Key Contour Feature Extraction of Printed Circuit Board Point Cloud

Fig. 4. Comparison of feature line extraction effects of 6 different algorithms on printed circuit board. (a) Raw point cloud; (b) r search algorithm; (c) k-NNS algorithm; (d) concave polygon algorithm; (e) convex hull algorithm; (f) RANSAC algorithm; (g) proposed algorithm

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Fig. 5. Comparison diagrams of the extraction effects. (a)(c) k-NNS algorithm; (b)(d) r search algorithm

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Fig. 6. Comparison diagrams of the extraction effects. (a)(c) RANSAC algorithm; (b)(d) proposed algorithm

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Fig. 7. Part contour size of the printed circuit board

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Table 1. Euclidean clustering based on normal vector angle

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Table 1. Euclidean clustering based on normal vector angle

Input:Point cloud: P= $\{p_{1}, \dots, p_{n}\}$ Parameter: i,j,k,N_min,N_max,d_th,p_r,n,α_thVector: C,Q[j]
1: For i=0 to size $\{P\}$ do
2: If p_r[i]=true then3: continue
4: Q.push_back(i)5: p_r[i]=true6: End if7: For j=1 to j do8: set p_q=p_j as query point9: Use kd-tree to find the k nearest neighbor $p_{i}^{k}$
10: If L(p_q, $p_{i}^{k}$ )≤d_th & arccos (<n_q,n_k>)≤α_th then11: Add $p_{i}^{k}$ to Q[j]12: j=j+113: p_r[i]=true14: End if15:End for
16: If N_min≤Q $[size]$ ≤N_max then17: C.push_back Q[j]
18: clear Q19: i=i+120: End if21: End for22:Return C

Table 2. Statistical results of number of reserved feature points after boundary point extraction
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Table 2. Statistical results of number of reserved feature points after boundary point extraction
Algorithm Number of reservedfeature points Time /s
r search 1658 178.0
k-NNS 4525 24.0
Concave polygon 281 4.0
Convex hull 183 2.0
RANSAC 6816 28.3
Proposed algorithm 7801 20.8

Table 3. Time-consuming of each sub-module of proposed algorithm
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Table 3. Time-consuming of each sub-module of proposed algorithm
Algorithm module Time /s
Pre-processing 0.096
Detection of maximum plane extraction 1.572
Remaining point extraction 1.672
Boundary 1 extraction 2.773
Boundary 2 extraction 0.474
B1∪B2 0.219
Total time 6.806

Table 4. Comparison between measurement results of contour size and actual values

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Table 4. Comparison between measurement results of contour size and actual values

Key contour size	Measurement result /mm	Actual value /mm	Actual error /mm	Relative error /%
L1	148.502	148.600	-0.098	-0.065
L2	115.739	115.840	-0.101	-0.087
L3	16.320	16.200	0.120	0.740
L4	16.233	16.260	-0.027	-0.166
L5	14.921	14.880	0.041	0.275

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Wenbin Zhong, Xurui Li, Si Sun, Guangshuai Liu. Key Contour Feature Extraction of Printed Circuit Board Point Cloud[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141001

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Paper Information

Category: Image Processing

Received: Oct. 8, 2019

Accepted: Nov. 25, 2019

Published Online: Jul. 24, 2020

The Author Email: Xurui Li (1252870628@qq.com)

DOI:10.3788/LOP57.141001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Euclidean clustering based on normal vector angle

Table 1. Euclidean clustering based on normal vector angle

Table 2. Statistical results of number of reserved feature points after boundary point extraction

Table 2. Statistical results of number of reserved feature points after boundary point extraction

Table 3. Time-consuming of each sub-module of proposed algorithm

Table 3. Time-consuming of each sub-module of proposed algorithm

Table 4. Comparison between measurement results of contour size and actual values

Table 4. Comparison between measurement results of contour size and actual values