Center Extraction of Structured Light Stripe Based on Back Propagation Neural Network

Yuehua Li; Peng Liu; Jingbo Zhou; Youzhi Ren; Jiangyan Jin

doi:10.3788/AOS201939.1212005

Acta Optica Sinica, Volume. 39, Issue 12, 1212005(2019)

Center Extraction of Structured Light Stripe Based on Back Propagation Neural Network

Yuehua Li, Peng Liu, Jingbo Zhou^*, Youzhi Ren, and Jiangyan Jin

Author Affiliations

School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China

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Figures & Tables(16)

Fig. 1. Selection of pixels of cross section profile

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Fig. 2. Basic principle of center computation of each column using neural network

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Fig. 3. Light stripes with different shapes for network training. (a) Falling stripe; (d) rising stripe; (c) horizontal stripe; (d) random stripe

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Fig. 4. Convergence curve of root mean square error

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Fig. 5. Gray value of stripe cross

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Fig. 6. Histogram of center extraction error

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Fig. 7. Center extraction results of strips with different shapes. (a) Arc stripe; (b) random stripe; (c) discontinuous stripe; (d) tooth stripe

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Fig. 8. Sample of straight line

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Fig. 9. Center extraction error of linear stipe for different numbers of hidden layer neurons. (a) Average value; (b) root mean square value

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Fig. 10. Center extraction result of stripe and error comparison. (a) Center extraction result of stripe using neural network; (b) comparison of center extraction errors

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Fig. 11. Comparison of center extraction results for different stripe qualities. (a) Original stripe; (b) under exposed stripe; (c) normal exposed stripe; (d) over exposed stripe

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Table 1. Mean square error and error distribution 3σ value under different noises
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Table 1. Mean square error and error distribution 3σ value under different noises
z₀ 0 5 10 15 20
3σ /pixel 0.0561 0.0627 0.0813 0.1419 0.1548
E_rms /pixel 0.0187 0.0209 0.0272 0.0479 0.0521

Table 2. Center extraction error for different numbers of hidden layers
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Table 2. Center extraction error for different numbers of hidden layers
Error L₁ L₂1 2 L₃1 2
1 2
E_rms /pixel 0.1505 0.2780 0.1564 0.2788 0.1596 0.2722
E_avr /pixel 0.1218 0.2253 0.1261 0.2200 0.1223 0.2135

Table 3. Stripe center extraction error from network using different training samples
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Table 3. Stripe center extraction error from network using different training samples
Error Fig. 3(a) Fig. 3(b) Fig. 3(c) Fig. 3(d)
E_rms /pixelE_avr /pixelE_md /pixel 0.20930.16480.8525 0.15710.12090.5325 0.23320.18590.7360 0.14860.11810.4969

Table 4. E_rms obtained by different center extraction methods for different angles between stripe and horizontal direction
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Table 4. E_rms obtained by different center extraction methods for different angles between stripe and horizontal direction
Angle /(°) E_rms /pixel
GGM Steger Our method
0 0.2669 0.1748 0.1405
20 0.2977 0.1664 0.1441
40 0.3683 0.1985 0.1721
60 0.4539 0.2956 0.2275
80 1.4576 1.4318 1.2533

Table 5. Run time of different center extraction methods
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Table 5. Run time of different center extraction methods
Sample Run time /s
Steger GGM Our method
Fig.7(a)Fig.10(a)Fig.11(a) 15.294415.097215.1043 0.01170.01210.0125 0.03970.04080.0402

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Yuehua Li, Peng Liu, Jingbo Zhou, Youzhi Ren, Jiangyan Jin. Center Extraction of Structured Light Stripe Based on Back Propagation Neural Network[J]. Acta Optica Sinica, 2019, 39(12): 1212005

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Paper Information

Category: Instrumentation, Measurement and Metrology

Received: Jun. 27, 2019

Accepted: Aug. 23, 2019

Published Online: Dec. 6, 2019

The Author Email: Jingbo Zhou (zhoujingbo@hebust.edu.cn)

DOI:10.3788/AOS201939.1212005

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Mean square error and error distribution 3σ value under different noises

Table 1. Mean square error and error distribution 3σ value under different noises

Table 2. Center extraction error for different numbers of hidden layers

Table 2. Center extraction error for different numbers of hidden layers

Table 3. Stripe center extraction error from network using different training samples

Table 3. Stripe center extraction error from network using different training samples

Table 4. Erms obtained by different center extraction methods for different angles between stripe and horizontal direction

Table 4. Erms obtained by different center extraction methods for different angles between stripe and horizontal direction

Table 5. Run time of different center extraction methods

Table 5. Run time of different center extraction methods

Table 4. E_rms obtained by different center extraction methods for different angles between stripe and horizontal direction

Table 4. E_rms obtained by different center extraction methods for different angles between stripe and horizontal direction