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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%.
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.
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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
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
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
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
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.
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
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 $ 
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
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 $ 
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
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] 
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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 500 w 800 × 600 20 ~ 10 m 75度 控制器 处理器 内存 固态 显卡 i5 8 GB 64 GB Intel HD 交换机 端口数 端口参数 8个 1000 M 自适应 RJ45 端口
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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
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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
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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 0.1 ~ 0.3 99.6 98.3 丝状瑕疵 ＞ 0.1 98.8 98.6 0.05 ~ 0.1 96.8 97.2
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