Skip to main content
SpringerLink
Log in

Data quality assessment of automated pavement cracking measurements in Mississippi

  • Published:
International Journal of Pavement Research and Technology Aims and scope Submit manuscript

Abstract

The reliability and cost-effectiveness of pavement maintenance and rehabilitation (M&R) decisions are highly dependent on the quality of pavement performance data which are regularly collected and stored in the Pavement Management System (PMS) of a state highway agency. Frequently, pavement performance evaluation relies on the precise and accurate measurements of the surface conditions of pavements. This study aims to statistically check the accuracy and precision of using a state of practice automated technology in place of the manual or semi-automated (semi-manual) rating method to collect pavement cracking data. In the study, the classification evaluation indicators were first employed to quantitively evaluate the two kinds of errors which may occur during the automated crack detection process. Second, the systemic errors of the automated method were quantitively evaluated by a t-test to check the accuracy of the automated method, and the variation pattern of the systemic errors was verified with the Pearson correlation test. Finally, the random errors of the automated method were quantitatively evaluated with the Levene’s test to check the precision of the automated method. The study results indicated that the state of practice automated survey method is still not yet ready to provide reliable pavement cracking measurement data. It seemed the automated method in the study could not accurately detect pavement cracking features from the background and an under-estimation tendency of cracking measurement was observed. The automated data also showed varying measurement performance when surveying different types of pavement cracking. While the collected transverse cracking data were relatively precise, obvious measurement errors were present in longitudinal cracking data. In addition, the systemic errors in the automated data were highly correlated with the surveyed pavement distress condition. A greater systemic error was frequently associated with a more deteriorated pavement condition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. S. Saliminejad, N. G. Gharaibeh, Impact of Error in Pavement Condition Data on the Output of Network-Level Pavement Management Systems, Transport. Res. Rec. 2366 (1) (2013) 110–119.

    Article  Google Scholar 

  2. X. Jia, B. Huang, Q. Dong, D. Zhu, J. Maxwell, Influence of Pavement Condition Data Variability on Network-Level Maintenance Decision, Transport. Res. Rec. 2589 (1) (2016) 20–31.

    Article  Google Scholar 

  3. L. Y. Lee, Z. Hossain, X. D. Hu, Quality Assurance of Performance Data for Pavement Management Systems. Design, Analysis, and Asphalt Material Characterization for Road and Airfield Pavements, American Society of Civil Engineers, Reston, VA, USA, 2014, pp. 163–169.

    Google Scholar 

  4. R. G. Lins, S. N. Givigi. Automatic Crack Detection and Measurement Based on Image Analysis, IEEE T. Instrum. Meas. 65 (3) (2016) 583–590.

    Article  Google Scholar 

  5. F. C. Pereira, C. E. Pereira, Embedded Image Processing Systems for Automatic Recognition of Cracks using UAVs, Ifac-PapersOnline 48 (10) (2015) 16–21.

    Article  Google Scholar 

  6. M. Salman et al. Pavement Crack Detection Using the Gabor Filter. 16th International IEEE Conference on Intelligent Transportation Systems, Institute of Electrical and Electronics Engineers, Hague, Netherlands, 2013, pp. 2039–2044.

    Google Scholar 

  7. K. H. McGhee, Automated Pavement Distress Collection Techniques: A Synthesis of Highway Practice, Transportation Research Board, Washington DC, USA, 2004.

    Google Scholar 

  8. M. Gavilán et al., Adaptive Road Crack Detection System by Pavement Classification, Sensors-Basel 11 (10) (2011) 9628–9657.

    Article  Google Scholar 

  9. N. Attoh-Okine, O. Adarkwa. Pavement Condition Surveys-Overview of Current Practices. Report Number DCT 245. Delaware Center for Transportation, Newark, DE, USA, 2013.

    Google Scholar 

  10. C. Haas, C. Hendrickson, Integration of Diverse Technologies for Pavement Sensing, Transport. Res. Rec., 1311 (1) (1991) 92–102.

    Google Scholar 

  11. K. C. P. Wang, W. G. Gong, T. Tracy, V. Nguyen, Automated Survey of Pavement Distress Based on 2D and 3D Laser Images. Report Number MBTC DOT 3023. Fayetteville: Mack-Blackwell National Rural Transportation Study Center, Washington DC, USA, 2011.

    Google Scholar 

  12. S. Chambon, J. M. Moliard, Automatic Road Pavement Assessment with Image Processing: Review and Comparison, Int. J. Geophys. 2011 (1) (2011) 1–20.

    Google Scholar 

  13. H. Zakeri, F. M. Nejad, A. Fahimifar, Image Based Tchniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review. Arch. Comput. Method. E. 24 (4) (2017) 935–977.

    Article  Google Scholar 

  14. J. Jiang, H. Liu, H. Ye, F. Feng, Crack Enhancement Algorithm Based on Improved EM, J. Inform. Comput. Sci. 12 (3) (2015) 1037–1043.

    Article  Google Scholar 

  15. C. Jiang, Y. J. Tsai, Enhanced Crack Segmentation Algorithm using 3D Pavement Data, J. Comput. Civil. Eng. 30 (3) (2015) 04015050.

    Article  Google Scholar 

  16. Y.C. Tsai, V. Kaul, R. M. Mersereau, Critical Assessment of Pavement Distress Segmentation Methods, J. Transp. Eng., 136 (1) (2009) 11–19.

    Article  Google Scholar 

  17. Y. Huang, Y. Tsai, Dynamic Programming and Connected Component Analysis for An Enhanced Pavement Distress Segmentation Algorithm, Transport. Res. Rec. 2225 (1) (2011) 89–98.

    Article  Google Scholar 

  18. T. S. Nguyen, M. Avila, S. Begot, Automatic Detection and Classification of Defect on Road Pavement using Anisotropy Measure, 17th European Signal Processing Conference, Institute of Electrical and Electronics Engineers, Glasgow, UK., 2009, pp. 617–621.

    Google Scholar 

  19. H. Oliveira, P. L. Correia, Automatic Road Crack Detection and Characterization, IEEE T. Intell. Transp. 14 (1) (2013) 155–168.

    Article  Google Scholar 

  20. N.D. Hoang, Q.L. Nguyen, Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance, Math. Probl. Eng. (2018) 1–16.

  21. S. Li, Y. Cao, H. Cai, Automatic Pavement-Crack Detection and Segmentation Based on Steerable Matched Filtering and An Active Contour Model, J. Comput. Civil. Eng. 31 (5) (2017) 04017045.

    Article  Google Scholar 

  22. Y.J. Tsai, C. Jiang, Z. Wang, Pavement Crack Detection Using High-Resolution 3D Line Laser Imaging Technology, In 7th RILEM International Conference on Cracking in Pavements, Springer, Dordrecht, 4 (2012) (2012) 169–178.

  23. G. Sollazzo, K.C.P. Wang, G. Bosurgi, J.Q. Li, Hybrid Procedure for Automated Detection of Cracking with 3D Pavement Data, J. Comput. Civ. Eng. 30(6) (2016) 04016032.

    Article  Google Scholar 

  24. B. Li, K.C.P. Wang, A. Zhang, Y. Fei, Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images, J. Adv. Transport. 2019 (1) (2019) 1–9.

    Google Scholar 

  25. A. Zhang, K. C. Wang, C. Ai, 3D shadow modeling for detection of descended patterns on 3D pavement surface, J. Comput. Civil. Eng. 31 (4) (2017) 04017019.

    Article  Google Scholar 

  26. A. Zhang et al., Deep Learning-Based Fully Automated Pavement Crack Detection on 3D Asphalt Surfaces with An Improved Cracknet, J. Comput. Civil. Eng. 32 (5) (2018) 04018041.

    Article  Google Scholar 

  27. B. Li, K.C.P. Wang, A. Zhang, E. Yang, G. Wang, Automatic Classification of Pavement Crack Using Deep Convolutional Neural Network, Inter. J. Pavement. Eng. 21 (4) (2020) 457–463.

    Article  Google Scholar 

  28. L. Song, X. Wang, Faster Region Convolutional Neural Network for Automated Pavement Distress Detection, Road Mater. Pavement Des., (2019) 1–19. https://doi.org/10.1080/14680629.2019.1614969

  29. J. Lekshmipathy, N.M. Samuel, S. Velayudhan, Vibration Vs. Vision: Best Approach for Automated Pavement Distress Detection, Inter. J. Pavement. Res. Technol. 13 (2) (2020) 1–9.

    Google Scholar 

  30. G. D. Cline, M. Y. Shahin, J. A. Burkhalter, 2003. Automated Data Collection for Pavement Condition Index Survey. Pavement Evaluation Conference, Roanoke, VA, USA, 2002

  31. S. L. Tighe, L. Ningyuan, T. J. Kazmierowski. Evaluation of Semiautomated and Automated Pavement Distress Collection for Network-Level Pavement Management, Transport. Res. Rec. 2084 (1) (2008) 11–17.

    Article  Google Scholar 

  32. G. P. Ong, S. Noureldin, K. C. Sinha, Automated Pavement Condition Data Collection Quality Control, Quality Assurance, and Reliability. Report Number FHWA/IN/JTRP-2009/17. Department of Transportation and Purdue University, West Lafayette, IN, USA, 2010.

    Google Scholar 

  33. K. C. P. Wang, Z. Hou, S. Williams, Precision Test of Cracking Surveys with the Automated Distress Analyzer, J. Transp. Eng. 137 (8) (2011) 571–579.

    Article  Google Scholar 

  34. P. A. Serigos et al., Field Evaluation of Automated Distress Measuring Equipment. Report Number 0-6663-2. Texas: University of Texas, Texas Department of Transportation and Federal Highway Administration, Austin, TX, USA, 2014.

    Google Scholar 

  35. N. Kargah-Ostadi, A. Nazef, J. Daleiden, Y. Zhou, Evaluation Framework for Automated Pavement Distress Identification and Quantification Applications, Transport. Res. Rec. 2639 (1) (2017) 46–54.

    Article  Google Scholar 

  36. G. W. Flintsch, K. K. McGhee, Quality Management of Pavement Condition Data Collection, Transportation Research Board, Washington DC, USA, 2009.

    Book  Google Scholar 

  37. X. Luo, F. Wang, N. Wang, J. Tao, X. Qiu, F. Amini, Rebuilding the Distress Thresholds for Pavement Warranty Program in Mississippi, Transport. Res. Rec. 2673 (2) (2019) 323–334.

    Article  Google Scholar 

  38. J. S. Miller, W. Y. Bellinger, Distress Identification Manual for The Long-Term Pavement Performance Program. Report Number FHWA-RD-03-031. Federal Highway Administration, Washington DC, USA, 2014.

    Google Scholar 

  39. P.A. Serigos, J.A. Prozzi, Evaluation of 3D Automated Systems for the Measurement of Pavement Surface Cracking, J. Transp. Eng., 142 (6) (2016) 05016003.

    Article  Google Scholar 

  40. N. Kargah-Ostadi et al. An Evaluation Framework for Automated Pavement Distress Identification and Quantification Applications, Transport. Res. Rec. 2639 (1) (2016) 46–54.

    Article  Google Scholar 

  41. L. M. Pierce, K. A. Zimmerman, Quality Management for Pavement Condition Data Collection, 9th International Conference on Managing Pavement Assets, Transportation Research Board, Alexandria, Virginia, USA, 2015, pp. 1–12.

    Google Scholar 

  42. K. Pearson, Note on Regression and Inheritance in the Case of Two Parents, Proc. R. Soc., Ser. A 58 (1) (1895) 240–242.

    Google Scholar 

  43. H. Levene, Robust Tests for Equality of Variance, Stanford University Press, CA, USA, 1960.

    MATH  Google Scholar 

Download references

Acknowledgments

The authors are grateful to Mississippi Department of Transportation (MDOT) for funding this study. Specifically, the authors would like to thank Cindy Smith, Marta Charria, Rhea Vincent, Alex Collum, Alan Hatch, James Watkins, and Randy Battey for their support.

Funding

Funding: This work was supported by the [Mississippi Department of Transportation] under Grant [FHWA/MDOT-RD-18-273].

Author information

Authors and Affiliations

  1. Ingram School of Engineering, Texas State University, San Marcos, TX, 78666, USA

    Jueqiang Tao, Xiaohua Luo & Feng Wang

  2. Department of Road and Traffic Engineering, Zhejiang Normal University, Jinhua, Zhejiang, 321000, China

    Xin Qiu

Authors
  1. Jueqiang Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaohua Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Feng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng Wang.

Additional information

Peer review under responsibility of Chinese Society of Pavement Engineering.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tao, J., Luo, X., Qiu, X. et al. Data quality assessment of automated pavement cracking measurements in Mississippi. Int. J. Pavement Res. Technol. 14, 117–128 (2021). https://doi.org/10.1007/s42947-020-0331-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42947-020-0331-6

Keywords

Search

Navigation