# A Novel Camera-Based Measurement System for Roughness Determination of Concrete Surfaces

^{*}

## Abstract

**:**

## 1. Introduction

- Fully digital measurement system and reproducibility of results.
- Contactless and area-based measurement.
- Deployable on construction sites and high mobility.
- Applicability on arbitrary oriented surfaces.
- Easy to use.
- Lightweight.
- Low-cost.

## 2. State of the Art and Related Work

## 3. Theoretical Background

#### 3.1. Defining Roughness

#### 3.1.1. Shape Deviations

#### 3.1.2. Parameters

#### Arithmetical Mean Deviation of the Assessed Profile (${R}_{\mathrm{a}}$)

#### Mean Texture Depth ($MTD$)

#### 3.2. Digital Photogrammetry

#### 3.2.1. Camera Model

#### 3.2.2. Epipolar Geometry

## 4. Measurement System

#### 4.1. Concept for Image Capture

#### 4.2. Apparatus

#### 4.3. Custom-Built 3D Calibration Test-Field

## 5. Methodology

#### 5.1. 3D Reconstruction Pipeline

#### 5.1.1. Preprocessing

#### 5.1.2. Structure from Motion

#### 5.1.3. Dense Image Matching

- A
- Cost Initialization:

- B
- Cost Aggregation:

- C
- Disparity Selection:

#### Disparity Map Fusion and Point Cloud Generation

#### 5.2. Adapting Roughness Parameter to 3D Point Clouds

#### Arithmetical Mean Deviation ${R}_{a}$

## 6. Experiments

#### 6.1. Camera Calibration

#### 6.1.1. Self-Calibration

#### 6.1.2. Calibration of the Test-Field

#### 6.1.3. Calibration of the Industrial Camera

#### 6.2. System Assessment

#### 6.2.1. Test Objects

#### 6.2.2. Measurement Procedure

## 7. Results and Discussion

#### 7.1. GPU Acceleration of SGM

#### 7.2. Comparison of the Results of Our Measurement System with the Sand Patch Method

#### 7.3. Area- vs. Line-Based Estimation of the Roughness

## 8. Conclusions

#### 8.1. Summary

#### 8.2. Outlook

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Julio, E.N.B.S.; Branco, F.A.B.; Silva, V.D. Concrete-to-concrete bond strength. Influence of the roughness of the substrate surface. Constr. Build. Mater.
**2004**, 18, 675–681. [Google Scholar] [CrossRef] [Green Version] - Santos, P.M.D.; Julio, E.N.B.S.; Silva, V.D. Correlation between concrete-to-concrete bond strength and the roughness of the substrate surface. Constr. Build. Mater.
**2007**, 21, 1688–1695. [Google Scholar] [CrossRef] [Green Version] - Santos, D.S.; Santos, P.M.D.; Dias-da-Costa, D. Effect of surface preparation and bonding agent on the concrete-to-concrete interface strength. Constr. Build. Mater.
**2012**, 37, 102–110. [Google Scholar] [CrossRef] - Santos, P.M.D.; Julio, E.N.B.S. A state-of-the-art review on roughness quantification methods for concrete surfaces. Constr. Build. Mater.
**2013**, 38, 912–923. [Google Scholar] [CrossRef] - Bikerman, J.J. The Science of Adhesive Joints; Academic Press: Cambridge, MA, USA, 1968. [Google Scholar]
- Kaufmann, N. Das Sandflächenverfahren. Straßenbautechnik
**1971**, 24, 131–135. [Google Scholar] - Mellmann, G.; Oppat, K. Maß für Maß. Rautiefen-Bestimmung von Betonoberflächen mittels Laserverfahren. Bautenschutz Bausanier B B
**2008**, 31, 30–32. [Google Scholar] - Steinhoff, A.; Holthausen, R.S.; Raupach, M.; Schulz, R.-R. Entwicklung eines Pastenverfahrens zur Bestimmung der Rautiefe an vertikalen Betonoberflächen. Entwicklungsschwerpunkte und Ergebnisse einer Studie. Beton
**2020**, 70, 182–186. [Google Scholar] - China, S.; James, D.E. Comparison of Laser-Based and Sand Patch Measurements of Pavement Surface Macrotexture. J. Transp. Eng.
**2012**, 138, 176–181. [Google Scholar] [CrossRef] - ASTM E2157-15. Standard Test Method for Measuring Pavement Macrotexture Properties Using the Circular Track Meter; ASTM: West Conshohocken, PA, USA, 2019. [Google Scholar]
- Ma, L.F.; Li, Y.; Li, J.; Wang, C.; Wang, R.S.; Chapman, M.A. Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review. Remote Sens.
**2018**, 10, 1531. [Google Scholar] [CrossRef] [Green Version] - Gonzalez-Jorge, H.; Rodriguez-Gonzalvez, P.; Shen, Y.Q.; Laguela, S.; Diaz-Vilarino, L.; Lindenbergh, R.; Gonzalez-Aguilera, D.; Arias, P. Metrological intercomparison of six terrestrial laser scanning systems. IET Sci. Meas. Technol.
**2018**, 12, 218–222. [Google Scholar] [CrossRef] [Green Version] - Stal, C.; Verbeurgt, J.; De Sloover, L.; De Wulf, A. Assessment of handheld mobile terrestrial laser scanning for estimating tree parameters. J. For. Res.
**2020**. [Google Scholar] [CrossRef] - DIN EN ISO 13473-1:2017-08. Characterization of Pavement Texture by Use of Surface Profiles—Part 1: Determination of Mean Profile Depth; ISO: Geneva, Switzerland, 2017. [Google Scholar]
- Schulz, R.-R.; Schmidt, T.; Hardt, R.; Riedl, R. Baustellengerechte Laser-Profilmessverfahren für die Steuerung und Eigenüberwachung der Oberflächentexturierung von Verkehrsflächen aus Beton. Straße Autob.
**2013**, 64, 911–920. [Google Scholar] - Schulz, R.-R. Fortschritte bei der Rauheitsbewertung von Betonoberflächen. Alternativen zum Sandflächenverfahren. Beton
**2016**, 66, 502–510. [Google Scholar] - Schulz, R.-R. Laser schlägt Sand—Rautiefenmessung an Betonoberflächen. Bau. Im Bestand B B
**2017**, 40, 44–48. [Google Scholar] - Schulz, R.-R. Roughness and anti-slip properties of concrete surfaces—Electro-optical measuring systems to determine roughness parameters. Bft Int.
**2008**, 74, 4–15. [Google Scholar] - Werner, S.; Neumann, I.; Thienel, K.C.; Heunecke, O. A fractal-based approach for the determination of concrete surfaces using laser scanning techniques: A comparison of two different measuring systems. Mater. Struct.
**2013**, 46, 245–254. [Google Scholar] [CrossRef] - PHIDIAS. The Complete Solution for Photogrammetric Close Range Applications. Available online: http://www.phocad.com/en/en.html (accessed on 19 October 2020).
- Benning, W.; Lange, J.; Schwermann, R.; Effkemann, C.; Görtz, S. Monitoring crack origin and evolution at concrete elements using photogrammetry. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
**2004**, 35, 678–683. [Google Scholar] - Benning, W.; Lange, J.; Schwermann, R.; Effkemann, C.; Gortz, S. Photogrammetric measurement system for two-dimensional deformation and crack analysis of concrete constructions. Sens. Meas. Syst.
**2004**, 1829, 813–817. [Google Scholar] - Calonder, M.; Lepetit, V.; Strecha, C.; Fua, P. BRIEF: Binary Robust Independent Elementary Features. In Proceedings of the Computer Vision ECCV, Heraklion, Greece, 5–11 September 2010; Volume 6314, pp. 778–792. [Google Scholar] [CrossRef] [Green Version]
- Rosten, E.; Porter, R.; Drummond, T. Faster and Better: A Machine Learning Approach to Corner Detection. IEEE Trans. Pattern Anal. Mach. Intell.
**2010**, 32, 105–119. [Google Scholar] [CrossRef] [Green Version] - Lowe, D.G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis.
**2004**, 60, 91–110. [Google Scholar] [CrossRef] - Bay, H.; Ess, A.; Tuytelaars, T.; Van Gool, L. Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst.
**2008**, 110, 346–359. [Google Scholar] [CrossRef] - Wieneke, K.; Herbrand, M.; Vogler, N.; Schwermann, R.; Blankenbach, J. Measurement methods for determining the roughness of concrete surfaces. Bauingenieur
**2018**, 93, 365–373. [Google Scholar] - Grigoriadis, K. Use of laser interferometry for measuring concrete substrate roughness in patch repairs. Autom. Constr.
**2016**, 64, 27–35. [Google Scholar] [CrossRef] [Green Version] - Lange, D.A.; Jennings, H.M.; Shah, S.P. Analysis of Surface-Roughness Using Confocal Microscopy. J. Mater. Sci.
**1993**, 28, 3879–3884. [Google Scholar] [CrossRef] - Sadowski, L. Methodology of the assessment of the interlayer bond in concrete composites using NDT methods. J. Adhes. Sci. Technol.
**2018**, 32, 139–157. [Google Scholar] [CrossRef] - Özcan, B.; Schwermann, R.; Blankenbach, J. Kamerabasiertes Messsystem zur Bestimmung der Rauigkeit von Bauteiloberflächen—Kalibrierung und erste Ergebnisse. In Proceedings of the 19. Internationaler Ingenieurvermessungskurs, München, Germany, 3–6 March 2020. [Google Scholar]
- DIN 4760:1982-06. Form Deviations; Concepts; Classification System; Beuth: Berlin, Germany, 1982. [Google Scholar]
- DIN EN ISO 4287:2010-07. Geometrical Product Specifications (GPS)—Surface Texture: Profile Method—Terms, Definitions and Surface Texture Parameters; ISO: Geneva, Switzerland, 2010. [Google Scholar]
- Luhmann, T.; Robson, S.; Kyle, S.; Boehm, J. Close-Range Photogrammetry and 3d Imaging, 3rd ed.; Walter de Gruyter GmbH: Berlin, Germany, 2020. [Google Scholar]
- Hartley, R.; Zisserman, A. Multiple View Geometry in Computer Vision, 2nd ed.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2003. [Google Scholar]
- Agisoft Metashape. Available online: https://www.agisoft.com/features/professional-edition/ (accessed on 19 October 2020).
- Panchal, P.M.; Panchal, S.R.; Shah, S.K. A Comparison of SIFT and SURF. Int. J. Innov. Res. Comput. Commun. Eng.
**2013**, 1, 323–327. [Google Scholar] - Hartley, R.I. In defense of the eight-point algorithm. IEEE Trans. Pattern Anal. Mach. Intell.
**1997**, 19, 580–593. [Google Scholar] [CrossRef] [Green Version] - Hartley, R.I.; Sturm, P. Triangulation. Comput. Vis. Image Underst.
**1997**, 68, 146–157. [Google Scholar] [CrossRef] - Blut, C.; Blankenbach, J. Three-dimensional CityGML building models in mobile augmented reality: A smartphone-based pose tracking system. Int. J. Digit. Earth
**2020**. [Google Scholar] [CrossRef] [Green Version] - Fischler, M.A.; Bolles, R.C. Random Sample Consensus—A Paradigm for Model-Fitting with Applications to Image-Analysis and Automated Cartography. Commun. ACM
**1981**, 24, 381–395. [Google Scholar] [CrossRef] - Levenberg, K. A method for the solution of certain non-linear problems in least squares. Q. Appl. Math.
**1944**, 2, 164–168. [Google Scholar] [CrossRef] [Green Version] - Marquardt, D.W. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. J. Soc. Ind. Appl. Math.
**1963**, 11, 431–441. [Google Scholar] [CrossRef] - Sun, J.; Zheng, N.N.; Shum, H.Y. Stereo matching using belief propagation. IEEE Trans. Pattern Anal. Mach. Intell.
**2003**, 25, 787–800. [Google Scholar] [CrossRef] - Kolmogorov, V.; Zabih, R. Computing visual correspondence with occlusions using graph cuts. In Proceedings of the Eighth Ieee International Conference on Computer Vision, Vol Ii, Proceedings, Vancouver, BC, Canada, 7–14 July 2001; pp. 508–515. [Google Scholar]
- Hirschmuller, H. Accurate and efficient stereo processing by semi-global matching and mutual information. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, 20–25 June 2005; Volume 2, pp. 807–814. [Google Scholar] [CrossRef]
- CUDA Zone|NVIDIA Developer. Available online: https://developer.nvidia.com/cuda-zone (accessed on 19 October 2020).
- Zhao, F.; Huang, Q.M.; Gao, W. Image matching by normalized cross-correlation. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse, France, 14–19 May 2006; Volume 1–13, pp. 1977–1980. [Google Scholar]
- Zabih, R.; Woodfill, J. Non-Parametric Local Transforms for Computing Visual Correspondence; Springer: Berlin/Heidelberg, Germany, 1994; pp. 151–158. [Google Scholar]
- Spangenberg, R.; Langner, T.; Rojas, R. Weighted Semi-Global Matching and Center-Symmetric Census Transform for Robust Driver Assistance; Springer: Berlin/Heidelberg, Germany, 2013; pp. 34–41. [Google Scholar]
- Tian, Q.; Huhns, M.N. Algorithms for Subpixel Registration. Comput. Vis. Graph. Image Process.
**1986**, 35, 220–233. [Google Scholar] [CrossRef] - RenderScript. Available online: https://developer.android.com/guide/topics/renderscript/compute (accessed on 19 October 2020).

**Figure 1.**Profile line of a technical surface which is composed of multiple orders of shape deviations.

**Figure 4.**Epipolar geometry—initial image planes (gray) and stereo rectified images planes (orange).

**Figure 5.**Concept of the recording geometry. 1—camera position; 2—camera trajectory; 3—field of view pyramid; 4—imaging area; 5—concrete surface.

**Figure 6.**Camera-based measurement system. 1—Industrial camera; 2—concrete specimen; 3—one of the four LED strips; 4—both moving axes; 5—power switch; 6—rotary switch for illumination adjustment; 7—rechargeable battery.

**Figure 12.**Reconstructed point cloud with conspicuous curvature after self-calibration (

**top**,

**left**), extracted profile line (

**bottom**, 10-fold scaled in height), and histogram of the height values (

**right**).

**Figure 14.**Point cloud (

**top**,

**left**), extracted profile line (

**bottom**, 10-fold scaled in height), and histogram of the height values (

**right**) after reconstruction with the new parameter set.

**Figure 16.**3D point cloud reconstruction of a particular concrete specimen: (

**a**) Reconstructed camera poses including the sparse point cloud after SfM procedure, (

**b**) generated dense point cloud after Dense Image Matching (DIM), and (

**c**) zoom-in of the dense point cloud.

**Figure 17.**Comparison of the total runtime between the pure CPU implementation and the Graphics Processing Unit (GPU)-accelerated implementation.

**Figure 19.**Comparison between ${R}_{a}$ estimated by our measurement system and the references values for $MTD$ determined by the sand patch method.

**Figure 20.**Correlations between ${R}_{a}$ estimated by our measurement system and the reference values for $MTD$ determined by the sand patch method.

Shape Deviations | ||
---|---|---|

1. Order Form deviation | Curvature, Unevenness | |

2. Order Waviness | Waves | |

3. Order Roughness | Grooves | |

4. Order Roughness | Ridges, Scales, Crests | |

5. Order Roughness | Microstructure of the material | not easily presentable in image form |

6. Order | Lattice structure of the material | not easily presentable in image form |

Specification | Value |
---|---|

Resolution (H × V) | 3840 pixel × 2748 pixel |

Pixel size (H × V) | 1.67 µm × 1.67 µm |

Bit depth | 12 bits |

Signal-to-noise ratio | 32.9 dB |

Mono/Colour | Mono |

Shutter technology | Rolling shutter |

$\mathit{f}$ | $\mathit{c}\mathit{x}$ | $\mathit{c}\mathit{y}$ | $\mathit{k}\mathbf{1}$ | $\mathit{k}\mathbf{2}$ | $\mathit{k}\mathbf{3}$ | $\mathit{p}\mathbf{1}$ | $\mathit{p}\mathbf{2}$ | |
---|---|---|---|---|---|---|---|---|

$\mathit{f}$ | 1.00 | −0.06 | 0.15 | −1.00 | 0.99 | −0.97 | 0.22 | 0.13 |

$\mathit{c}\mathit{x}$ | 1.00 | 0.05 | 0.06 | −0.06 | 0.06 | 0.04 | 0.01 | |

$\mathit{c}\mathit{y}$ | 1.00 | −0.15 | 0.14 | −0.13 | 0.04 | −0.02 | ||

$\mathit{k}\mathbf{1}$ | 1.00 | −0.99 | 0.97 | −0.22 | −0.13 | |||

$\mathit{k}\mathbf{2}$ | 1.00 | −0.99 | 0.22 | 0.13 | ||||

$\mathit{k}\mathbf{3}$ | 1.00 | −0.21 | −0.13 | |||||

$\mathit{p}\mathbf{1}$ | 1.00 | 0.03 | ||||||

$\mathit{p}\mathbf{2}$ | 1.00 |

**Table 4.**Parameters of the interior orientation after calibration using the 3D calibration test-field.

Parameter | Value | Std. Dev. | |
---|---|---|---|

$f$ | 8.2545 mm | 0.0007 mm | |

$cx$ | 0.0737 mm | 0.0012 mm | |

$cy$ | 0.0051 mm | 0.0007 mm | |

$k1$ | $\left(\cdot {10}^{-4}\right)$ | −43.8231 | 0.2449 |

$k2$ | $\left(\cdot {10}^{-7}\right)$ | 565.8091 | 36.7678 |

$k3$ | $\left(\cdot {10}^{-10}\right)$ | −10,555.4693 | 1665.3598 |

$p1$ | $\left(\cdot {10}^{-5}\right)$ | 5.1708 | 0.2624 |

$p2$ | $\left(\cdot {10}^{-5}\right)$ | 10.2455 | 0.2549 |

**Table 5.**Correlation coefficients of the parameters of interior orientation after calibration using the 3D calibration test-field.

$\mathit{f}$ | $\mathit{c}\mathit{x}$ | $\mathit{c}\mathit{y}$ | $\mathit{k}\mathbf{1}$ | $\mathit{k}\mathbf{2}$ | $\mathit{k}\mathbf{3}$ | $\mathit{p}\mathbf{1}$ | $\mathit{p}\mathbf{2}$ | |
---|---|---|---|---|---|---|---|---|

$\mathit{f}$ | 1.00 | 0.00 | 0.01 | 0.03 | −0.01 | 0.00 | 0.00 | 0.00 |

$\mathit{c}\mathit{x}$ | 1.00 | 0.00 | −0.01 | 0.01 | −0.01 | 0.72 | 0.00 | |

$\mathit{c}\mathit{y}$ | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.38 | ||

$\mathit{k}\mathbf{1}$ | 1.00 | −0.98 | 0.93 | 0.01 | 0.00 | |||

$\mathit{k}\mathbf{2}$ | 1.00 | −0.99 | −0.01 | 0.00 | ||||

$\mathit{k}\mathbf{3}$ | 1.00 | 0.01 | 0.00 | |||||

$\mathit{p}\mathbf{1}$ | 1.00 | 0.00 | ||||||

$\mathit{p}\mathbf{2}$ | 1.00 |

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**MDPI and ACS Style**

Özcan, B.; Schwermann, R.; Blankenbach, J.
A Novel Camera-Based Measurement System for Roughness Determination of Concrete Surfaces. *Materials* **2021**, *14*, 158.
https://doi.org/10.3390/ma14010158

**AMA Style**

Özcan B, Schwermann R, Blankenbach J.
A Novel Camera-Based Measurement System for Roughness Determination of Concrete Surfaces. *Materials*. 2021; 14(1):158.
https://doi.org/10.3390/ma14010158

**Chicago/Turabian Style**

Özcan, Barış, Raimund Schwermann, and Jörg Blankenbach.
2021. "A Novel Camera-Based Measurement System for Roughness Determination of Concrete Surfaces" *Materials* 14, no. 1: 158.
https://doi.org/10.3390/ma14010158