纺织学报 ›› 2020, Vol. 41 ›› Issue (04): 51-57.doi: 10.13475/j.fzxb.20190502107

• 纺织工程 • 上一篇    下一篇

基于机器视觉的碳纤维预浸料表面缺陷检测方法

路浩, 陈原()   

  1. 山东大学 机电与信息工程学院, 山东 威海 264209
  • 收稿日期:2019-05-13 修回日期:2020-01-19 出版日期:2020-04-15 发布日期:2020-04-27
  • 通讯作者: 陈原
  • 作者简介:路浩(1994—),男,硕士生。主要研究方向为机器人与机器视觉。
  • 基金资助:
    国家自然科学基金资助项目(51375264);中央高校基本科研业务费专项资金资助项目(2019ZRJC006);山东省重大创新工程资助项目(2017CXGC0923);山东省重点研发计划资助项目(2018GGX103025);山东省自然科学基金资助项目(ZR2019MEE019)

Surface defect detection method of carbon fiber prepreg based on machine vision

LU Hao, CHEN Yuan()   

  1. School of Mechanical, Electronic & Information Engineering, Shandong University, Weihai, Shandong 264209, China
  • Received:2019-05-13 Revised:2020-01-19 Online:2020-04-15 Published:2020-04-27
  • Contact: CHEN Yuan

摘要:

针对碳纤维预浸料表面缺陷人工检测方法效率低、成本高、实时性差等问题,提出基于机器视觉的碳纤维预浸料表面缺陷自动检测方法。首先,在碳纤维预浸料生产线上,采用2台高分辨率线扫描相机快速连续采集图像,从中随机选择带有缺陷的图像1 000张;其次,基于大气光散射模型对图像进行去雾增强处理,以消除白色树脂的干扰;然后,改进具有19个卷积层和5个最大值池化层的YOLOv2目标检测算法,用于缺陷的检测;最后,对预处理后的图像进行网络训练提取图像特征,识别图像目标,并对训练好的网络进行实验验证。结果表明:该方法在复杂的工业环境下,具有较高的识别精度和鲁棒性,识别成功率达到94%以上,且每张图像的检测时间不超过 0.1 s,可满足工业生产中精度和实时性要求。

关键词: 机器视觉, 碳纤维预浸料, 表面缺陷检测, 图像预处理, YOLOv2算法

Abstract:

Aiming at low efficiency, high cost and poor real-time of artificial detection of surface defects of carbon fiber prepregs, an automatic detection method based on machine vision was proposed. Two high resolution line scanning cameras were used to collect images quickly and continuously in the carbon fiber production line, from which 1 000 images with defects were randomly selected. After that, the image enhancement algorithm based on the atmospheric light scattering model was used to pre-process the images to eliminate the interference of white resin. The YOLOv2 object detection network was refined with 19 convolution layers and 5 maximum pooling layers for improvement in detect detection. Finally, the pre-processed images were trained, image features were extracted, image objects were identified, and the trained network was verified. The experimental results show that the proposed method has high accuracy and robustness under complex industrial environment, the recognition success rate in this research is over 94%, and the detection time of each image is less than 0.1 s, meeting the requirements of precision and real-time in industrial production.

Key words: machine vision, carbon fiber prepreg, surface defect detection, image pre-procession, YOLOv2 algorithm

中图分类号: 

  • TP391

图1

碳纤维预浸料的表面缺陷检测系统 1—工控机; 2—碳纤维预浸料; 3—线扫相机;4—编码器; 5—光源; 6—滚筒。"

图2

碳纤维预浸料的表面缺陷"

图3

图像预处理"

图4

YOLOv2目标检测算法的框架 1—图像特征提取分类; 2—图像识别(边界框预测);3—非极大值抑制; 4—边界框回归。"

表1

Darknet-19卷积神经网络的结构"

类型 滤波器个数 卷积核/步长 输出特征尺寸
卷积层1 32 3×3 224像素×224像素
最大池化层1 1 2×2/2 112像素×112像素
卷积层2 64 3×3 112像素×112像素
最大池化层2 1 2×2/2 56像素×56像素
卷积层3 128 3×3 56像素×56像素
卷积层4 64 1×1 56像素×56像素
卷积层5 128 3×3 56像素×56像素
最大池化层3 1 2×2/2 28像素×28像素
卷积层6 256 3×3 28像素×28像素
卷积层7 128 1×1 28像素×28像素
卷积层8 256 3×3 28像素×28像素
最大池化层4 1 2×2/2 14像素×14像素
卷积层9 512 3×3 14像素×14像素
卷积层10 256 1×1 14像素×14像素
卷积层11 512 3×3 14像素×14像素
卷积层12 256 1×1 14像素×14像素
卷积层13 512 3×3 14像素×14像素
最大池化层5 1 2×2/2 7像素×7像素
卷积层14 1 024 3×3 7像素×7像素
卷积层15 512 1×1 7像素×7像素
卷积层16 1 024 3×3 7像素×7像素
卷积层17 512 1×1 7像素×7像素
卷积层18 1 024 3×3 7像素×7像素
卷积层19 1 000 1×1 7像素×7像素
平均池化 全局 1 000

图5

非极大值抑制过程"

图6

边界框回归"

图7

碳纤维预浸料表面缺陷的训练结果"

表2

YOLOv2模型的训练结果对比"

YOLOv2模型 数据集 mAP 训练时间/h
YOLO-VOC 训练数据集 76.8 5
Tiny-YOLO 训练数据集 75.4 3

表3

Tiny-YOLO多尺度训练结果"

图像分辨率/像素 数据集 mAP 处理速度/(帧·s-1)
288×288 训练数据集 69.6 82
416×416 训练数据集 73.5 55
544×544 训练数据集 74.2 40

图8

碳纤维预浸料表面缺陷的检测结果"

表4

系统检测效果"

缺陷类型 检出率 误检率
裂缝 98.0 0.0
毛团 96.0 1.0
孔洞 97.0 1.0
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