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Road rut detection system with embedded multi-channel laser sensor

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Abstract

Laser sensing technology is used to detect the pavement. In the pavement cross-section rut detection, multi-channel laser sensors are used to collect the three-dimensional information of the uneven cross-section direction of the road surface at the same time. In the pavement structure depth detection, two laser sensors are used to collect the three-dimensional information of the road surface structure depth at the same time. Linear regression and quadratic regression algorithm are used to reduce the pavement cross-section rut and structure depth detection. On this basis, the embedded multi-channel laser pavement rutting detection system and laser pavement structure depth detection system are researched and developed; many field tests and application tests have been carried out. Through comparison and verification, the system has strong reliability, small error, and fast detection speed, which is suitable for road detection of different structures.

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References

  1. Renong LIANG (2021) Research on Construction and Optimization of Evaluation Index System for Transportation Development Level. Comprehensive Transportation 43(03):32–37

    Google Scholar 

  2. Jie TANG (2020) Research on cost control of municipal road maintenance. Accounting study 12:7–9

    Google Scholar 

  3. Al-Qerem A, Alauthman M, Almomani A, Gupta BB (2020) IoT transaction processing through cooperative concurrency control on fog–cloud computing environment. Soft Comput 24(8):5695–5711

    Article  Google Scholar 

  4. BRAD H (2021) In-furrow peanut fertilizers are still not recommended. Southwest Farm Press

  5. Zhijian LAN (2021) Application of Preventive Highway Maintenance Technology in Modern Highway Maintenance. Low Carbon World 11(02):197–198

    Google Scholar 

  6. DB32/T944–2006 (2007) Highway maintenance quality inspection and evaluation method

  7. JTG_F80/1–2004 (2017) Standard for quality inspection and evaluation of Highway Engineering

  8. Renfei C (2014) Asphalt pavement flatness detection technology and improvement measures. Innov Appl Sci Technol 33:235

    Google Scholar 

  9. S M, A M, I E, et al (2013) Development of high power laser technology: 915 nm mini-bars for fibre laser pumping and red laser bars for cinema/projector applications

  10. Li G, Zhang C (2019) Automatic detection technology of sports athletes based on image recognition technology. EURASIP Journal on Image and Video Processing 2019(1)

  11. CHANG Chengli (2011) Research on Traceability and Transfer Technology of International Flatness Quantity Value. Beijing Polytechnic University

  12. Kamel A, Miky Y, Shouny AEFTF (2020) A quick surveying approach for constructing high resolution digital surface model for road elements. Geomat Nat Haz Risk 11(1):1466–1489

    Article  Google Scholar 

  13. C Lei, Dawei QI (2005) Application of Computerized Tomography (CT) in Non-destructive Testing for Logs. Forest Engineering (03): 21–23

  14. Al-Smadi M, Qawasmeh O, Al-Ayyoub M, Jararweh Y, Gupta B (2018) Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. J Comput Sci 27:386–393

    Article  Google Scholar 

  15. Yamni M, Karmouni H, Sayyouri M et al (2021) Novel Octonion Moments for color stereo image analysis. Digital Signal Processing 108(3)

  16. Pascual D, Clarizia MP et al (2021) Spaceborne Demonstration of GNSS-R Scattering Cross Section Sensitivity to Wind Direction. IEEE Geoscience and Remote Sensing Letters PP(99):1–5

  17. Koch C, Brilakis I (2011) Pothole detection in asphalt pavement images. Adv Eng Inform 25(3):507–515

    Article  Google Scholar 

  18. Rahman SU, Cao Q et al (2020). Multifunctional polarization converting metasurface and its application to reduce the radar cross-section of an isolated MIMO antenna. Journal of Physics D Applied Physics 53(30)

  19. Sharbaf M, Ghafoori N (2021) Laboratory evaluation of geogrid-reinforced flexible pavements. Transportation Engineering (4):100070

  20. Ramkumar S, Rajeev Kumar M et al (2021) Patient health care intensive care system using wearable band sensor network. Materials Today: Proceedings (47):80–87

  21. Salhab N, Langar R et al (2021) 5G network slices resource orchestration using Machine Learning techniques. Computer Networks 188:107829

    Article  Google Scholar 

  22. Le W (2018) Development of laser triangular displacement sensor for measuring wheelset parameters. Beijing Jiaotong University

  23. Ruiz-Llata M, Martín-Mateos P et al (2014) Remote optical sensor for real-time residual salt monitoring on road surfaces. Sens Actuators, B Chem 191:371–376

    Article  Google Scholar 

  24. Dettenborn T, Hartikainen A et al (2020). Pavement Maintenance Threshold Detection and Network-Level Rutting Prediction Model Based on Finnish Road Data. Journal of Infrastructure Systems 26(2):04020016

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Funding

This work was supported by the Scientific Research Foundation of Shaanxi University of Technology.

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Contributions

Jin Han contributed to system design, algorithm, manuscript writing, editing and other aspects.

Likun Cui contributed to the experiment and data collection.

Shaoning Shi contributed to the review and editing.

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Correspondence to Jin Han.

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The authors declare no competing interests.

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This article is part of the Topical Collection: New Intelligent Manufacturing Technologies through the Integration of Industry 4.0 and Advanced Manufacturing

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Han, J., Cui, L. & Shi, S. Road rut detection system with embedded multi-channel laser sensor. Int J Adv Manuf Technol 122, 41–50 (2022). https://doi.org/10.1007/s00170-021-08027-w

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  • DOI: https://doi.org/10.1007/s00170-021-08027-w

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