Skip to main content

Advertisement

Springer Nature Link
Log in

Classification of pavement crack types based on square bounding box diagonal matching method

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the increase in traffic volume, it brings new problems to people’s live, that is, the task of inspection and maintenance of road pavements has become increasingly arduous. Therefore, the detection and identification of the road surface has become particularly urgent. This paper proposes a crack classification method based on diagonal matching of square bounding boxes. This method is used to match the geometric features of different types of cracks and gives an evaluation criterion for the regional similarity metrics that can be used to construct the classification of crack types. It provides quantitative criteria for refining the intrinsic relationship of complex types of crack areas. The experimental results show that the classification function of the crack type of the pavement is better. The results show that the accuracy of the identification and classification of block and crack cracks is obviously better than the traditional classification methods such as random forest and support vector machine.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Sy NT, Avila M, Begot S, Bardet JC (2008) Detection of the defects in road surface by a vision system. In: The 14th IEEE mediterranean electrotechnical conference, pp 847–851

  2. Amhaz R, Chambon S, Idier J, Baltazart V (2016) Automatic crack detection on two-dimensional pavement images: an algorithm based on minimal path selection. IEEE Trans Intell Transp Syst 17(10):2718–2729

    Article  Google Scholar 

  3. Tang J, Gu Y (2013) Automatic crack detection and segmentation using a hybrid algorithm for road distress analysis. In: 2013 IEEE international conference on systems, man, and cybernetics, Manchester, pp 3026–3030

  4. Chambon S (2011) Detection of points of interest for geodesic contours: application on road images for crack detection. In: VISAAP 2011-proceedings of the sixth international conference on computer vision theory and application, pp 210–213

  5. Zhang J (2004) Study on pavement crack identification and evaluation technology based on digital image processing. Chang’an University, Xi’an

    Google Scholar 

  6. Cheng HD et al (1999) Novel Approach to pavement cracking detection based on fuzzy set theory. J Comput Civ Eng 13(4):270–280

    Article  Google Scholar 

  7. Wang C, Sha A, Sun Z (2010) Pavement crack classification based on chain code. In: 2010 Seventh international conference on fuzzy systems and knowledge discovery, pp 593–597

  8. Cha YJ et al (2017) Deep learning based crack damage detection using convolutional neural networks. Comput Aided Civ Infrastruct Eng 32:361–378

    Article  Google Scholar 

  9. Zhang A et al (2017) Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Comput Aided Civ Infrastruct Eng 32:805–819

    Article  Google Scholar 

  10. Zhang A et al (2019) Pixel-level cracking detection on 3D asphalt pavement images through deep-learning-based CrackNet-V. IEEE Trans Intell Transp Syst 21(1):273–284

    Google Scholar 

  11. Zhang A et al (2019) Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network. Comput Aided Civ Infrastruct Eng 34:213–229

    Article  Google Scholar 

  12. Ai D, Jiang G, Kei LS, Li C (2018) Automatic pixel-level pavement crack detection using information of multi-scale neighborhoods. IEEE Access 6:24452–24463

    Article  Google Scholar 

  13. Zhang L, Yang F, Zhang YD, Zhu YJ (2016) Road crack detection using deep convolutional neural network. In: 2016 IEEE international conference on image processing (ICIP), Phoenix, AZ, pp 3708–3712

  14. Fan Z, Wu Y, Lu J et al (2018) Automatic pavement crack detection based on structured prediction with the convolutional neural network. arXiv:1802.02208

  15. Li L, Sun L, Ning G, Tan S (2014) Automatic pavement crack recognition based on Bp neural network. PROMET-Traffic Transp 26(1):11–22

    Article  Google Scholar 

  16. Shi Y, Cui L, Qi Z, Meng F, Chen Z (2016) Automatic road crack detection using random structured forests. IEEE Trans Intell Transp Syst 17(12):3434–3445

    Article  Google Scholar 

  17. Wang X, Wang M, Li C (2009) Research on collision detection algorithm based on AABB. In: ICNC 2009-proceedings of the fifth international conference on natural computation, pp 422–424

  18. Varadharajan S, Jose S, Sharma K, Wander L, Mertz C (2014) Vision for road inspection. In: IEEE winter conference on applications of computer vision, Steamboat Springs, CO, pp 115–122

  19. Zakeri H, Nejad FM, Fahimifar A (2017) Image based techniques for crack detection, classification and quantification in asphalt pavement: a review. Arch Comput Methods Eng 24(4):935–977

    Article  Google Scholar 

  20. Na W, Tao W (2012) Proximal support vector machine based pavement image classification. In: International conference on advanced computational intelligence, pp 686–688

  21. Fan Y, Chu Y (2015) Study of a new vehicle detection algorithm based on linear CCD images. Optik Int J Light Electron Opt 126(24):5932–5935

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Tongji University, Shanghai, China

    Guofeng Qin & Linyu Huang

  2. College of Transportation Engineering, Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, China

    Shuo Yang

Authors
  1. Guofeng Qin
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Linyu Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Shuo Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence to Guofeng Qin or Shuo Yang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qin, G., Huang, L. & Yang, S. Classification of pavement crack types based on square bounding box diagonal matching method. Neural Comput & Applic 34, 13125–13132 (2022). https://doi.org/10.1007/s00521-020-04929-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04929-0

Keywords