纺织品车缝线迹分割网络  

Segmentation Network for Textile Sewing Stitches

作　　者：李鑫[1,2] 崔文婷 金帆 於全豪 余烨[2,3] 路强[2,3] Li Xin;Cui Wenting;Jin Fan;Yu Quanhao;Yu Ye;Lu Qiang(School of Electrical and Automation,Hefei University of Technology,Hefei 230009;Intelligent Interconnection Systems Laboratory of Anhui Province(Hefei University of Technology),Hefei 230009;School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601)

机构地区：[1]合肥工业大学电气与自动化工程学院,合肥230009 [2]智能互联系统安徽省实验室(合肥工业大学),合肥230009 [3]合肥工业大学计算机与信息学院,合肥230601

出　　处：《计算机辅助设计与图形学学报》2024年第8期1246-1254,共9页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61906061);合肥工业大学智能制造研究所科技成果培养项目(IMIPY2021022).

摘　　要：针对织物车缝线缝制工艺多样,造成其种类多、形态不定以及缝线与织物纹理近似性等导致车缝线准确分割困难等问题,提出一个基于多尺度特征的纺织品车缝线迹分割网络.首先采用融合注意力机制的残差网络提取其位置信息;然后使用增强特征金字塔模块,充分利用多尺度特征得到预候选区域的语义信息,将其融合后经过筛选得到车缝线候选区域;最后经过全卷积网络实现车缝线的分割.在真实纺织品车缝线数据集SewTrace上进行实验的结果表明,所提网络对纺织品车缝线迹分割的均值平均精度为0.96,计算量为1.5G;在具有相似特征的公开数据集CrackForest,CRKWH100和Kolektor上与其他同类网络进行实验的结果表明,该网络的均值平均精度分别达到0.85,0.89和0.89,分割精度和预测速度指标优于其他同类网络,证明其能够有效地提高线形目标分割精度.In order to solve the problems that fabric stitches are difficult to be accurately segmented due to the variety of fabric stitches,the uncertainty of the types,and the similarity between stitches and fabric texture,a textile stitches segmentation network based on multi-scale features is proposed.Firstly,the residual network with attention mechanism is used to extract the location information;and the enhanced pyramid module is used,making full use of the multi-scale features to obtain the semantic information of the pre-candidate areas.After fusion,the candidate regions of sewing thread are filtered;Finally,the segmentation of sewing is realized through full convolution network,realizing the segmentation of sewing line.On real textile sewing data sets,SewTrace,the mean average precision of the proposed network is 0.96,the computational load is 1.5G.On the open data sets CrackForest,CRKWH100 and Koletor with similar linear characteristics,comparative analysis is carried out with other similar networks,the experimental results show that the mean average precision of the proposed network is 0.85,0.89 and 0.89,respectively,the segmentation precision and prediction speed indica-tors are better than other similar networks,which shows that the proposed network can effectively improve the segmentation precision of linear objects.

关 键 词：车缝线 多尺度特征 图像分割 注意力机制 增强特征金字塔 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]