无纺布疵点实时检测技术与系统设计

邓泽林; 刘行; 董云龙; 袁烨

doi:10.16383/j.aas.c200446

无纺布疵点实时检测技术与系统设计

doi: 10.16383/j.aas.c200446

邓泽林¹,
刘行²,
董云龙¹,
袁烨^{1, 3}

1.
华中科技大学人工智能与自动化学院武汉 430074
2.
华东理工大学信息科学与工程学院上海 200237
3.
华中科技大学数字制造装备与技术国家重点实验室武汉 430074

基金项目: 国家自然科学基金(91748112)资助

详细信息

作者简介:
邓泽林：2018年获得华中科技大学数学与统计学院学士学位. 2020年获得中国香港城市大学商学院硕士学位. 主要研究方向为深度学习在智能制造中的应用. E-mail: dengzelinhust@gmail.com

刘行：华东理工大学信息科学与工程学院硕士研究生. 2020年获华东交通大学电气与自动化工程学院学士学位. 主要研究方向为深度学习, 智能控制与图像处理. E-mail: prevalent98@outlook.com

董云龙：华中科技大学人工智能与自动化学院博士. 2017年获华中科技大学自动化学院学士学位. 主要研究方向为深度学习, 控制理论和机器人. E-mail: dyl@hust.edu.cn

袁烨：2008年获得中国上海交通大学自动化系自动化学士学位. 分别于2009年10月和2012年2月在英国剑桥剑桥大学工程系控制科学与工程硕士学位与博士学位. 现任中国武汉华中科技大学教授. 曾担任加州大学伯克利分校博士后研究员, 剑桥大学达尔文学院的初级研究员. 主要研究方向为信息物理系统的系统识别和控制. 本文通信作者. E-mail: yye@hust.edu.cn

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出版历程
- 收稿日期: 2020-06-22
- 网络出版日期: 2021-04-02
- 刊出日期: 2021-04-02

Non-woven Fabric Real-time Defects Detection Method and Framework Design

1.
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074
2.
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237
3.
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074

Funds: Supported by National Natural Science Foundation of China (91748112)

More Information

Author Bio:
DENG Ze-Lin　Received his bachelor degree from the School of Mathematics and Statistics, Huazhong University of Science and Technology in 2018, and his master degree from the College of Business, City University of Hong Kong, China in 2020. His research interest covers deep learning in smart manufacturing

LIU Xing　Master student at the School of Information Science and Engineering, East China University of Science and Technology. He received his bachelor degree from the School of Electrical and Automation Engineering, East China Jiaotong University in 2020. His research interest covers deep learning, intelligent control, and image processing

DONG Yun-Long　Ph.D. candidate at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. He received his bachelor degree from the School of Automation, Huazhong University of Science and Technology in 2017. His research interest covers deep learning, control theory, and robotics

YUAN Ye　Received his bachelor degree in automation (valedictorian) from the Department of Automation, Shanghai Jiao Tong University, in 2008, and the M.Phil. and Ph.D. degrees in control science and engineering from the Department of Engineering, University of Cambridge, Cambridge, UK, in October 2009 and February 2012, respectively. He is currently a full professor at Huazhong University of Science and Technology. He was a postdoctoral researcher at UC Berkeley, a junior research fellow at Darwin College, University of Cambridge, UK. His research interest covers system identification and control with applications to cyber-physical systems. Corresponding author of this paper

摘要

摘要:
无纺布生产过程中产生的疵点会严重影响产品质量并限制生产效率. 提高疵点检测的自动化程度对于无纺布的生产效率和质量管控至关重要. 传统疵点检测方法难以应对纹理、疵点类型以及环境变化等问题, 限制了其应用范围. 近年来基于卷积神经网络的方法在疵点检测领域得到了广泛应用, 具有泛化性强、准确度高的特点. 但是在无纺布生产过程中, 布匹宽度大、速度快的特点会产生大量图像数据, 基于卷积神经网络的方法难以实现实时检测. 针对上述难题, 本文提出了一种基于最大稳定极值区域分析与卷积神经网络协同的疵点实时检测方法, 并设计了分布式计算处理架构应对数据流过大的问题. 在实际生产部署应用中, 本文所设计的系统与算法无需使用专用计算硬件(GPU、FPGA等), 通过8台工控机与16路工业摄像头对复卷机上布宽2.8 m、速度30 m/min的无纺布进行分布式实时在线检测, 大幅度提高无纺布生产中疵点检测的自动化程度与效率. 本文所提出的系统能够实现对0.3 mm以上疵点召回率100%, 对0.1 mm丝状疵点召回率98.8%.
- 疵点检测 /
- 卷积神经网络 /
- 实时处理 /
- 分布式架构
Abstract:
The defects generated during the production process of non-woven fabric will seriously affect the quality and limit the efficiency. How to improve the automatic degree of non-woven fabric defects detection plays a significant role. The traditional defects detection methods cannot deal with the changing of texture, defects type and environments, which limits the application scope. In recent years, the methods based on convolutional neural networks (CNNs) have been widely used in the field of defects detection, which are shown to have the characteristics of strong generalization ability and high accuracy. However, in the non-woven fabric production process, the large width and high speed of cloth will introduce huge amount of image data, which makes it difficult for CNN based methods to achieve real-time detection. In this paper, a real-time defects detection method based on stable extremal region analysis and CNN is proposed, and a distributed computing architecture is designed to handle the problem of large image data stream. In the actual deployment application, the system designed in this paper does not need specific computing hardware (GPU, FPGA, etc.). 8 industrial computers and 16 industrial cameras are coupled together in a distribution scheme to finish real-time defects detection of non-woven fabric rewinder with cloth width 2.8 m and speed 30 m/min, which greatly improves the automation and production efficiency. The system proposed in this paper can achieve 100% recall rate of punctiform defects above 0.3 mm and 98.8% recall rate of 0.1 mm filiform defects.
- Defects detection /
- convolutional neural network (CNN) /
- real-time processing /
- distributed architecture

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图 1 无纺布生产过程中产生的瑕疵, 第1行为点状疵点, 第2行为丝状瑕疵

Fig. 1 Defects generated in the production process of non-woven fabrics. The dotted defects and filamentary defects are shown in the first and second row

下载: 全尺寸图片幻灯片

图 2 在不同阈值$\tau $时MSER算法产生的候选区域, 图中点代表候选区域的中心

Fig. 2 In the candidate regions generated by the MSER algorithm at different thresholds $\tau,$ the dots represent the center position of the candidate region

下载: 全尺寸图片幻灯片

图 3 本文采用的预检测模型结构图. 输入疵点图像经过三个不同尺度的卷积后得到三个特征图, 特征图拼接后作为稠密连接模块的输入. 稠密连接模块输出与全连接层和softmax层相连. 其中虚线矩形框出部分为展开稠密连接模块的具体形式

Fig. 3 The structure diagram of the pre-detection model used in this paper. The input defect image is convolved at three different scales to obtain three feature maps, and the feature maps are concatenated as the input of the dense block. The output of the dense block is connected to the fully connected layer and the softmax layer. Among them. The dashed rectangle outlines the specific form of the dense block

下载: 全尺寸图片幻灯片

图 4 本文采用的精确检测模型. 其中${t_x},{t_y},{t_w},{t_h}$分别表示检测矩形框的横坐标、纵坐标、宽度与高度, ${{C}}$表示疵点检测置信度

Fig. 4 The precise detection model used in this paper. ${t_x},{t_y},{t_w},{t_h}$ are the abscissa, ordinate, width and height of the detection rectangle, and ${{C}}$ is the confidence of defect detection

下载: 全尺寸图片幻灯片

图 5 NMS算法处理效果图

Fig. 5 NMS algorithm processing effect diagram. (a) is the original image; (b) is the network prediction effect diagram, the network prediction rectangle is drawn with a red rectangle; and (c) is the effect diagram processed by the NMS algorithm.

下载: 全尺寸图片幻灯片

图 7 无纺布疵点检测系统总体组成. (I) 代表工业控制机集群, (II) 代表高速工业摄像头, 摄像头发出的光线代表摄像头的视野. 下方的摄像头组用于预检测, 上方的摄像头组用于精确检测

Fig. 7 The overall composition of the non-woven defect detection system. In the design diagram (I) represents the industrial control machine cluster, (II) represents the high-peed industrial camera, and the light from the camera represents the camera's field of view. The lower camera group is used for pre-detection, and the upper camera group is used for precise detection

下载: 全尺寸图片幻灯片

图 8 系统分布式设计结构

Fig. 8 System distributed design structure

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图 6 无纺布疵点检测算法流程图

Fig. 6 Flow chart of non-woven fabric defect detection algorithm

下载: 全尺寸图片幻灯片

图 9 疵点检测系统终端软件

Fig. 9 Defect detection system terminal software

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图 12 不同模型在不同阈值$\tau $下的召回率对比

Fig. 12 Comparison of recall rates of different models under different thresholds $\tau $

下载: 全尺寸图片幻灯片

图 10 通过本文算法对疵点进行检测的结果

Fig. 10 The result of defect detection through the algorithm of this paper

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图 11 不同模型在不同阈值$\tau $下的精度对比

Fig. 11 Comparison of the accuracy of different models under different thresholds $\tau $

下载: 全尺寸图片幻灯片

图 13 不同光照和纹理条件下疵点检测结果

Fig. 13 Defect detection results under different lighting and texture conditions

下载: 全尺寸图片幻灯片

表 1 训练超参数配置

Table 1 Training hyperparameter configuration

参数类型	取值
输入尺寸	48×48
训练轮数	100
学习率	$1{\times 10^{ - 4} }$
衰减因子	0.99
批次大小	8
优化器	Adam^[34]

下载: 导出CSV

表 2 硬件设备参数

Table 2 Hardware device information

硬件类型	参数
摄像头	像素	分辨率	物距	视角
摄像头	500 w	800 × 600	20 ~ 10 m	75度
控制器	处理器	内存	固态	显卡
控制器	i5	8 GB	64 GB	Intel HD
交换机	端口数	端口参数
交换机	8个	1000 M 自适应 RJ45 端口

下载: 导出CSV

表 3 模型在不同阈值下的检测精度和召回率

Table 3 The detection accuracy and recall rate of the model under different thresholds

模型	阈值	精度(%)	召回率(%)
预检测	$\tau {\rm{ = 20}}$	73.0	100.0
	$\tau {\rm{ = 30}}$	80.5	100.0
	$\tau {\rm{ = 40}}$	86.4	99.3
精确检测	$\tau {\rm{ = 20}}$	86.1	100.0
	$\tau {\rm{ = 30}}$	94.5	100.0
	$\tau {\rm{ = 40}}$	98.4	99.7

下载: 导出CSV

表 4 模型预测速度测试 (ms)

Table 4 Model prediction speed test (ms)

模型	48×48 候选区域	2400×600 无纺布图像
预检测	3.2	46.8
精确检测	13.9	213.3

下载: 导出CSV

表 5 不同类型疵点的检测精度和召回率测试

Table 5 Testing accuracy and recall rate of different types of defects

疵点类型	疵点尺寸 (mm)	召回率 (%)	精度 (%)
点状瑕疵	＞ 0.3	100.0	98.8
点状瑕疵	0.1 ~ 0.3	99.6	98.3
丝状瑕疵	＞ 0.1	98.8	98.6
丝状瑕疵	0.05 ~ 0.1	96.8	97.2

下载: 导出CSV

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