Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (04): 51-57.doi: 10.13475/j.fzxb.20190502107

• Textile Engineering • Previous Articles     Next Articles

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 E-mail:cyzghysy@sdu.edu.cn

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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

CLC Number: 

  • TP391

Fig.1

Surface defect detection system of carbon fiber prepreg"

Fig.2

Surface defects of carbon fiber prepreg. (a) Normal; (b) Crack; (c) Hairball; (d) Hole"

Fig.3

Image pre-processing. (a) Original image;(b) Dehazing image; (c) Enhanced image"

Fig.4

Algorithm framework of YOLOv2"

Tab.1

Architecture of convolutional neural network of Darknet-19"

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

Fig.5

Process of non-maximum suppression"

Fig.6

Bounding box regression"

Fig.7

Training results of carbon fiber prepreg surface defect"

Tab.2

YOLOv2 model training results comparison"

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

Tab.3

Tiny-YOLO multi-scale training results"

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

Fig.8

Detect results of carbon fiber prepreg surface defects. (a) Crack defect inspection results;(b) Hair mass defect detection results; (c) Hole defect detection results"

Tab.4

System detect effect%"

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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