Performance Assessment of Reference Modelling Methods for Defect Evaluation in Asphalt Concrete
<p>Mosaic composed of orthoimages of examined 8 plots, top row plots 1–4, bottom row plots 5–8. The black stick with white targets that is visible in most images measures 2 m.</p> "> Figure 2
<p>Pavement samples used for reference measurement. Samples in ID number order, from left to right, 1–5.</p> "> Figure 3
<p>Reference measurement setup. Samples are in ID order 1–5 from left to right.</p> "> Figure 4
<p>Difference image showing distance to reference model of pavement sample 3, as measured using high-resolution photogrammetry. Reference model is pictured as transparent white.</p> "> Figure 5
<p>A section of the road profile from plot 1 showing Nikon and TLS point clouds. Photogrammetric data is 1 mm deep, while TLS data is 3 mm deep for visual clarity.</p> "> Figure 6
<p>Three mm deep profiles of defect 1 as measured by all instruments in an arbitrary coordinate system.</p> "> Figure 7
<p>Three mm deep profiles of plot 7 as measured by all instruments in an arbitrary coordinate system.</p> "> Figure 8
<p>Three mm deep profiles of plot 1 as measured by all instruments in an arbitrary coordinate system.</p> "> Figure 9
<p>Three mm deep profiles of defect 27 as measured by all instruments in an arbitrary coordinate system.</p> "> Figure 10
<p>Correlation between defect size and standard deviation in point–point distances.</p> "> Figure 11
<p>Orthoimage of defect 27 (plot 7).</p> "> Figure 12
<p>Point clouds of defect 27 (plot 7), colored based on vertical distance to nearest point in Nikon point cloud.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Test Site
2.2. Instruments and Data Processing
2.2.1. High-Resolution Photogrammetry
2.2.2. Industrial Camera Photogrammetry
2.2.3. Terrestrial Laser Scanning
2.2.4. Artec Leo
2.2.5. Faro Freestyle
2.3. Reference Measurements
2.3.1. Pavement Samples
2.3.2. Reference Measurement Setup
2.3.3. Reference Measurement Analysis
2.4. Data Analysis
2.4.1. Plot-Level and Cross-Section Analysis
2.4.2. Defect-Level Analysis: Point–Point Distances
2.4.3. Defect Analysis: Volume and Maximum Depth
2.4.4. Qualitative Experiences in Usability and Efficiency
3. Results
3.1. Reference Measurement Results
3.2. Photogrammetric Reconstruction
3.3. Field Measurement Point Clouds
3.4. Cross-Section and Graphical Analysis
3.5. Point–Point Distances
3.6. Volume and Defect Depth
4. Discussion
4.1. Reference Measurements and Systematic Error
4.2. Evaluating Usability and Efficiency
4.2.1. Measurement and Processing Times
4.2.2. Other Considerations
4.3. Future Research
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Distress Type | Mechanism | Manifestation |
---|---|---|
Cracking | Fractures of surface and fundamental pavement layers | Transverse, longitudinal, edge, block, alligator cracks |
Disintegration | Progressive division of pavement into loose pieces | Potholes, patches |
Surface deformation | Change in pavement structure | Shoving, rutting, distortion |
Surface defects | Loss of surface microtexture or macrotexture | Raveling, bleeding |
Plot Number | Location (ETRS-TM35FIN) |
---|---|
1 | (363545, 6672374) |
2 | (363602, 6672639) |
3 | (363634, 6672740) |
4 | (363659, 6672781) |
5 | (363723, 6672860) |
6 | (363801, 6672933) |
7 | (363995, 6673252) |
8 | (363955, 6673569) |
Nikon D800E | Nikon D810 | |
---|---|---|
24 mm Lens | 50/60 mm Lens | |
Aperture | 8 | 14 |
ISO | 100–800 | 800–1600 |
Shutter speed | 1/80–1/125 s | 1/125 s |
Alignment Settings | |
Engine | RealityCapture |
Mode | High |
Max features per Mpx | 0 |
Max features per image | 0 |
Detector sensitivity | Medium |
Preselector features | 10,000 |
Image downscale factor | 1 |
Maximal feature reprojection error [pixels] | 3.00 |
Use camera positions | True |
Lens distortion model | K + Brown3 with tangential2 |
Final optimization | True |
Model Generation Settings | |
Quality level | High |
Alignment Settings | |
Accuracy | Highest |
Generic preselection | Yes |
Reference preselection | Sequential |
Key point limit | 0 |
Tie point limit | 0 |
Filter points by mask | No |
Mask tie points | No |
Guided image matching | No |
Adaptive camera model fitting | Yes |
Depth Map Processing Settings | |
Quality | Ultra High |
Filtering mode | Aggressive |
Specification | Artec Leo | Faro Freestyle |
---|---|---|
3D point accuracy | 0.1 mm | 0.5 mm |
3D point resolution | 0.2 mm | 0.2 mm |
RMS noise @ 0.8 m range | – | 0.8 mm |
Working distance | 0.35–1.2 m | 0.5–3 m |
3D reconstruction rate | 22 fps | – |
Data acquisition speed | 35 Mpts/s | 88 kpts/s |
3D light source | VCSEL | LED flash |
Position sensing | 9-DOF inertial system | – |
ID | Diameter (mm) | Height (mm) | Description |
---|---|---|---|
1 | 102 | 31 | Fresh, bituminous and coarse pavement |
2 | 102 | 29 | Heavily worn, low bitumen |
3 | 102 | 29 | Heavily worn and cracked |
4 | 95 | 62 | Slightly worn, large grain |
5 | 153 | 59 | Artificially smooth |
Nikon | GH | TLS | Leo | FF | |
---|---|---|---|---|---|
n | 934,097 | 1,087,748 | 50,585 | 4480 | 8183 |
(mm) | 0.34 | 0.25 | 0.43 | 0.30 | 1.22 |
(mm) | 0.39 | 0.15 | 0.41 | 0.18 | 0.98 |
(mm) | 0.15 | 0.18 | 0.41 | 0.20 | 0.65 |
(mm) | 0.13 | 0.15 | 0.35 | 0.15 | 0.49 |
(mm) | 0.20 | 0.20 | 0.37 | 0.20 | 0.49 |
(mm) | 0.24 | 0.19 | 0.31 | 0.21 | 0.74 |
(mm) | 0.26 | 0.11 | 0.35 | 0.13 | 0.54 |
(mm) | 0.10 | 0.14 | 0.31 | 0.14 | 0.46 |
(mm) | 0.06 | 0.08 | 0.29 | 0.08 | 0.38 |
(mm) | 0.08 | 0.07 | 0.22 | 0.10 | 0.33 |
(mm) | 0.24 | 0.19 | 0.40 | 0.20 | 0.77 |
(mm) | 0.15 | 0.12 | 0.30 | 0.13 | 0.48 |
Plot Number | Tie Points (Nikon) | Nikon Mean Reprojection Error (Pixels) | Tie Points (GH) | GH RMS Reprojection Error (Pixels) |
---|---|---|---|---|
1 | 9,708,844 | 0.39 | 10,144,098 | 0.33 |
2 | 13,903,757 | 0.38 | 3,203,031 | 0.54 |
3 | 15,743,599 | 0.46 | 15,101,018 | 0.35 |
4 | 11,888,215 | 0.43 | 15,381,474 | 0.47 |
5 | 9,893,413 | 0.51 | 12,773,785 | 0.54 |
6 | 16,196,109 | 0.43 | 4,469,455 | 0.71 |
7 | 14,784,526 | 0.39 | 11,174,053 | 0.47 |
8 | 15,778,640 | 0.43 | 15,778,194 | 0.42 |
Plot Number | Nikon | GH | TLS | Leo | Freestyle |
---|---|---|---|---|---|
1 | 140,396,091 | 310,755,752 | 6,389,651 | 6,001,849 | 11,463,027 |
2 | 287,491,907 | 335,736,791 | 3,534,097 | 18,146,934 | 3,447,980 |
3 | 252,277,528 | 327,961,220 | 5,101,989 | 6,009,833 | 2,522,223 |
4 | 255,212,221 | 650,398,114 | 6,126,542 | 1,626,388 | 3,718,867 |
5 | 260,599,273 | – | 7,157,959 | 2,013,197 | 2,984,767 |
6 | 238,366,658 | 535,690,203 | 7,336,520 | 1,616,939 | 7,336,520 |
7 | 216,517,583 | 487,028,634 | 7,252,653 | 2,009,979 | 2,045,904 |
8 | 238,496,328 | 455,029,861 | 8,396,739 | 2,005,995 | 791,550 |
Plot Number | Nikon | GH | TLS | Leo | Freestyle |
---|---|---|---|---|---|
1 | 0.11 | 0.04 | 2.01 | 3.22 | 0.92 |
2 | 0.05 | 0.05 | 6.46 | 0.85 | 1.43 |
3 | 0.05 | 0.06 | 7.43 | 3.17 | 1.37 |
4 | 0.05 | 0.03 | 3.81 | 7.80 | 1.54 |
5 | 0.06 | – | 2.59 | 6.64 | 1.38 |
6 | 0.05 | 0.03 | 2.17 | 9.03 | 1.40 |
7 | 0.04 | 0.02 | 2.61 | 7.40 | 1.94 |
8 | 0.05 | 0.02 | 3.11 | 9.69 | 6.54 |
Defect Number | Plot Number | Description | Volume (mL) |
---|---|---|---|
1 | 1 | pothole | 13,361 |
2 | 1 | longitudinal crack | 1141 |
3 | 1 | longitudinal crack & small pothole | 3547 |
4 | 2 | longitudinal crack | 3806 |
5 | 2 | longitudinal crack | 626 |
6 | 2 | alligator crack | 1342 |
7 | 2 | transverse crack | 288 |
9 | 4 | longitudinal crack | 11 |
10 | 4 | longitudinal crack | 119 |
11 | 4 | longitudinal crack | 264 |
12 | 4 | longitudinal crack | 286 |
13 | 4 | longitudinal crack | 414 |
14 | 5 | transverse crack | 16 |
15 | 5 | transverse crack | 10 |
16 | 5 | crack | 151 |
17 | 5 | raveling | 3371 |
18 | 6 | longitudinal crack | 792 |
19 | 6 | crack | 19 |
20 | 6 | crack | 23 |
21 | 6 | longitudinal crack | 10 |
22 | 6 | long central line pothole | 2233 |
23 | 6 | longitudinal crack | 687 |
24 | 7 | transverse crack | 7 |
25 | 7 | pothole and transverse crack | 1504 |
26 | 7 | longitudinal crack | 141 |
27 | 7 | longitudinal crack & filled pothole | 543 |
28 | 7 | wide longitudinal crack | 5739 |
29 | 7 | longitudinal crack | 180 |
31 | 8 | longitudinal/alligator crack | 1627 |
32 | 8 | longitudinal crack | 40 |
33 | 8 | longitudinal crack | 507 |
34 | 3 | crack network | 4796 |
35 | 3 | raveling | 1506 |
36 | 3 | wide longitudinal crack | 784 |
Nikon | GH | TLS | Leo | FF | ||
---|---|---|---|---|---|---|
Nikon | mean | 0.829 | 0.595 | 1.026 | 1.061 | |
std dev | 0.787 | 0.502 | 0.969 | 0.993 | ||
GH | mean | 0.829 | 0.613 | 1.005 | 0.994 | |
std dev | 0.787 | 0.529 | 0.972 | 1.016 | ||
TLS | mean | 0.595 | 0.613 | 1.458 | 1.429 | |
std dev | 0.502 | 0.529 | 0.930 | 0.928 | ||
Leo | mean | 1.026 | 1.005 | 1.458 | 1.840 | |
std dev | 0.969 | 0.972 | 0.930 | 1.109 | ||
FF | mean | 1.061 | 0.994 | 1.429 | 1.840 | |
std dev | 0.993 | 1.016 | 0.928 | 1.109 | ||
mean | mean | 0.878 | 0.860 | 1.024 | 1.332 | 1.331 |
std dev | 0.813 | 0.826 | 0.722 | 0.995 | 1.012 |
Method | Mean Offset (mL) | Standard Deviation (mL) | Mean Relative Offset (%) | Std. Dev of Relative Offset (%) |
---|---|---|---|---|
GH | 145 | 356 | 5.8 | 18.7 |
TLS | 31 | 183 | 3.7 | 15.6 |
Leo | 189 | 530 | 5.4 | 32.8 |
FF | −103 | 539 | −15.4 | 25.2 |
Method | Mean Offset (mm) | Standard Deviation (mm) |
---|---|---|
GH | 4.0 | 5.0 |
TLS | 0.7 | 1.1 |
Leo | 1.9 | 2.9 |
FF | 7.0 | 22.4 |
Method | Measuring Time (Minutes) | Processing Time (Minutes) | Requires Targets or Markers |
---|---|---|---|
Nikon | 10 | 1400 | For scale |
GH | 5 | 1400 | For scale |
TLS | 20 | 120 | For registration |
Leo | 2 | 300 | No |
FF | 4 | 1 | For drift prevention |
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Putkiranta, P.; Kurkela, M.; Ingman, M.; Keitaanniemi, A.; El Issaoui, A.; Kaartinen, H.; Honkavaara, E.; Hyyppä, H.; Hyyppä, J.; Vaaja, M.T. Performance Assessment of Reference Modelling Methods for Defect Evaluation in Asphalt Concrete. Sensors 2021, 21, 8190. https://doi.org/10.3390/s21248190
Putkiranta P, Kurkela M, Ingman M, Keitaanniemi A, El Issaoui A, Kaartinen H, Honkavaara E, Hyyppä H, Hyyppä J, Vaaja MT. Performance Assessment of Reference Modelling Methods for Defect Evaluation in Asphalt Concrete. Sensors. 2021; 21(24):8190. https://doi.org/10.3390/s21248190
Chicago/Turabian StylePutkiranta, Pauli, Matti Kurkela, Matias Ingman, Aino Keitaanniemi, Aimad El Issaoui, Harri Kaartinen, Eija Honkavaara, Hannu Hyyppä, Juha Hyyppä, and Matti T. Vaaja. 2021. "Performance Assessment of Reference Modelling Methods for Defect Evaluation in Asphalt Concrete" Sensors 21, no. 24: 8190. https://doi.org/10.3390/s21248190