召回率以及宏F_(1)评估模型的性能,并统计了参数量和计算复杂度衡量模型的资源消耗。实验结果显示,对于纬编针织物特殊的结构特点,双分支网络架构具有很好的适应性。改进后的模型增强了不同组织间的特征区分度,在受到角度旋转、尺度改变、光照条件变化等干扰下,本文方法的分类准确率可达99.51%,且保持了较小的资源消耗。
Objective The structure of the fabric is one of the important parameters to guide the production of fabric.The automatic identification of the fabric structure through machine vision helps to improve the design and production efficiency.Weft knitted fabrics have complex knitting methods and various structures,and it is difficult to accurately determine the structure of the fabric only by the image of one side of the fabric.Therefore,it is necessary to design an efficient and accurate classification method for the special structure of weft knitted fabrics.Method A fabric image acquisition platform was built to capture images of both sides of fabric samples at multi-scale and various lighting conditions.A dataset containing images of weft knitted fabrics in nine categories was produced.The method in this paper was improved based on GhostNet which is a lightweight convolutional neural network.In order to improve the network′s ability to learn different features,two strategies were adopted to introduce an attention mechanism in the feature extraction stage.The network structure was adjusted to a dual-branch architecture,so that the features of the double-sided image of knitted fabrics were simultaneously extracted through the weight-sharing sub-network,and the extracted high-dimensional feature maps were serially fused.Results The experimental part analyzes the effectiveness and performance of the proposed method.Multiple online augmentation methods increase the diversity of fabric sample data and improve the robustness of the model.Compared with the original data set,the model has higher accuracy rate on the validation set.Adding a dropout layer after the fully connected layer improves the generalization performance of the model.When the dropout rate is 0.4,the model has the best performance.For the fabric categories that are difficult to distinguish based on single-sided images,the proposed method has achieved a classification accuracy of more than 99%.In order to observe the feature extraction effect of the model more intuitively,the feature maps of different levels of the fabric image are visualized.The model pays more attention to important features such as the shape and texture of the fabric.Accuracy,macro precision,macro recall,and macro F_(1)are adopted to evaluate the performance of the model,and the number of parameters and computational complexity are calculated to measure the resource consumption of the model.The results of the ablation experiments show that the incorporation of the CBAM module effectively improves the performance of the model.Different models and methods are compared under the same hyper parameter settings.First,common CNN models are tested on the dataset constructed in this paper.The prosposed method achieves the highest classification accuracy of 99.51%,the macro precision rate is 0.9941,the macro recall rate is 0.9946,and the macro F_(1)score is 0.9942,with lower FLOPs(0.31 G)and params(4.62 M)compared to other models.Conclusion Aiming at the classification of weft-knitted fabrics,a double-sided image data set is used in the classification of knitted fabrics.An end-to-end classification method of knitted fabric structure was prosposed based on GhostNet,which is a lightweight convolutional neural network.The experimental results show that the CBAM module enhances the feature discrimination between different fabrics,which improves the network performance.The double-sided features of weft-knitted fabric were efficiently extracted by dual-branch network architecture.Compared with other classification methods based on CNN,the proposed method has a higher classification accuracy and consumes less resources,which is conducive to the deployment of the model on mobile devices or embedded devices.In future work,the case that a single fabric image contains multiple fabric structure will become the focus of research,which is of great significance to further improve the recognition efficiency of the algorithm in the actual design and production process of fabrics.
作者
胡旭东
汤炜
曾志发
汝欣
彭来湖
李建强
王博平
HU Xudong;TANG Wei;ZENG Zhifa;RU Xin;PENG Laihu;LI Jianqiang;WANG Boping(Key Laboratory of Modern Textile Machinery&Technology of Zhejiang Province,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;Zhejiang Hengqiang Technology Co.,Ltd.,Hangzhou,Zhejiang 311121,China;College of Biomedical Engineering&Instrument Science,Zhejiang University,Hangzhou,Zhejiang 310027,China)
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2024年第5期60-69,共10页
Journal of Textile Research
基金
浙江省公益技术研究计划项目(LGG21E050024)
浙江理工大学科研启动基金项目(18022224-Y)。
作者简介
第一作者:胡旭东(1959-),男,教授,博士。主要研究方向为智能纺织装备技术;通信作者:李建强(1990-),男,博士。主要研究方向为机器视觉、机电一体化技术。E-mail:wzcnljq@126.com。
