Fast Extraction Method of Stripe Center Based on Time-Continuous Frame Information Multiplexing

Jingya Yi; Tianguang Zhao; Yuxiang Wu; Zhangyu Ma; Jinyu Su

doi:10.3788/LOP202259.1912001

Laser & Optoelectronics Progress, Volume. 59, Issue 19, 1912001(2022)

Fast Extraction Method of Stripe Center Based on Time-Continuous Frame Information Multiplexing

Jingya Yi^1、*, Tianguang Zhao¹, Yuxiang Wu², Zhangyu Ma¹, and Jinyu Su¹

Author Affiliations

¹The Engineering Research Center for Intelligent Robotics, Jihua Laboratory, Foshan 538000, Guangdong , China

²School of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710000, Shaanxi , China

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Figures & Tables(15)

Fig. 1. Extraction process of laser fringe centerline

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Fig. 2. Workpiece and line laser image.(a) Workpiece;(b) line laser image; (c) image processed by adaptive gamma transform algorithm

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Fig. 3. Double threshold optical strip segmentation

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Fig. 4. Subpixel coordinate extraction. (a) Normal direction of centerline; (b) subpixel precision centerline

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Fig. 5. Flow chart of algorithm

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Fig. 6. Flow chart of nearest neighbor search algorithm

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Fig. 7. Diagram of nearest neighbor search

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Fig. 8. Experimental system image. (a) Measuring system; (b) scanning metal workpiece

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Fig. 9. Ten frames of continuous stripe pattern. (a) Frame 1; (b) frame 2; (c) frame 3; (d) frame 4; (e) frame 5; (f) frame 6; (g) frame 7; (h) frame 8; (i) frame 9; (j) frame 10

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Fig. 10. Root mean square error distribution of each method

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Fig. 11. 3D reconstruction of welding workpiece. (a) Metal workpiece; (b) 3D point cloud

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Fig. 12. 3D reconstruction of complex workpiece. (a) Metal workpiece; (b) 3D point cloud

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Table 1. Corresponding $[X_{m i n}, X_{m a x}]$ value for different search combinations

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Table 1. Corresponding $[X_{m i n}, X_{m a x}]$ value for different search combinations

Search combination	$P_{A}$ search abort condition	$P_{B}$ search abort condition	$X_{m i n}$ value	$X_{m a x}$ value
$\vec{A_{1}}$ 、 $\vec{B_{1}}$	$g_{r}^{j} (r, a - δ_{1}) < g_{T}$	$g_{r}^{j} (r, a + δ_{2}) < g_{T}$	$a - δ_{1} + 1$	$a + δ_{2} - 1$
$\vec{A_{1}}$ 、 $\vec{B_{2}}$	$g_{r}^{j} (r, a - δ_{1}) < g_{T}$	$g_{r}^{j} (r, a - δ_{2}) \geq g_{T}$	$a - δ_{1} + 1$	$a - δ_{2}$
$\vec{A_{2}}$ 、 $\vec{B_{1}}$	$g_{r}^{j} (r, a + δ_{1}) \geq g_{T}$	$g_{r}^{j} (r, a + δ_{2}) < g_{T}$	$a + δ_{1}$	$a + δ_{2} - 1$
$\vec{A_{2}}$ 、 $\vec{B_{2}}$	$g_{r}^{j} (r, a + δ_{1}) \geq g_{T}$ $⋂ a + δ_{1} < b g_{r}^{j} (r, a + δ_{1}) \geq g_{T}$ $⋂ a + δ_{1} < b$	$g_{r}^{j} (r, a + δ_{2}) < g_{T}$ $⋂ a + δ_{2} > a + δ_{1} g_{r}^{j} (r, a + δ_{2}) < g_{T}$ $⋂ a + δ_{2} > a + δ_{1}$	$a + δ_{1}$	$a - δ_{2}$
$\vec{A_{2}}$ 、 $\vec{B_{2}}$	$a + δ_{1} = b$	$a + δ_{2} \leq a + δ_{1}$	-1	-1

Table 2. Running time of different methods
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Table 2. Running time of different methods
Number Method Average time /ms
1 Steger 127.39
2 Gray centroid method 28.75
3 Adaptive segmentation method 33.51
4 Proposed method 18.78

Table 3. Error analysis
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Table 3. Error analysis
Number Method Standard deviation mean $\bar{σ}$ /pixel
1 Steger 0.01981
2 Gray centroid method 0.02825
3 Adaptive segmentation method 0.00506
4 Proposed method 0.00235

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Jingya Yi, Tianguang Zhao, Yuxiang Wu, Zhangyu Ma, Jinyu Su. Fast Extraction Method of Stripe Center Based on Time-Continuous Frame Information Multiplexing[J]. Laser & Optoelectronics Progress, 2022, 59(19): 1912001

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