551
Views
10
CrossRef citations to date
0
Altmetric
Research Article

An automatic pothole detection algorithm using pavement 3D data

ORCID Icon, , ORCID Icon & ORCID Icon
Article: 2057978 | Received 21 Dec 2021, Accepted 21 Mar 2022, Published online: 04 Apr 2022

References

  • MATLAB [Computer software]. MathWorks, Natick, MA.
  • Baek, J. W., and Chung, K., 2020. Pothole classification model using edge detection in road image. Applied Sciences, 10, 6662. doi:10.3390/app10196662.
  • Bosurgi, G., et al., 2019. Automatic crack detection results using a novel device for survey and analysis of road pavement condition. In: M. Pasetto, M. Partl, and G. Tebaldi, eds. International symposium on asphalt pavement & environment. Lecture Notes in Civil Engineering, vol. 48 Cham: Springer, 431–440.
  • Bosurgi, G., et al., 2021. A web platform for the management of road survey and maintenance information: A preliminary step towards smart road management systems. Structural Control & Health Monitoring, 29 (3). doi:10.1002/stc.2905.
  • Bosurgi, G., Pellegrino, O., and Sollazzo, G., 2021. Pavement condition information modelling in an I-BIM environment. International Journal of Pavement Engineering, 1–16. doi:10.1080/10298436.2021.1978442.
  • Daniel, A., and Preeja, V., 2014. A Novel technique for automatic road distress detection and analysis. International Journal of Computer Applications (0975 - 8887), 101 (10), 18–23.
  • Dhiman, A., and Klette, R., 2020. Pothole detection using computer vision and learning. IEEE Transactions on Intelligent Transportation Systems, 21 (8). doi:10.1109/TITS.2019.2931297.
  • Erikson, J., Girod, L., and Hull, B., 2008. The pothole patrol: using a mobile sensor network for road surface monitoring. Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services, 29-39.
  • Fan, R., et al., 2020. Pothole detection based on disparity transformation and road surface modeling. IEEE Transactions on Image Processing, 29, 897–908. doi:10.1109/TIP.2019.2933750.
  • Gao, M., et al., 2020. Detection and segmentation of cement concrete pavement pothole based on image processing technology. hindawai. Mathematical Problems in Engineering. doi:10.1155/2020/1360832.
  • Guan, J., et al., 2021. Automated pixel-level pavement distress detection based on stereo vision and deep learning. Automation in Construction, 129. doi:10.1016/j.autcon.2021.103788.
  • Haq, M.U.U., et al., 2019. Stereo-based 3D reconstruction of potholes by a hybrid, dense matching scheme. IEEE Sensors Journal, 19 (10), 3807–3817. doi:10.1109/JSEN.2019.2898375.
  • Ibragimov, E., et al., 2020. Automated pavement distress detection using region based convolutional neural networks. International Journal of Pavement Engineering. doi:10.1080/10298436.2020.1833204.
  • Inzerillo, L., Di Mino, G., and Roberts, R., 2018. Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress. Automation in Construction, 96, 457–469.
  • Jang, D.W., and Park, R.H., 2016. Pothole detection using spatio-temporal saliency. IET Intelligent Transport System, 10 (9), 605–612. doi:10.1049/iet-its.2016.0006.
  • Kim, T., and Ryu, S.-K., 2014. Review and analysis of pothole detection methods. Journal of Emerging Trends in Computing and Information Sciences, 5 (8), 603–608.
  • Koch, C., and Brilakis, I., 2011. Pothole detection in asphalt pavement images. Advanced Engineering Informatics, 25 (3), 507–515.
  • Kyriakou, C., Christodoulou, S. E., and Dimitriou, L., 2019. Smartphone-based pothole detection utilizing artificial neural networks. Journal of Infrastructure System. doi:10.1061/(ASCE)IS.1943-555X.0000489.
  • Li, Q., et al., 2009. A real-time 3D scanning system for pavement distortion inspection. Measurement Science and Technology, 21 (1), 15702–15709.
  • Li, Q., et al., 2017. 3D laser imaging and sparse points grouping for pavement crack detection. 25th European Signal Processing Conference (EUSIPCO).
  • Lin, J., and Liu, Y., 2010. Potholes detection based on SVM in the pavement distress image. Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, 544–547. doi:10.1109/DCABES.2010.115.
  • Maeda, H., et al., 2018. Road damage detection using deep neural networks with images captured through a smartphone. Computer Aided Civil and Infrastructure Engineering, 33 (12), 1127–1141. doi:10.1111/mice.12387.
  • Mednis, A., et al., 2001. Real time pothole detection using Android smartphones with accelerometers. Proceedings of the International Conference on Distributed Computing in Sensor Systems and Workshops, 1-6. doi:10.1109/DCOSS.2011.5982206.
  • Ouma, Y.O., and Hahn, M., 2017. Pothole detection on asphalt pavements from 2D-colour pothole images using fuzzy c-means clustering and morphological reconstruction. Automation in Construction, 83, 196–211.
  • Radopoulou, S. C., and Brilakis, I., 2016. Improving road asset condition monitoring. Transportation Research Procedia, 14, 3004–3012.
  • Salem, S. A., Kalyankar, N. V., and Khamitkar, S.D., 2010. Image segmentation by using threshold techniques. Journal of Computing, 2 (5), 83–86.
  • Sollazzo, G., et al., 2016. Hybrid procedure for automated detection of cracking with 3D pavement data. Journal of Computing in Civil Engineering, 30 (6). doi:10.1061/(ASCE)CP.1943-5487.0000597.
  • Tsai, Y.C.J., and Chatterjee, A., 2017. Pothole detection and classification using 3D technology and watershed method. Journal of Computing in Civil Engineering, 32 (2). doi:10.1061/(ASCE)CP.1943-5487.0000726.
  • Tsai, Y.C.J.l., and Li, F., 2012. Critical assessment of detecting asphalt pavement cracks under different lighting and low intensity contrast conditions using emerging 3D laser technology. Journal of Transportation Engineering, 138 (5), 649–656.
  • Wang, K.C.P., 2011. Elements of automated survey of pavements and a 3D methodology. Journal of Modern Transportation, 19 (1), 51–57.
  • Wang, K.C.P., and Smadi, O., 2011. Automated imaging technologies for pavement distress survey. Pavement Monitoring and Evaluation Committee, Transportation Research Circular E-C156.
  • Wen, T., et al., 2021. Automated pavement distress segmentation on asphalt surfaces using a deep learning network. International Journal of Pavement Engineering. doi:10.1080/10298436.2022.2027414.
  • Xu, C., and Prince, J. L., 1998. Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing, 7 (3), 359–369.
  • Yan, F., Zhang, H., and Kube, C. R., 2005. A multistage adaptive thresholding method. Pattern Recognition Letters, 26, 1183–1191.
  • Yu, X, and Salari, E., 2011. Pavement pothole detection and severity measurement using laser imaging. 2011 IEEE international conference on electro/information technology, 2011, 1-5. doi: 10.1109/EIT.2011.5978573.
  • Zhang, D., et al., 2018. Automatic pavement defect detection using 3D laser profiling technology. Automation in Construction, 96, 350–365.
  • Zhang, A., Wang, K.C.P., and Ai, C., 2017. 3D shadow modeling for detection of descended patterns on 3D pavement surface. Journal of Computing in Civil Engineering, 31 (4), 04017019. doi:10.1061/(ASCE)CP.1943-5487.0000661.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.