Optical Cable Pitch Detection Method Based on Machine Vision

Fig. 4. Influence of different types of visual information. (a) Influence of class I sample cable point and cable ties; (b) influence of class II sample cable ties

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Fig. 5. Binarization results of original images. (a) Class I; (b) class II

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Fig. 6. Pretreatment process of class Ⅰ cable. (a) Original image; (b) thresholding; (c) denoising; (d) fitting

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Fig. 7. Pretreatment process of class Ⅱ cable. (a) Original image; (b) filtering; (c) grayscale stretching; (d) fitting

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Fig. 8. Results of matching. (a) Success for matching; (b) failure for matching

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Fig. 9. Template area division for different types of commutation points. (a) Model1; (b) model2; (c) model3

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Fig. 10. Flow chart of automatically constructing template

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Fig. 11. Best template

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Fig. 12. Physical image of detection system

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Fig. 13. Cable pretreatment results. (a) Class I; (b) class II

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Fig. 14. Matching results of template partition precise positioning method

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Fig. 15. Numerical results of different matching methods and different types of pitch errors. Direct template matching for (a) class I and (c) class II; template partition precise positioning method for (b) class I and (d) class II

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Table 1. Relationship between mean value of the template and matching error rate at different node gray level

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Table 1. Relationship between mean value of the template and matching error rate at different node gray level

Cable point gray
116.05		111.94		97.86
Errorrate /%	Averagegray	Errorrate /%	Averagegray	Errorrate /%	Averagegray
17.5	167	18.0	194	20.0	171
15.0	177	15.5	202	16.0	188
10.0	187	11.0	210	11.0	205
5.5	197	3.0	218	4.5	213
9.014.016.0	207217227	7.013.019.0	226234243	10.014.018.0	225236250

Table 2. Relationship between v' of optimal filtering structural elements matrix and mean values of node gray level
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Table 2. Relationship between v' of optimal filtering structural elements matrix and mean values of node gray level
Error rate/% 0 0 1 0 0
Matrix 100×20 120×24 150×30 200×40 220×44
Cable pointgray 116.05 111.94 106.33 97.86 90.36

Table 3. Comparison of common template matching and template partition precise positioning method
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Table 3. Comparison of common template matching and template partition precise positioning method
Method Cablepitch /pixel Variance /pixel Errorrate /%
Original 688 70 15.0
Precise positioning 711 3 0

Table 4. Average pitch and error results measured by two types of optical cables
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Table 4. Average pitch and error results measured by two types of optical cables
Class Length /m Cable pitch /pixel Variance /pixel
I 1000 694 4
II 1000 711 3

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Zhipeng Wu, Danping Huang, Kang Guo, Jianping Tian, Licheng Wu, Shaodong Yu. Optical Cable Pitch Detection Method Based on Machine Vision[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081505

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Paper Information

Category: Machine Vision

Received: Aug. 16, 2019

Accepted: Sep. 24, 2019

Published Online: Apr. 3, 2020

The Author Email: Danping Huang (hdpyx2002@163.com)

DOI:10.3788/LOP57.081505

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Relationship between mean value of the template and matching error rate at different node gray level

Table 1. Relationship between mean value of the template and matching error rate at different node gray level

Table 2. Relationship between v' of optimal filtering structural elements matrix and mean values of node gray level

Table 2. Relationship between v' of optimal filtering structural elements matrix and mean values of node gray level

Table 3. Comparison of common template matching and template partition precise positioning method

Table 3. Comparison of common template matching and template partition precise positioning method

Table 4. Average pitch and error results measured by two types of optical cables

Table 4. Average pitch and error results measured by two types of optical cables