Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques
Abstract
:1. Introduction
- (1)
- A hand-held visual sensor and online measurement system based on laser triangulation and deep learning technique for box dimension measurement are proposed.
- (2)
- A valid dataset of the laser-box images is created, and an effective structured edge detection and key point detection approach based on a Trimmed-HED network and straight-line processing are proposed.
- (3)
- An optimization method is proposed to achieve the robust calibration of the visual sensor.
2. Materials and Methods
3. Proposed Algorithmic Procedure
3.1. Dimension Measurement Principle
3.2. Automatic Calibration for the Visual Sensor
3.2.1. Parameter Calibration of the Visual Sensor
3.2.2. Optimization for Calibration Parameters by Analyzing the Probability Distributions and Outlier Removal
- (1)
- Acquire N calibration pattern images with laser stripes in different positions, and feature points and calibration points can be detected successfully from these images.
- (2)
- Select M images from the N calibration pattern images randomly in Step (1) to form a new image set, and there would be form CN M subsets image total, where CN M is a binomial coefficient.
- (3)
- The internal and external parameters of the visual sensor by these subsets image in Step (2) are calculated respectively with the method described above. A total of CN M sets of parameter data are generated to form a matrix of Rn×12, where each row in the matrix corresponds to the parameters (α,β,u0,v0,k1,k2,a1,b1,c1,a2,b2,c2) of the device calculated by each subset of images.
- (4)
- For each column parameter of the Rn×12 matrix in Step (3), calculate the mean mp and standard deviation σp via the method of maximum likelihood estimation of a normal distribution.
- (5)
- From the matrix parameters in Step (4), remove the row in which value of at least one parameter data (α,β,u0,v0,k1,k2,a1,b1,c1,a2,b2,c2) not lie inside the range of [m − 3σ,m + 3σ] to form a new matrix and repeat the operation of Step (4) for the new matrix until no data were removed from the new matrix.
- (6)
- The nature mean of each column of the final matrix in Step (5) is used as the final internal and external parameters of the visual sensor.
3.2.3. Experimental Verification and Accuracy Assessment
3.3. Image Processing for the Laser-Box Image
3.3.1. Detecting Structured Edge Map
3.3.2. Detecting 2D Key Points via the Hough Transformation
- Step 1.
- The Hough line transform was used to detect the straight line ρ = xcos(θ) + ysin(θ) from the structured edge maps of the laser-box images and transformed from each straight line to the parameter space.
- Step 2.
- (ρ,θ) for many cells was quantified, and an accumulator for each interval area was created. For every pixel (x,y) in the structured edge map of the laser-box image, the quantized value (ρ,θ) was computed, and the nearly collinear line segments were clustered by a suitable threshold for ρ and θ.
- Step 3.
- The image space lines composed of the N first (ρ,θ) in Step 2 were obtained and fitted via the LSM. N is 6 in this study.
4. Experimental Results
4.1. Measurement Statistical Analysis Experimental of Varying Orientations of the Measurement Object
4.2. Measurement Statistical Analysis Experimental of the Changing Distance between the Visual Sensor and the Measured Box
4.3. Stability Analysis and Evaluation of Uncertainty in the Measurement Experimental of the Measurement System
4.4. Measurement Statistical Analysis Experimental for Various Boxes in Different Scenarios
4.5. Measurement Result in Real Applications
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Measurement | Value |
---|---|
Mean RMS calculated using 10 sets of calibration pattern images | 0.0568 |
RMS calculated using the true parameters | 0.0215 |
Parameters | Minimal Value | Maximal Value | True Value |
---|---|---|---|
α | 2345.064 | 2363.778 | 2353.85 |
β | 2358.273 | 2377.399 | 2367.13 |
u0 | 1244.827 | 1262.702 | 1254.69 |
v0 | 1007.973 | 1026.720 | 1018.36 |
k1 | −0.0241551 | −0.008247 | −0.016697 |
k2 | 0.155643 | 0.3567460 | 0.232305 |
a1 | 0.00905 | 0.0160500 | 0.01021 |
b1 | −0.013303 | −0.009352 | −0.010614 |
c1 | 0.0019727 | 0.002677 | 0.002166 |
a2 | 0.0095584 | 0.013372 | 0.010326 |
b2 | 0.009839 | 0.016606 | 0.010347 |
c2 | 0.0018325 | 0.0025490 | 0.002031 |
ODS | OIS | AP | |
---|---|---|---|
Original HED | 0.490 | 0.566 | 0.539 |
Original HED (with our data set) | 0.621 | 0.662 | 0.648 |
Trimmed-HED (with deep supervision) | 0.753 | 0.783 | 0.776 |
Trimmed-HED (without deep supervision) | 0.803 | 0.816 | 0.809 |
Measurement at Different Angles (The Measurement Error (mm) in Brackets) [The Relative Error (%) in Square Brackets] | |||||||
---|---|---|---|---|---|---|---|
Dim | Ground Truth | 90° | 75° | 60° | 45° | 30° | |
(a) | W | 190.0 | 190.0 | 189.3 (−0.7) [0.37%] | 190.9 (+0.9) [0.47%] | 191.1 (+1.1) [0.58%] | 188.2 (−1.8) [0.95%] |
L | 253.0 | 253.0 | 252.2 (−0.8) [0.32%] | 251.8 (−1.2) [0.47%] | 251.7 (−1.3) [0.51%] | 254.9 (+1.9) [0.75%] | |
H | 400.0 | 400.0 | 400.9 (+0.9) [0.23%] | 401.6 (+1.6) [0.40%] | 401.9 (+1.9) [0.48%] | 396.2 (−3.8) [0.95%] | |
(b) | W | 320.0 | 320.0 | 320.6 (+0.6) [0.19%] | 321.5 (+1.5) [0.47%] | 321.6 (+1.6) [0.50%] | 317.3 (−2.7) [0.84%] |
L | 320.0 | 320.0 | 320.9 (+0.9) [0.28%] | 319.6 (−0.4) [0.13%] | 321.4 (+1.4) [0.44%] | 318.5 (−1.5) [0.47%] | |
H | 620.0 | 620.0 | 621.3 (+1.3) [0.21%] | 622.4 (+2.4) [0.39%] | 617.5 (−2.5) [0.4%] | 623.5 (+3.5) [0.56%] |
Box | Dim | Ground Truth | Measurement at Three Box-Sensor Distances (mm) (The Measurement Error (mm) in Brackets) [The Relative Error (%) in Square Brackets.] | ||||
---|---|---|---|---|---|---|---|
/mm | 800 | 1200 | 1600 | 2000 | 2400 | ||
(a) | W | 750.0 | 750.5 (+0.5) [0.07%] | 751.2 (+1.2) [0.16%] | 747.7 (−2.3) [0.31%] | 746.4 (−3.6) [0.48%] | 744.6 (−5.4) [0.72%] |
L | 495.0 | 494.8 (−0.2) [0.04%] | 493.9 (−1.1) [0.22%] | 496.1 (+1.1) [0.22%] | 492.4 (−2.6) [0.53%] | 490.4 (−4.6) [0.93%] | |
H | 330.0 | 330.4 (+0.4) [0.12%] | 330.8 (+0.8) [0.24%] | 329.0 (−1.0) [0.30%] | 326.9 (−3.1) [0.94%] | 333.9 (+3.9) [1.18%] | |
(b) | W | 480.0 | 479.7 (−0.3) [0.63%] | 480.8 (+0.8) [0.17%] | 478.5 (−1.5) [0.31%] | 483.0 (+3.0) [0.63%] | 485.3 (+5.3) [1.10%] |
L | 550.0 | 549.6 (−0.4) [0.07%] | 550.6 (+0.6) [0.11%] | 548.1 (−1.9) [0.35%] | 552.4 (+2.4) [0.44%] | 555.4 (+5.4) [0.98%] | |
H | 380.0 | 380.4 (+0.4) [0.11%] | 379.1 (−0.9) [0.24%] | 381.3 (+1.3) [0.34%] | 382.6 (+2.6) [0.68%] | 376.3 (−3.7) [0.97%] | |
(c) | W | 450.0 | 450.5 (+0.5) [0.11%] | 449.6 (−0.4) [0.09%] | 451.4 (+1.4) [0.31%] | 446.8 (−3.2) [0.71%] | 454.8 (+4.8) [1.07%] |
L | 650.0 | 649.1 (−0.9) [0.14%] | 648.7 (−1.3) [0.20%] | 648.4 (−1.6) [0.25%] | 654.2 (+4.2) [0.65%] | 655.8 (+5.8) [0.89%] | |
H | 350.0 | 350.1 (+0.1) [0.03%] | 350.5 (+0.5) [0.14%] | 348.8 (−1.2) [0.34%] | 353.3 (+3.3) [0.94%] | 346.7 (−3.3) [0.94%] |
No. | W (a) | L (a) | H (a) | W (b) | L (b) | H (b) | W (c) | L (c) | H (c) | W (d) | L (d) | H (d) |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 110.5 | 410.1 | 620.3 | 391.6 | 239.9 | 530.6 | 1111.7 | 749.1 | 880.1 | 689.8 | 570.2 | 1500.9 |
2 | 109.4 | 409.6 | 620.4 | 390.2 | 240.1 | 529.1 | 1109.3 | 750.9 | 879.3 | 688.3 | 570.6 | 1501.2 |
3 | 107.6 | 411.6 | 619.6 | 390.5 | 241.9 | 531.2 | 1112.2 | 752.3 | 881.7 | 690.4 | 569.3 | 1499.7 |
4 | 110.2 | 411.8 | 621.1 | 389.4 | 240.7 | 531.7 | 1109.5 | 751.7 | 882.6 | 689.7 | 568.9 | 1496.5 |
5 | 110.3 | 410.6 | 620.2 | 388.3 | 238.6 | 529.6 | 1111.3 | 747.1 | 879.4 | 687.3 | 571.2 | 1502.3 |
6 | 109.8 | 408.3 | 621.8 | 390.6 | 239.9 | 528.7 | 1109.6 | 749.3 | 878.2 | 691.2 | 570.3 | 1505.3 |
7 | 109.2 | 410.3 | 617.6 | 391.5 | 238.9 | 529.1 | 1113.3 | 750.6 | 880.9 | 690.3 | 571.4 | 1496.8 |
8 | 110.7 | 408.1 | 619.7 | 391.4 | 241.6 | 530.1 | 1112.5 | 748.3 | 881.1 | 691.5 | 567.6 | 1498.3 |
9 | 110.6 | 409.5 | 618.3 | 388.6 | 240.1 | 527.6 | 1108.7 | 750.2 | 882.3 | 689.2 | 568.4 | 1502.4 |
10 | 111.3 | 410.2 | 621.8 | 391.2 | 240.9 | 528.9 | 1108.1 | 751.6 | 881.6 | 688.4 | 571.9 | 1497.9 |
11 | 111.5 | 411.3 | 617.4 | 390.4 | 241.5 | 529.6 | 1111.6 | 750.3 | 880.9 | 691.3 | 570.7 | 1500.8 |
12 | 110.1 | 412.3 | 620.5 | 389.2 | 238.6 | 530.8 | 1109.8 | 749.7 | 880.4 | 691.5 | 568.3 | 1496.3 |
13 | 109.6 | 408.6 | 619.7 | 388.6 | 239.2 | 530.7 | 1107.2 | 748.6 | 879.8 | 692.6 | 569.6 | 1498.4 |
14 | 108.3 | 409.9 | 621.9 | 389.2 | 240.5 | 531.8 | 1111.5 | 747.9 | 876.4 | 690.7 | 571.6 | 1503.6 |
15 | 109.2 | 410.7 | 619.5 | 391.7 | 241.1 | 528.4 | 1110.6 | 750.6 | 881.5 | 689.1 | 569.5 | 1502.7 |
Mean | 109.88 | 410.19 | 619.98 | 390.16 | 240.23 | 529.86 | 1110.46 | 749.88 | 880.41 | 690.08 | 569.96 | 1500.20 |
Ave_Err | 0.94 | 1.01 | 1.26 | 1.09 | 0.91 | 1.2 | 1.46 | 1.36 | 1.20 | 1.22 | 1.12 | 2.35 |
Std | 1.01 | 1.21 | 1.36 | 1.15 | 1.03 | 1.20 | 1.68 | 1.44 | 1.57 | 1.39 | 1.26 | 2.68 |
μA | 1.05 | 1.25 | 1.41 | 1.19 | 1.07 | 1.25 | 1.74 | 1.49 | 1.63 | 1.44 | 1.30 | 2.77 |
No. | (a) | (b) | (c) | (d) | ||||
Box | | | | | | | | |
Edge map | | | | | | | | |
Key points | | | | | | | | |
Results | L: 99.5 (−0.5) W: 220.6(+0.6) H: 349.6(−0.4) | L: 301.2(+0.6) W: 368.3(−2.2) H: 428.5(−1.5) | L: 282.4(+0.4) W: 291.1(+1.1) H: 318.2(+2.2) | L: 294.2(+1.2) W: 312.3(+2.3) H: 349.8(−0.2) | ||||
No. | (e) | (f) | (g) | (h) | ||||
Box | | | | | | | | |
Edge map | | | | | | | | |
Key points | | | | | | | | |
>Results | L: 256.3(+1.3) W: 500.6 (−9.4) H: 811.3(+11.3) | L: 172.3(+1.8) W: 222.7(+2.1) H: 332.6(+2.1) | L: 170.1(−0.4) W: 221.9(+1.3) H: 331.5(+1.0) | L: 319.9(−0.1) W: 318.3(−1.7) H: 623.8(+3.8) |
No. | Actual Length/mm | Measured Length/mm | Length Error/mm | Relative Error of Length (%) | Volume Error/m3 | Relative Error of Volume (%) |
---|---|---|---|---|---|---|
(a) | 250.5 | 250.3 | −0.2 | 0.079% | −0.00019 | 0.476% |
350.8 | 350.1 | −0.7 | 0.199% | |||
454.6 | 453.7 | −0.9 | 0.197% | |||
(b) | 560.5 | 561.6 | +1.1 | 0.196% | 0.000130 | 0.125% |
430.5 | 431.3 | +0.8 | 0.185% | |||
430.5 | 429.4 | −1.1 | 0.255% | |||
(c) | 400.0 | 402.3 | +2.3 | 0.575% | 0.000175 | 0.488% |
450.0 | 452.1 | +2.1 | 0.466% | |||
200.0 | 198.9 | −1.1 | 0.550% | |||
(d) | 520.0 | 523.6 | +3.6 | 0.690% | 0.000193 | 0.164% |
440.0 | 438.3 | −1.7 | 0.386% | |||
515.0 | 513.6 | −2.4 | 0.466% | |||
(e) | 220.4 | 220.0 | −0.4 | 0.181% | −0.00012 | 0.451% |
300.7 | 299.4 | −1.3 | 0.432% | |||
430.5 | 431.2 | +0.7 | 0.162% | |||
(f) | 288.0 | 288.1 | +0.1 | 0.034% | −0.00001 | 0.039% |
288.0 | 287.6 | −0.4 | 0.138% | |||
310.0 | 310.2 | +0.2 | 0.064% | |||
(g) | 1350.0 | 1354.5 | +4.5 | 0.333% | 0.000450 | 0.673% |
330.0 | 329.8 | −0.2 | 0.060% | |||
150.0 | 150.6 | +0.6 | 0.400% | |||
(h) | 1800.0 | 1807.6 | +7.6 | 0.422% | 0.003262 | 0.503% |
900.0 | 904.8 | +4.8 | 0.533% | |||
400.0 | 398.2 | −1.8 | 0.450% |
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Peng, T.; Zhang, Z.; Chen, F.; Zeng, D. Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques. Appl. Sci. 2020, 10, 26. https://doi.org/10.3390/app10010026
Peng T, Zhang Z, Chen F, Zeng D. Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques. Applied Sciences. 2020; 10(1):26. https://doi.org/10.3390/app10010026
Chicago/Turabian StylePeng, Tao, Zhijiang Zhang, Fansheng Chen, and Dan Zeng. 2020. "Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques" Applied Sciences 10, no. 1: 26. https://doi.org/10.3390/app10010026
APA StylePeng, T., Zhang, Z., Chen, F., & Zeng, D. (2020). Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques. Applied Sciences, 10(1), 26. https://doi.org/10.3390/app10010026