Abstract
Road maintenance and the early detection of road defects rely on routine pavement inspections. While advanced 3D laser profiling systems have the capability to automatically identify certain types of distress such as cracks and ruts, more complex pavement damage, including patches, often require manual identification. To address this limitation, this study proposes an automated patch detection system that employs object detection techniques. The results demonstrate the ability of object detection models to accurately identify patches in laser profiling images, indicating that the proposed approach has the capability to significantly enhance automation in visual inspection processes. This has the potential for significant cost reduction in inspections, improved safety conditions during checks, and acceleration of the current manual inspection processes.
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References
Koch, C., Brilakis, I.: Pothole detection in asphalt pavement images. Adv. Eng. Inform. 25(3), 507–515 (2011). https://doi.org/10.1016/j.aei.2011.01.002
Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., Fieguth, P.: A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv. Eng. Inform. 29(2), 196–210 (2015)
Radopoulou, S.C., Brilakis, I.: Patch detection for pavement assessment. Autom. Constr. 53, 95–104 (2015)
Schnebele, E., Tanyu, B.F., Cervone, G., Waters, N.: Review of remote sensing methodologies for pavement management and assessment. Eur. Transp. Res. Rev. 7(2), 1–19 (2015)
Sholevar, N., Golroo, A., Esfahani, S.R.: Machine learning techniques for pavement condition evaluation. Autom. Constr. 136, 104190 (2022)
Gupta, S., Sharma, P., Sharma, D., Gupta, V., Sambyal, N.: Detection and localization of potholes in thermal images using deep neural networks. Multimed. Tools Appl. 79(35), 26265–26284 (2020)
Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q., Wang, S.: Deepcrack: learning hierarchical convolutional features for crack detection. IEEE Trans. Image Process. 28(3), 1498–1512 (2018)
Laurent, J., Fox-Ivey, R., Petitclerc, B.: High resolution multi-lane road surface mapping using 3D laser profilers for 3D paving and milling projects. In: Proceedings of the 7th Eurasphalt and Eurobitume Congress. Brussels, Belgium: European Asphalt Pavement Association (2020)
Luo, W., Wang, K.C.P., Li, L., Li, Q.J., Moravec, M.: Surface drainage evaluation for rigid pavements using an inertial measurement unit and 1-mm three-dimensional texture data. Transp. Res. Rec. 2457(1), 121–128 (2014)
Laurent, J., Hébert, J.F., Lefebvre, D., Savard, Y.: Using 3D laser profiling sensors for the automated measurement of road surface conditions. In: 7th RILEM International Conference on Cracking in Pavements: Mechanisms, Modeling, Testing, Detection and Prevention Case Histories, vol. 4, pp. 157–167. Springer, Netherlands (2012). https://doi.org/10.1007/978-94-007-4566-7_16
Luo, W., Qin, Y., Zhang, D., Li, L.: Measurement of pavement rutting trajectories on two-lane highway using the 3D line scanning laser system. Int. J. Pavement Eng. 1–16 (2022)
Mathavan, S., Rahman, M.M., Stonecliffe-Janes, M., Kamal, K.: Pavement raveling detection and measurement from synchronized intensity and range images. Transp. Res. Rec. 2457, 3–11. National Research Council (2014). https://doi.org/10.3141/2457-01
Qureshi, W.S., et al.: An exploration of recent intelligent image analysis techniques for visual pavement surface condition assessment. Sensors 22(22), 9019 (2022)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13
Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big data 3(1), 1–40 (2016)
Alzraiee, H., Leal Ruiz, A., Sprotte, R.: Detecting of pavement marking defects using faster R-CNN. J. Perform. Constr. Facil., 35(4), 4021035 (2021)
Hassan, S.I., O’Sullivan, D., Mckeever, S.: Pothole detection under diverse conditions using object detection models. IMPROVE 1, 128–136 (2021)
Wang, J., Xu, G., Yan, F., Wang, J., Wang, Z.: Defect transformer: an efficient hybrid transformer architecture for surface defect detection. Measurement 211, 112614 (2023)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
Hassan, S.I., O’Sullivan, D., McKeever, S., Power, D., McGowan, R., Feighan, K.: Detecting patches on road pavement images acquired with 3D laser sensors using object detection and deep learning. In: VISIGRAPP (5: VISAPP), pp. 413–420 (2022)
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This work was funded by Science Foundation Ireland through the SFI Centre for Research Training in Machine Learning (18/CRT/6183).
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Syed, I.H., McKeever, S., Feighan, K., Power, D., O’Sullivan, D. (2023). A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images. In: Christensen, H.I., Corke, P., Detry, R., Weibel, JB., Vincze, M. (eds) Computer Vision Systems. ICVS 2023. Lecture Notes in Computer Science, vol 14253. Springer, Cham. https://doi.org/10.1007/978-3-031-44137-0_18
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