Automated pothole condition assessment in pavement using photogrammetry-assisted convolutional neural network

References

• “Agisoft Metashape Professional edition: Agisoft LLC software”, 2021. Retrieved from : http://www.agisoft.com/downloads/installer/.
   Google Scholar
• Ali, R., et al., 2022. Structural crack detection using deep convolutional neural networks. Automation in Construction, 133, Elsevier B.V. doi: 10.1016/j.autcon.2021.103989.
   Web of Science ®Google Scholar
• Alqaydi, S., et al. 2021. Using smart phones to assessment road roughness in the UAE. In Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management, Singapore, pp. 7-11.
   Google Scholar
• Arjapure, S., and Kalbande, D. R., 2021. Deep learning model for pothole detection and area computation, In 2021 International Conference on Communication information and Computing Technology (ICCICT), pp. 1–6.
   Google Scholar
• Arya, D., et al., 2021. Rdd2020: An image dataset for smartphone-based road damage detection and classification, Mendeley Data, 2021. https://data.mendeley.com/datasets/5ty2wb6gvg/1 (accessed Dec. 02, 2022).
   Google Scholar
• Banharnsakun, A., Apr. 2017. Hybrid ABC-ANN for pavement surface distress detection and classification. International Journal of Machine Learning and Cybernetics, 8 (2), 699–710. doi:10.1007/s13042-015-0471-1
   Web of Science ®Google Scholar
• Barbosh, M., Dunphy, K., and Sadhu, A., 2022. Acoustic emission-based damage localization using wavelet-assisted deep learning. Journal of Infrastructure Preservation and Resilience, 3 (1), 6. doi:10.1186/s43065-022-00051-8.
   Google Scholar
• Cao, W., Liu, Q., and He, Z., 2020. Review of pavement defect detection methods. IEEE Access, 8, 14531–14544. doi: 10.1109/aCCESS.2020.2966881.
   Web of Science ®Google Scholar
• Chen, H., Yao, M., and Gu, Q., 2020. Pothole detection using location-aware convolutional neural networks. International Journal of Machine Learning and Cybernetics, 11 (4), 899–911. doi:10.1007/s13042-020-01078-7
   Web of Science ®Google Scholar
• de Blasiis, M. R., di Benedetto, A., and Fiani, M., 2020. Mobile laser scanning data for the evaluation of pavement surface distress. Remote Sensing, 12 (6). doi:10.3390/rs12060942
   Web of Science ®Google Scholar
• Deeksha, Arya, et al., 2020. Global road damage detection: state-of-the-art solutions. In: Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020.
   Google Scholar
• Deeksha, Arya, et al., 2021. Deep learning-based road damage detection and classification for multiple countries. Automation in Construction, 132.
   Web of Science ®Google Scholar
• Dejin, Zhang, et al., 2018. Automatic pavement defect detection using 3D laser profiling technology. Automation in Construction, 96, 350–365. doi:10.1016/j.autcon.2018.09.019
   Web of Science ®Google Scholar
• Dib, J., Sirlantzis, K., and Howells, G., 2020. A review on negative road anomaly detection methods. IEEE Access, 8, 57298–57316. doi:10.1109/ACCESS.2020.2982220
   Web of Science ®Google Scholar
• Du, Z., et al., 2021. Application of image technology on pavement distress detection: A review. Measurement (Lond), 184.
   PubMed Web of Science ®Google Scholar
• Everingham, M., van Gool, L., and Williams, C. K. I., 2010. The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88, 303–338. doi:10.1007/s11263-009-0275-4
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Foley, J. D., Fischler, M. A., and Bolles, R. C., 1981. Graphics and image processing random sample consensus: a paradigm for model fitting with Apphcatlons to image analysis and automated cartography.
   Google Scholar
• Gao, M., et al., 2020. Detection and segmentation of cement concrete pavement pothole based on image processing technology. Mathematical Problems in Engineering, 2020.
   Web of Science ®Google Scholar
• Gerard, Salton, and Michael, McGill, 1983. Introduction to modern information retrieval. New York: McGraw-Hill.
   Google Scholar
• Ha, J., Park, K., and Kim, M., 2021. A development of road crack detection system using deep learning-based segmentation and object detection. The Journal of Society for e-Business Studies, 26 (1), 93–106.
   Google Scholar
• He, K., et al., 2015. Deep residual learning for image recognition, [Online]. Available: http://arxiv.org/abs/1512.03385.
   Google Scholar
• Hegde, V., et al., 2020. Yet another deep learning approach for road damage detection using ensemble learning. In: Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, pp. 5553–5558. doi:10.1109/BigData50022.2020.9377833.
   Google Scholar
• Huidrom, L., Das, L. K., and Sud, S. K., 2013. Method for automated assessment of potholes, cracks and patches from road surface video clips. Procedia - Social and Behavioral Sciences, 104, 312–321. doi:10.1016/j.sbspro.2013.11.124
   Google Scholar
• Kanta Sunni, Kushwaha Prasad, 2018. 3D documentation and condition assessment of surface and subsurface of engineering structures LiDAR Data acquisition and processing view project forest above ground biomass assesment using LiDAR and SAR View project.
   Google Scholar
• Khurram, Kamal, et al., 2018. Performance assessment of Kinect as a sensor for pothole imaging and metrology. International Journal of Pavement Engineering, 19 (7), 565–576. doi:10.1080/10298436.2016.1187730
   Web of Science ®Google Scholar
• Kim, M. K., Wang, Q., and Li, H., 2019. Non-contact sensing based geometric quality assessment of buildings and civil structures: A review. Automation in Construction, 100, Elsevier B.V., 163–179. doi:10.1016/j.autcon.2019.01.002
   Web of Science ®Google Scholar
• Kingma, D. P., and Ba, J., 2014. Adam: a method for stochastic optimization, [Online]. Available: http://arxiv.org/abs/1412.6980.
   Google Scholar
• Koch, C., and Brilakis, I., 2011. Pothole detection in asphalt pavement images. Advanced Engineering Informatics, 25 (3), 507–515. doi:10.1016/j.aei.2011.01.002
   Web of Science ®Google Scholar
• Lekshmipathy, J., and Velayudhan, S. Pavement condition assessment using smartphone accelerometers pavement condition assessment view project urban pavement maintenance management system view project pavement condition assessment using smartphone accelerometers. doi:10.13140/RG.2.2.13820.46723
   Google Scholar
• Lili, Pei, et al., 2022. Detecting potholes in asphalt pavement under small-sample conditions based on improved faster region-based convolution neural networks. Canadian Journal of Civil Engineering, 49 (2), 265–273. doi:10.1139/cjce-2020-0764
   Web of Science ®Google Scholar
• Lin, T.-Y., et al., 2018. Focal loss for dense object detection.
   Google Scholar
• Maeda, H., et al., 2018. Road damage detection and classification using deep neural networks with smartphone images. Computer-Aided Civil and Infrastructure Engineering, 33 (12), 1127–1141. doi:10.1111/mice.12387
   Web of Science ®Google Scholar
• Maeda, H., et al., 2021. Generative adversarial network for road damage detection. Computer-Aided Civil and Infrastructure Engineering, 36 (1), 47–60. doi:10.1111/mice.12561
   Web of Science ®Google Scholar
• Mei, Q., and Gül, M., 2020. A cost effective solution for pavement crack inspection using cameras and deep neural networks. Construction and Building Materials, 256.
   Web of Science ®Google Scholar
• Miller, J. S., and Bellinger, W. Y., 2014. Distress identification manual for the long-term pavement performance Program.
   Google Scholar
• Nguyen, S. D., Tran, T. S., and Tran, V. P., Apr. 2022. Deep learning-based crack detection: A survey. International Journal of Pavement Research and Technology.
   Google Scholar
• Ochoa-Ruiz, G., et al., 2020. An asphalt damage dataset and detection system based on retinanet for road conditions assessment. Applied Sciences, 10 (11. doi:10.3390/app10113974
   Google Scholar
• Pan, Y., et al., 2018. Detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11 (10), 3701–3712. doi:10.1109/JSTARS.2018.2865528
   Web of Science ®Google Scholar
• Ranyal, E., Sadhu, A., and Jain, K., 2022. Road condition monitoring using smart sensing and artificial intelligence: A Review.
   Google Scholar
• Ravi, R., Bullock, D., and Habib, A., 2020a. Highway and airport runway pavement inspection using mobile lidar. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, XLIII-B1-2020 (B1), 349–354.
   Google Scholar
• Ravi, R., Habib, A., and Bullock, D., 2020b. Pothole mapping and patching quantity estimates using lidar-based mobile mapping systems. Transportation Research Record: Journal of the Transportation Research Board, 2674 (9), 124–134. doi:10.1177/0361198120927006
   Web of Science ®Google Scholar
• Ryu, S. K., Kim, T., and Kim, Y. R., 2015. Image-Based pothole detection system for ITS service and road management system. Mathematical Problems in Engineering, 2015.
   Web of Science ®Google Scholar
• Sandamal, R. M. K., and Pasindu, H. R., 2022. Applicability of smartphone-based roughness data for rural road pavement condition evaluation. International Journal of Pavement Engineering, 23 (3), 663–672. doi:10.1080/10298436.2020.1765243
   Web of Science ®Google Scholar
• Sony, S., et al., 2021. A systematic review of convolutional neural network-based structural condition assessment techniques. Engineering Structures, 226, Elsevier Ltd. doi:10.1016/j.engstruct.2020.111347
   Web of Science ®Google Scholar
• Soviany, P., and Ionescu, R. T., 2018. Optimizing the trade-off between single-stage and Two-stage object detectors using image difficulty prediction, [Online]. Available: http://arxiv.org/abs/1803.08707.
   Google Scholar
• Tedeschi, A., and Benedetto, F., Apr. 2017. A real-time automatic pavement crack and pothole recognition system for mobile Android-based devices. Advanced Engineering Informatics, 32, 11–25. doi:10.1016/j.aei.2016.12.004
   Web of Science ®Google Scholar
• Tzutalin, 2015. Labelimg graphical annotation tool, Git Code. https://github.com/tzutalin/labelImg.
   Google Scholar
• van der Horst, B. B., Lindenbergh, R. C., and Puister, S. W. J., 2019. Mobile laser scan data for road surface damage detection. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42 (2/W13), 1141–1148. doi:10.5194/isprs-archives-XLII-2-W13-1141-2019
   Google Scholar
• Varona, B., Monteserin, A., and Teyseyre, A., 2020. A deep learning approach to automatic road surface monitoring and pothole detection. Personal and Ubiquitous Computing, 24 (4), 519–534. doi:10.1007/s00779-019-01234-z
   Web of Science ®Google Scholar
• Wenbo, Hu, et al., 2021. Machine vision-based surface crack analysis for transportation infrastructure. Automation in Construction, 132.
   Web of Science ®Google Scholar
• Yik, Y. K., et al., 2021. A real-time pothole detection based on deep learning approach. Journal of Physics: Conference Series, 1828 (1).
   Google Scholar