基于语义信息增强的化纤丝线网络度检测方法

郑广智; 彭添强; 肖计春; 吴高昌; 李智; 柴天佑

doi:10.16383/j.aas.c230649

基于语义信息增强的化纤丝线网络度检测方法

doi: 10.16383/j.aas.c230649

郑广智^1,,
彭添强¹,
肖计春^2,,
吴高昌^1,,
李智^1,,
柴天佑^{1, 3,}

1.
东北大学流程工业综合自动化国家重点实验室沈阳 110819
2.
东北大学机械工程与自动化学院沈阳 110819
3.
国家冶金自动化工程技术研究中心沈阳 110819

基金项目: 国家自然科学基金(62173077, 61991404, 62103092)资助

详细信息

作者简介:
郑广智：东北大学流程工业综合自动化国家重点实验室硕士研究生. 2021年获得大连海洋大学学士学位. 主要研究方向为机器学习, 深度学习和计算机视觉. E-mail: zgz4923@163.com

彭添强：东北大学流程工业综合自动化国家重点实验室硕士研究生. 2020年获得青岛理工大学学士学位. 主要研究方向为机器学习和深度学习. E-mail: ptq15236155308@163.com

肖计春：东北大学机械工程与自动化学院博士研究生. 2020年获得重庆理工大学硕士学位. 主要研究方向为运动规划, 攀爬机器人和自主系统. E-mail: xc390297815@163.com

吴高昌：东北大学流程工业综合自动化国家重点实验室副教授. 主要研究方向为智能计算成像, 深度学习和异常工况智能感知与预测. E-mail: wugc@mail.neu.edu.cn

李智：东北大学流程工业综合自动化国家重点实验室教授. 主要研究方向为数据驱动的建模与控制方法, 精密运动控制和智能机器人. 本文通信作者. E-mail: lizhi1@mail.neu.edu.cn

柴天佑：中国工程院院士, 东北大学教授, IEEE Life Fellow, IFAC Fellow, 欧亚科学院院士. 主要研究方向为自适应控制, 智能解耦控制, 流程工业综合自动化与智能化系统理论、方法与技术. E-mail: tychai@mail.neu.edu.cn

中图分类号: Y
计量
- 文章访问数: 257
- HTML全文浏览量: 61
- PDF下载量: 64
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-24
- 录用日期: 2024-01-23
- 网络出版日期: 2024-07-09
- 刊出日期: 2024-10-21

A Detection Method for the Interlacing Degree of Filament Yarn Based on Semantic Information Enhancement

1.
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819
2.
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819
3.
National Engineering Research Center of Metallurgy Automation, Shenyang 110819

Funds: Supported by National Natural Science Foundation of China (62173077, 61991404, 62103092)

More Information

Author Bio:
ZHENG Guang-Zhi　Master student at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. He received his bachelor degree from Dalian Ocean University in 2021. His research interest covers machine learning, deep learning, and computer vision

PENG Tian-Qiang　Master student at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. He received his bachelor degree from Qingdao University of Technology in 2020. His research interest covers machine learning and deep learning

XIAO Ji-Chun　Ph.D. candidate at the School of Mechanical Engineering and Automation, Northeastern University. He received his master degree from Chongqing University of Technology in 2020. His research interest covers motion planning, climbing robots, and autonomous systems

WU Gao-Chang　Associate professor at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interest covers intelligent computational imaging, deep learning, and intelligent sensing and prediction of abnormal working conditions

LI Zhi　Professor at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interest covers data-driven modeling and control methods, precision motion control, and intelligent robots. Corresponding author of this paper

CHAI Tian-You　Academician of Chinese Academy of Engineering, professor at Northeastern University, IEEE Life Fellow, IFAC Fellow, and academician of the International Eurasian Academy of Sciences. His research interest covers adaptive control, intelligent decoupling control, and theories, methods and technology of synthetical automation and intelligent system for process industries

摘要

摘要: 网络度是衡量化纤丝线及化纤织物性能的重要指标之一, 在生产车间中通常采用人工方式进行检测. 为解决人工检测误检率较高的问题, 提出一种基于语义信息增强的化纤丝线网络度并行检测方法. 首先, 为提升单根化纤丝线网络结点识别的准确度, 使用基于MobileNetV2优化的主干网络结构提取语义信息, 以提高模型的运算速度. 在所提主干网络的基础上, 设计语义信息增强模块和多级特征扩张模块处理主干网络的特征信息, 同时, 设计像素级注意力掩膜对特征信息进行加权和融合, 以提高网络度检测的准确性. 然后, 为实现多根化纤丝线网络度的批量计算, 基于所提语义信息增强算法, 设计网络度并行检测方法. 使用算法检测丝线网络结点, 同时使用连通域分析及掩膜提取的方法并行检测, 提取视野内每条丝线的独立区域. 随后, 将并行检测结果融合, 以准确获取每根丝线的网络度检测结果. 为验证所提方法的有效性, 使用自主研发的网络度检测设备建立了化纤丝线数据集, 并进行了实验验证. 结果表明, 所提出的方法能够有效地提高检测的准确性.
- 网络度 /
- 图像语义分割 /
- 语义特征信息 /
- 并行检测策略 /
- 注意力机制
Abstract: The interlacing degree serves as an important indicator for evaluating the performance of filament yarns and fabrics, typically detected manually in production workshop. To address the issues of high false detection rates in manual inspection, a parallel detection method for filament yarn interlacing degree based on semantic information enhancement is proposed. Firstly, to improve the recognition accuracy of interlacing nodes in a filament yarn, an improved backbone architecture based on MobileNetV2 is used for semantic information extraction to improve the computational speed of model. Building upon the proposed backbone architecture, semantic information enhancement module and multilevel feature dilated module are designed to process the feature information of the backbone architecture. Meanwhile, a pixel-level attention mask is designed to weight and fuse the feature, in order to improve the accuracy of interlacing degree detection. Then, based on the proposed enhancement algorithm for semantic information, a parallel detection method of interlacing degree is designed to achieve batch calculation for interlacing degree of multiple filament yarns. The algorithm is used to detect interlacing node, while connected domain analysis and mask extraction are used for parallel detection to extract independent regions of each filament yarn within the field. The parallel detection results are then fused to accurately obtain the interlacing degree detection results for each filament yarn. To validate the effectiveness of the proposed method, a synthetic filament yarn dataset is established using a self-developed interlacing degree detection device, and experimental verification is conducted. The results demonstrate that the proposed method can effectively improve the accuracy of detection.
- Interlacing degree /
- image semantic segmentation /
- semantic feature information /
- parallel detection strategy /
- attention mechanism

HTML全文

图 1 化纤丝线网络度示例

Fig. 1 Interlacing degree diagram of filament yarns

下载: 全尺寸图片幻灯片

图 2 网络度检测算法结构

Fig. 2 Architecture of interlacing degree detection algorithm

下载: 全尺寸图片幻灯片

图 3 基于语义信息增强模块

Fig. 3 Enhancement module based on semantic information

下载: 全尺寸图片幻灯片

图 4 多级特征扩张模块

Fig. 4 Dilated module of multilevel features

下载: 全尺寸图片幻灯片

图 5 阶段性特征融合模块

Fig. 5 Fusion module of staged feature

下载: 全尺寸图片幻灯片

图 6 单根丝线图像提取过程

Fig. 6 Process of extracting single filament yarn image

下载: 全尺寸图片幻灯片

图 7 多根丝线网络度并行检测方法

Fig. 7 Parallel detection method for interlacing degree of the multiple filament yarns

下载: 全尺寸图片幻灯片

图 8 多根丝线并行网络度检测设备

Fig. 8 Parallel detection equipment for interlacing degree of multiple filament yarns

下载: 全尺寸图片幻灯片

图 9 网络度检测设备操作流程

Fig. 9 Operation process of interlacing degree detection equipment

下载: 全尺寸图片幻灯片

图 10 四种丝线样本图像

Fig. 10 Sample images for four types of filament yarns

下载: 全尺寸图片幻灯片

图 11 丝线散开大于20 mm时的图像检测结果

Fig. 11 Detection results of images with filament yarn spreading greater than 20 mm

下载: 全尺寸图片幻灯片

图 12 丝线散开小于10 mm时的图像检测结果

Fig. 12 Detection results of images with filament yarn spreading less than 10 mm

下载: 全尺寸图片幻灯片

图 13 含倾斜丝线的图像检测结果

Fig. 13 Detection results of images with inclined filament yarn

下载: 全尺寸图片幻灯片

图 14 含不明显网络结点的图像检测结果

Fig. 14 Detection results of images with indistinct interlacing nodes

下载: 全尺寸图片幻灯片

表 1 主干网络架构

Table 1 Architecture of the backbone network

特征尺寸(像素)	扩展因子	循环次数	输出通道数	步长
512 × 512 × 3	—	1	32	2
256 × 256 × 32	1	2	32	1
128 × 128 × 32	6	4	64	2
64 × 64 × 64	6	2	96	2
32 × 32 × 96	—	—	—	—

下载: 导出CSV

表 2 模型训练环境配置

Table 2 Configuration of model training environment

项目	版本参数
操作系统	Ubuntu 18.04.6 LTS
CUDA	cuda 11.3
GPU	NVIDIA RTX 3 090
训练框架	PyTorch 1.10.2
内存	128 GB
编程语言	Python 3.8

下载: 导出CSV

表 3 模型训练超参数配置

Table 3 Configuration of model training hyperparameter

参数	配置信息
输入图像尺寸	512 × 512 像素
下采样倍数	16
初始学习率	$5 \times 10^{-3}$
最小学习率	$5 \times 10^{-5}$
优化器	Adam
权值衰减	$5 \times 10^{-4}$
批量大小	12

下载: 导出CSV

表 4 不同方法的评价指标比较

Table 4 Comparison of evaluation indicators for different methods

方法	平均交并比 (%)	$F_{1}$分数 (%)	每秒传输帧数(帧/s)	参数量 (MB)
BiSeNet	78.95	86.76	63.84	48.93
CGNet	79.00	86.79	33.17	2.08
DeepLabV3+	79.50	87.35	43.11	209.70
HRNet	78.74	86.69	12.43	37.53
PSPNet	73.58	82.46	49.80	178.51
SegFormer	79.04	86.84	40.87	14.34
UNet	79.83	87.63	22.54	94.07
本文方法	81.52	88.12	76.16	7.98
注: 加粗字体表示各列最优结果.

下载: 导出CSV

表 5 模块有效性验证实验结果

Table 5 Results of module validity verification experimental

方案序号	语义信息增强模块	多级特征扩张模块	阶段性特征融合模块	MIoU (%)	FPS (帧/s)
1	$\times$	$\times$	$\times$	77.18	72.75
2	$\surd$	$\times$	$\times$	79.91	72.15
3	$\times$	$\surd$	$\times$	79.85	66.32
4	$\times$	$\times$	$\surd$	79.33	68.31
5	$\times$	$\surd$	$\surd$	80.71	55.30
6	$\surd$	$\times$	$\surd$	81.15	61.48
7	$\surd$	$\surd$	$\times$	80.25	78.16
8	$\surd$	$\surd$	$\surd$	81.52	76.16
注: $\surd$指使用此模块, $\times$指不使用此模块.

下载: 导出CSV

表 6 不同主干网络提取效率比较

Table 6 Comparison of extraction efficiency of different backbone networks

方案序号	主干网络	MIoU (%)	FPS (帧/s)
1	FCN	79.65	33.45
2	MobileNetV2	80.25	43.11
3	Xception	79.61	27.45
4	VGGNet	77.45	30.12
5	ResNet18	77.52	45.21
6	ResNet50	78.01	47.06
7	本文方法	81.52	76.16

下载: 导出CSV

表 7 不同语义信息提取方法结果比较

Table 7 Comparison results of extraction method for different context information

方案序号	注意力选择	MIoU (%)
1	SA	80.36
2	SE	80.44
3	CBAM	80.83
4	ECA	79.89
5	本文方法	81.52

下载: 导出CSV

表 8 不同扩张卷积提取方式结果比较

Table 8 Comparison results of different dilated convolution extraction methods

方案序号	$x_{3}$	$x_{4}$	MIoU (%)
1	$\times$	$\times$	80.56
2	$\surd$	$\times$	81.02
3	$\times$	$\surd$	81.13
4	$\surd$	$\surd$	81.52
注: $\surd$指使用此模块, $\times$指不使用此模块.

下载: 导出CSV

表 9 阶段性特征融合方法实验比较

Table 9 Comparison results of staged feature fusion module

方案序号	全局平均池化	逐点卷积	组合方法	MIoU (%)
1	$\surd$	$\times$	无	80.91
2	$\times$	$\surd$	无	80.65
3	$\surd$	$\surd$	串行	78.91
4	$\surd$	$\surd$	并行	81.52
注: $\surd$指使用此模块, $\times$指不使用此模块.

下载: 导出CSV

参考文献(34)

[1]	陈向玲, 王华平, 吉鹏. 我国化纤智能制造的柔性与多目标生产. 纺织导报, 2020, 916(3): 13−14, 16−18, 21−22, 24−25 doi: 10.3969/j.issn.1003-3025.2020.03.004 Chen Xiang-Ling, Wang Hua-Ping, Ji Peng. Flexibility and multi-objective production of intelligent manufacturing of China's chemical fiber industry. China Textile Leader, 2020, 916(3): 13−14, 16−18, 21−22, 24−25 doi: 10.3969/j.issn.1003-3025.2020.03.004
[2]	Wang M, Zhan Y L, Yao L. A new method to evaluate interlacing yarns. Textile Research Journal, 2020, 90(7−8): 838−846 doi: 10.1177/0040517519881820
[3]	张叶兴, 陈浩秋, 高国洪, 黄猛富, 肖永新, 刘丽娜. 合成纤维长丝网络度测试方法比较分析. 中国纤检, 2010, 359(15): 54−57 doi: 10.3969/j.issn.1671-4466.2010.15.019 Zhang Ye-Xing, Chen Hao-Qiu, Gao Guo-Hong, Huang Meng-Fu, Xiao Yong-Xin, Liu Li-Na. Comparative analysis about tesiting method of synthetic filament network degree. China Fiber Inspection, 2010, 359(15): 54−57 doi: 10.3969/j.issn.1671-4466.2010.15.019
[4]	Yan N, Zhu L L, Yang H M, Li N N, Zhang X D. Online yarn breakage detection: A reflection-based anomaly detection method. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1−13
[5]	Guo M R, Gao W D, Wang J A. Online measurement of sizing yarn hairiness based on computer vision. Fibers and Polymers, 2023, 24(4): 1539−1552 doi: 10.1007/s12221-023-00136-5
[6]	Wang L, Lu Y C, Pan R R, Gao W D. Evaluation of yarn appearance on a blackboard based on image processing. Textile Research Journal, 2021, 91(19−20): 2263−2271 doi: 10.1177/00405175211002863
[7]	Khaddam H S, Ahmad G G. A method to evaluate the diameter of carded cotton yarn using image processing and artificial neural networks. The Journal of the Textile Institute, 2022, 113(8): 1648−1657 doi: 10.1080/00405000.2021.1943259
[8]	唐嘉潞, 杨钟亮, 张凇, 毛新华, 董庆奇. 结合显微视觉和注意力机制的毛羽检测方法. 智能系统学报, 2022, 17(6): 1209−1219 doi: 10.11992/tis.202112035 Tang Jia-Lu, Yang Zhong-Liang, Zhang Song, Mao Xin-Hua, Dong Qing-Qi. Detection of yarn hairiness combining microscopic vision and attention mechanism. CAAI Transactions on Intelligent Systems, 2022, 17(6): 1209−1219 doi: 10.11992/tis.202112035
[9]	Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015. 640−651
[10]	Zhao H S, Shi J P, Qi X J, Wang X G, Jia J Y. Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 6230−6239
[11]	Zhou Z W, Siddiquee M M R, Tajbakhsh N, Liang J M. UNet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 2019, 39(6): 1856−1867
[12]	Chen L C, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision. Munich, Germany: Springer, 2018. 801−818
[13]	Chen L C, Papandreou G, Schroff F, Adam H. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv: 1706.05587, 2017.
[14]	Chen L C, Papandreou G, Kokkinos I, Murphy K, Yuille A L. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834−848 doi: 10.1109/TPAMI.2017.2699184
[15]	Rush A M, Chopra S, Weston J. A neural attention model for abstractive sentence summarization. arXiv preprint arXiv: 1509.00685, 2015.
[16]	Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, et al. Attention is all you need. arXiv preprint arXiv: 1706.03762, 2023.
[17]	Cheng H X, Han X F, Xiao G Q. TransRVNet: Lidar semantic segmentation with transformer. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(6): 5895−5907 doi: 10.1109/TITS.2023.3248117
[18]	Liu Z, Hu H, Lin Y T, Yao Z L, Xie Z D, Wei Y X, et al. Swin transformer v2: Scaling up capacity and resolution. In: Proceedings of the International Conference on Computer Vision. New Orleans, USA: IEEE, 2022. 12009−12019
[19]	Huang T Y, Chen K X, Jiang L F. DS-UNeXt: Depthwise separable convolution network with large convolutional kernel for medical image segmentation. Signal, Image and Video Processing, 2023, 17(5): 1775−1783 doi: 10.1007/s11760-022-02388-9
[20]	Sinha A, Dolz J. Multi-scale self-guided attention for medical image segmentation. IEEE Journal of Biomedical and Health Informatics, 2020, 25(1): 121−130
[21]	彭秀平, 仝其胜, 林洪彬, 冯超, 郑武. 一种面向散乱点云语义分割的深度残差−特征金字塔网络框架. 自动化学报, 2021, 47(12): 2831−2840 Peng Xiu-Ping, Tong Qi-Sheng, Lin Hong-Bin, Feng Chao, Zheng Wu. A deep residual-feature pyramid network framework for scattered point cloud semantic segmentation. Acta Automatica Sinica, 2021, 47(12): 2831−2840
[22]	Zhai W Z, Gao M L, Li Q L, Jeon G, Anisetti M. FPANet: Feature pyramid attention network for crowd counting. Applied Intelligence, 2023, 53(16): 19199−19216 doi: 10.1007/s10489-023-04499-3
[23]	Mei Y Q, Fan Y C, Zhang Y L, Yu J H, Zhou Y Q, Liu D, et al. Pyramid attention network for image restoration. International Journal of Computer Vision, 2023, 131(12): 3207−3225 doi: 10.1007/s11263-023-01843-5
[24]	范兵兵, 葛利跃, 张聪炫, 李兵, 冯诚, 陈震. 基于多尺度变形卷积的特征金字塔光流计算方法. 自动化学报, 2023, 49(1): 197−209 Fan Bing-Bing, Ge Li-Yue, Zhang Cong-Xuan, Li Bing, Feng Cheng, Chen Zhen. A feature pyramid optical flow estimation method based on multi-scale deformable convolution. Acta Autom atica Sinica, 2023, 49(1): 197−209
[25]	金侠挺, 王耀南, 张辉, 刘理, 钟杭, 贺振东. 基于贝叶斯CNN和注意力网络的钢轨表面缺陷检测系统. 自动化学报, 2019, 45(12): 2312−2327 Jin Xia-Ting, Wang Yao-Nan, Zhang Hui, Liu Li, Zhong Hang, He Zhen-Dong. DeepRail: Automatic visual detection system for railway surface defect using Bayesian CNN and attention network. Acta Automatica Sinica, 2019, 45(12): 2312−2327
[26]	Sandler M, Howard A, Zhu M L, Zhmoginov A, Chen L C. MobilenetV2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018. 4510−4520
[27]	Chollet F. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 1251−1258
[28]	Kim S, Min D B, Ham B, Jeon S, Lin S, Sohn K. FCSS: Fully convolutional self-similarity for dense semantic correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(3): 581−595 doi: 10.1109/TPAMI.2018.2803169
[29]	Kirisci M. New cosine similarity and distance measures for fermatean fuzzy sets and topsis approach. Knowledge and Information Systems, 2023, 65(2): 855−868 doi: 10.1007/s10115-022-01776-4
[30]	Yu C Q, Wang J B, Peng C, Gao C X, Yu G, Sang N. BiSeNet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision. Munich, Germany: IEEE, 2018. 325−341
[31]	孟俊熙, 张莉, 曹洋, 张乐天, 宋倩. 基于Deeplab v3+ 的图像语义分割算法优化研究. 激光与光电子学进展, 2022, 59(16): 161−170 Meng Jun-Xi, Zhang Li, Cao Yang, Zhang Le-Tian, Song Qian. Optimization of image semantic segmentation algorithms based on Deeplab v3+. Laser and Optoelectronics Progress, 2022, 59(16): 161−170
[32]	Wu T Y, Tang S, Zhang R, Zhang Y D. CGNet: A light-weight context guided network for semantic segmentation. IEEE Transactions on Image Processing, 2020, 30: 1169−1179
[33]	Sun K, Xiao B, Liu D, Wang J D. Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE, 2019. 5693−5703
[34]	Xie E, Wang W H, Yu Z D, Anandkumar A, Alvarez J M, Luo P. SegFormer: Simple and efficient design for semantic segmentation with transformers. arXiv preprint arXiv: 2105.15203, 2021.