Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor
Abstract
:1. Introduction and Background
2. Literature Review
3. Objective and Scope
4. Research Methodology
4.1. Data Collection
4.2. Data Pre-Processing
4.2.1. Performing Noise Reduction Techniques
4.2.2. 3-D Surface Reconstruction
4.3. Data Post-Processing
4.3.1. IRI Calculation and Repeatability Controls
4.3.2. Validation
5. Results and Discussions
5.1. IRI Calculation for Different Pavement Textures
5.2. Statistical Repeatability Controls
5.3. Cross-Correlation Analysis
5.4. Study of Power Spectral Density (PSD)
5.5. Validation
6. Future Work
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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IRI Values (m/km) | Frequency | Average (m/km) | Standard Deviation (m/km) | Standard Error | 95% Confidence Interval | Min IRI (m/km) | Max IRI (m/km) |
---|---|---|---|---|---|---|---|
2–4 | 48 | 3.29 | 0.45 | 0.066 | 3.15–3.42 | 2.44 | 3.98 |
4–6 | 23 | 4.79 | 0.47 | 0.097 | 4.59–4.99 | 4.02 | 5.7 |
6–8 | 15 | 6.45 | 0.39 | 0.1 | 6.24–6.67 | 6.01 | 7.1 |
8–10 | 4 | 8.63 | 0.23 | 0.23 | 7.89–9.38 | 8.16 | 9.23 |
IRI Values (m/km) | Pavement Type | Frequency | Average (m/km) | Standard Deviation (m/km) | Standard Error | 95% Confidence Interval | Min IRI (m/km) | Max IRI (m/km) |
---|---|---|---|---|---|---|---|---|
1.5–3.5 | New pavements | 31 | 3.01 | 0.31 | 0.055 | 2.9–3.13 | 2.44 | 3.5 |
2.5-6 | Older pavement | 70 | 3.79 | 0.83 | 0.099 | 3.59–3.99 | 2.53 | 5.70 |
3.5-10 | Maintained unpaved | 60 | 5.16 | 1.42 | 0.183 | 4.79–5.52 | 3.5 | 9.23 |
Segments | Average of IRI (SD) (m/km) | CoV | Sum of Square | Mean Square (Between Groups) | Significance | F. |
---|---|---|---|---|---|---|
First | 3.63 (0.144) | 0.040 | 0.02 | 0.005 | 1 | 0.15 |
Second | 7.12 (0.225) | 0.032 | 0.032 | 0.008 | 1 | 0.007 |
Third | 5.22 (0.203) | 0.039 | 0.02 | 0.005 | 1 | 0.009 |
Fourth | 4.41 (0.235) | 0.053 | 0.520 | 0.130 | 0.880 | 0.295 |
First Run | Second Run | Third Run | Fourth Run | Fifth Run | Average | |
---|---|---|---|---|---|---|
IRI (m/km) | 3.9 | 4.08 | 3.92 | 3.93 | 3.77 | 3.92 |
Error in measuring the IRI between each run and the mean (%) | 0.51 | 4.08 | 0.00 | 0.26 | 3.83 | 1.73 |
Cross-correlation between each run and the basis run (%) | 92.5 | 94.2 | - | 94.2 | 90.5 | 92.85 |
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Mahmoudzadeh, A.; Golroo, A.; Jahanshahi, M.R.; Firoozi Yeganeh, S. Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor. Sensors 2019, 19, 1655. https://doi.org/10.3390/s19071655
Mahmoudzadeh A, Golroo A, Jahanshahi MR, Firoozi Yeganeh S. Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor. Sensors. 2019; 19(7):1655. https://doi.org/10.3390/s19071655
Chicago/Turabian StyleMahmoudzadeh, Ahmadreza, Amir Golroo, Mohammad R. Jahanshahi, and Sayna Firoozi Yeganeh. 2019. "Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor" Sensors 19, no. 7: 1655. https://doi.org/10.3390/s19071655
APA StyleMahmoudzadeh, A., Golroo, A., Jahanshahi, M. R., & Firoozi Yeganeh, S. (2019). Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor. Sensors, 19(7), 1655. https://doi.org/10.3390/s19071655