An image-based system for asphalt pavement bleeding inspection

References

• Addison, P. S, 2017. The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. London: The Institute of Physics.
   Google Scholar
• Albelwi, S. and Mahmood, A, 2017. A framework for designing the architectures of deep convolutional neural networks. Entropy, 19 (6), 242.
   Web of Science ®Google Scholar
• Alhasan, A., White, D. J., and De Brabanter, K., 2016. Wavelet filter design for pavement roughness analysis. Computer-Aided Civil and Infrastructure Engineering, 31 (12), 907–920.
   Web of Science ®Google Scholar
• Anderson, K., 1999. Pavement surface condition field rating manual for asphalt pavements. Washington State: Department of Transportation.
   Google Scholar
• Andrew, Williams, et al., 2006. Pavement condition rating system. Ohio State: Department of Transportation: Office of Pavement Engineering.
   Google Scholar
• Anon, 2017. Pavement assessment report. Illinois: Gewalt Hamilton Associates.
   Google Scholar
• Arbabpour Bidgoli, M., et al., 2019. Road roughness measurement using a cost-effective sensor-based monitoring system. Automation in Construction, 104, 140–152.
   Web of Science ®Google Scholar
• ASTM, D., 2018. D6433-18. Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys.
   Google Scholar
• Atto, A. M., Berthoumieu, Y., and Bolon, P, 2013. 2-d wavelet packet spectrum for texture analysis. IEEE Transactions on Image Processing, 22 (6), 2495–2500.
   PubMed Web of Science ®Google Scholar
• Attoh-Okine, N. and Adarkwa, O, 2013. Pavement condition surveys–overview of current practices., Project Rep, Delaware center for transportation. Newark, DE, USA: University of Delaware.
   Google Scholar
• Bhandare, A., et al., 2016. Applications of convolutional neural networks. International Journal of Computer Science Information Technologies, 7 (5), 2206–2215.
   Google Scholar
• Brimley, B. and Carlson, P, 2012. Using high friction surface treatments to improve safety at horizontal curves., Project Rep., Texas Transportation Institute, The Texas A&M University System.
   Google Scholar
• Chan, S., et al., 2016. Transition from Manual to Automated Pavement Distress Data Collection and Performance Modelling in the Pavement Management System. ed. TAC 2016: Efficient Transportation-Managing the Demand-2016 Conference and Exhibition of the Transportation Association of Canada.
   Google Scholar
• Ciresan, D. C., et al., 2011. Flexible, high performance convolutional neural networks for image classification. ed. IJCAI Proceedings-International Joint Conference on Artificial Intelligence, 1237.
   Google Scholar
• Coenen, T. B. J. and Golroo, A, 2017. A review on automated pavement distress detection methods. Cogent Engineering, 4 (1), p.1374822, 1–23.
   Web of Science ®Google Scholar
• Dorafshan, S., Thomas, R. J., and Maguire, M, 2018. Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Construction and Building Materials, 186, 1031–1045.
   Web of Science ®Google Scholar
• Du, Y., et al., 2020. A novel approach for pavement texture characterisation using 2D-wavelet decomposition. International Journal of Pavement Engineering, DOI: 10.1080/10298436.2020.1825712.
   Google Scholar
• Du, Y., et al., 2020. Pavement distress detection and classification based on YOLO network. International Journal of Pavement Engineering, DOI: 10.1080/10298436.2020.1714047.
   Google Scholar
• du Tertre, A., et al., 2020. Ultrasonic inspection of asphalt pavements to assess longitudinal joints. Road Materials Pavement Design, DOI: 10.1080/14680629.2020.1820895.
   Google Scholar
• Fauzi, A. A., Utaminingrum, F., and Ramdani, F, 2020. Road surface classification based on LBP and GLCM features using kNN classifier. Bulletin of Electrical Engineering Informatics, 9 (4), 1446–1453.
   Google Scholar
• Feldman, D. R., Pyle, T., and Lee, J., 2015. Automated Pavement Condition Survey Manual. California Department of Transportation.
   Google Scholar
• Gonzalez, R. and Woods, R., 2017. Digital image processing (4 ed)., New York: Pearson.
   Google Scholar
• Gopalakrishnan, K., et al., 2017. Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction and Building Materials, 157, 322–330.
   Web of Science ®Google Scholar
• Hadjidemetriou, G. M. and Christodoulou, S. E, 2019. Vision- and entropy-based detection of distressed areas for integrated pavement condition assessment. Journal of Computing in Civil Engineering, 33 (3), 04019020.
   Web of Science ®Google Scholar
• Hadjidemetriou, G. M., Vela, P. A., and Christodoulou, S. E, 2018. Automated Pavement patch detection and Quantification using support vector machines. Journal of Computing in Civil Engineering, 32 (1), 04017073.
   Web of Science ®Google Scholar
• Han, C., et al., 2020. Intelligent decision model of road maintenance based on improved weight random forest algorithm. International Journal of Pavement Engineering, DOI: 10.1080/10298436.2020.1784418.
   Google Scholar
• He, K., et al., 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. ed. Proceedings of the IEEE international conference on computer vision, 1026–1034.
   Google Scholar
• He, K., et al., 2016. Deep residual learning for image recognition. ed. Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778.
   Google Scholar
• Henderson, R., et al., 2011. The influence of binder rise in reducing tyre–road friction. New Zealand : Transport Agency.
   Google Scholar
• Hoang, N.-D, 2019. Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regression. Automation in Construction, 105, 102843.
   Web of Science ®Google Scholar
• Hung, C.-C., Song, E. and Lan, Y., 2019. Texture features and image texture models. In: C.-C Hung, E Song, and Y Lan, eds. Image texture analysis. Switzerland: Springer, 15–50.
   Google Scholar
• Iandola, F. N., et al., 2016. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:.07360.
   Google Scholar
• Ji, A., et al., 2020. An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement. Automation in Construction, 114, 103176.
   Web of Science ®Google Scholar
• Kamal, K., et al., 2018. Performance assessment of Kinect as a sensor for pothole imaging and metrology. International Journal of Pavement Engineering, 19 (7), 565–576.
   Web of Science ®Google Scholar
• Kantardzic, M, 2011. Data mining: concepts, models, methods, and algorithms. New Jersey: John Wiley & Sons.
   Google Scholar
• Kara De Maeijer, P., et al., 2019. Fiber optics sensors in asphalt pavement: state-of-the-art review. Infrastructures, 4 (2), 36.
   Google Scholar
• Karaşahin, M., Saltan, M., and Çetin, S, 2014. Determination of seal coat deterioration using image processing methods. Construction and Building Materials, 53 (Supplement C), 273–283.
   Google Scholar
• Khan, M., Qiao, F., and Yu, L, 2017. Wavelet analysis to characterize the dependency of vehicular emissions on road roughness. Transportation Research Record: Journal of the Transportation Research Board, 2641 (1), 111–125.
   Google Scholar
• Kheirati, A. and Golroo, A, 2020. Low-cost infrared-based pavement roughness data acquisition for low volume roads. Automation in Construction, 119, 103363.
   Web of Science ®Google Scholar
• Khosravi, H., et al., 2013. An analytical–empirical investigation of the bleeding mechanism of asphalt mixes. Construction and Building Materials, 45 (Supplement C), 138–144.
   Google Scholar
• Krizhevsky, A., Sutskever, I., and Hinton, G. E, 2012. Imagenet classification with deep convolutional neural networks. ed. Advances in neural information processing systems, 1097–1105.
   Google Scholar
• Lawson, W. D., 2006. Maintenance solutions for bleeding and flushed pavements.Texas : Department of Transportation.
   Google Scholar
• LeCun, Y., Bengio, Y., and Hinton, G, 2015. Deep learning. Nature, 521, 436–444.
   PubMed Web of Science ®Google Scholar
• Li, B., et al., 2018. Automatic classification of pavement crack using deep convolutional neural network. International Journal of Pavement Engineering, 21 (4), 457–463.
   Web of Science ®Google Scholar
• Luo, P., et al., 2018. CT Image Denoising Using Double Density Dual Tree Complex Wavelet with Modified Thresholding. ed. 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA), 287–290.
   Google Scholar
• Luo, W., Liu, L., and Li, L, 2020. Measuring rutting dimension and lateral position using 3D line scanning laser and inertial measuring unit. Automation in Construction, 111, 103056.
   Web of Science ®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.
   Web of Science ®Google Scholar
• Mataei, B., et al., 2018. Evaluation of pavement surface drainage using an automated image acquisition and processing system. Automation in Construction, 86, 240–255.
   Web of Science ®Google Scholar
• Mataei, B., Zakeri, H., and Nejad, F. M, 2019. An overview of multiresolution analysis for nondestructive evaluation of pavement surface drainage. Archives of Computational Methods in Engineering, 26 (1), 143–161.
   Web of Science ®Google Scholar
• Miao, Y., et al., 2015. Characterizing asphalt pavement 3-D macrotexture using features of co-occurrence matrix. International Journal of Pavement Research Technology, 8 (4), 243.
   Google Scholar
• Miller, J. S. and Bellinger, W. Y, 2014. Distress identification manual for the long-term pavement performance program. United States: Federal Highway Administration. Office of Infrastructure Research and Development.
   Google Scholar
• Moghadas Nejad, F. and Zakeri, H., 2011. An expert system based on wavelet transform and radon neural network for pavement distress classification. Expert Systems with Applications, 38 (6), 7088–7101.
   Web of Science ®Google Scholar
• Mohanaiah, P., Sathyanarayana, P., and GuruKumar, L, 2013. Image texture feature extraction using GLCM approach. International Journal of Scientific and Research Publications, 3 (5), 1.
   Google Scholar
• Nejad, F. M., Karimi, N., and Zakeri, H, 2016. Automatic image acquisition with knowledge-based approach for multi-directional determination of skid resistance of pavements. Automation in Construction, 71 (Part 2), 414–429.
   Google Scholar
• Nejad, F. M. and Zakeri, H., 2013. The hybrid method and its Application to smart pavement management. In: Yang, X.-S., et al. eds. Metaheuristics in water, geotechnical and Transport engineering. Oxford: Elsevier, 439–484.
   Google Scholar
• Nielsen, M. A., 2015. Neural networks and deep learning. San Francisco, CA: Determination press.
   Google Scholar
• Ouma, Y. O. and Hahn, M, 2016. Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular radon transform. Advanced Engineering Informatics, 30 (3), 481–499.
   Web of Science ®Google Scholar
• 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 (Supplement C), 196–211.
   Google Scholar
• Pan, S. J. and Yang, Q, 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22 (10), 1345–1359.
   Web of Science ®Google Scholar
• Pathak, B. and Barooah, D, 2013. Texture analysis based on the gray-level co-occurrence matrix considering possible orientations. International Journal of Advanced Research in Electrical, Electronics Instrumentation Engineering, 2 (9), 4206–4212.
   Google Scholar
• Prasad, P. and Umamadhuri, G., 2018. Biorthogonal wavelet-based image compression. In: S Dash, P Naidu, R Bayindir and S Das, ed. Artificial Intelligence and evolutionary computations in Engineering systems. Singapore: Springer, 391–404.
   Google Scholar
• Ranjbar, S., Nejad, F. M., and Zakeri, H, 2021. An image-based system for pavement crack evaluation using transfer learning and wavelet transform. International Journal of Pavement Research and Technology, 14 (4), 437–449.
   Google Scholar
• Rodrigues, R. S., et al., 2019. Pothole Detection in Asphalt: An Automated Approach to Threshold Computation Based on the Haar Wavelet Transform. ed. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), 306–315.
   Google Scholar
• Russakovsky, O., et al., 2015. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115 (3), 211–252.
   Web of Science ®Google Scholar
• Shahin, M. Y., 2006. Pavement management for airports, roads, and parking lots (2nd ed. New York, USA: Springer.
   Google Scholar
• Soille, P., 2013. Morphological image analysis: principles and applications. Verlag Berlin Heidelberg: Springer Science & Business Media.
   Google Scholar
• Song, L. and Wang, X, 2019. Faster region convolutional neural network for automated pavement distress detection. Road Materials and Pavement Design, 22 (1), 23–41.
   Web of Science ®Google Scholar
• Souza, V. M. A, 2018. Asphalt pavement classification using smartphone accelerometer and complexity invariant distance. Engineering Applications of Artificial Intelligence, 74, 198–211.
   Web of Science ®Google Scholar
• Szegedy, C., et al., 2015. Going deeper with convolutions. ed. Proceedings of the IEEE conference on computer vision and pattern recognition, 1–9.
   Google Scholar
• Tong, Z., et al., 2018. Recognition of asphalt pavement crack length using deep convolutional neural networks. Road Materials and Pavement Design, 19 (6), 1334–1349.
   Web of Science ®Google Scholar
• Tong, Z., et al., 2020. Pavement defect detection with fully convolutional network and an uncertainty framework. Computer-Aided Civil and Infrastructure Engineering.
   Google Scholar
• Wang, P., et al., 2017. Asphalt pavement pothole detection and segmentation based on wavelet energy field. Mathematical Modelling of Engineering Problems, 4, 13–17.
   Google Scholar
• Yang, G., et al., 2018. Wavelet based macrotexture analysis for pavement friction prediction. KSCE Journal of Civil Engineering, 22 (1), 117–124.
   Web of Science ®Google Scholar
• Yang, S., et al., 2020. Pavement curling and warping analysis using wavelet techniques. International Journal of Pavement Engineering, DOI: 10.1080/10298436.2020.1726346.
   Google Scholar
• Ye, W., et al., 2019. Convolutional neural network for pothole detection in asphalt pavement. Road Materials and Pavement Design, 22 (1), 42–58.
   Web of Science ®Google Scholar
• Zakeri, H., Nejad, F. M., and Fahimifar, A, 2016. Rahbin: A quadcopter unmanned aerial vehicle based on a systematic image processing approach toward an automated asphalt pavement inspection. Automation in Construction, 72 (Part 2), 211–235.
   Google Scholar
• Zakeri, H., Nejad, F. M., and Fahimifar, A, 2017. Image based techniques for crack detection, classification and Quantification in asphalt pavement: A review. Archives of Computational Methods in Engineering, 24 (4), 935–977.
   Web of Science ®Google Scholar
• Zelelew, H., Khasawneh, M., and Abbas, A, 2014. Wavelet-based characterisation of asphalt pavement surface macro-texture. Road Materials and Pavement Design, 15 (3), 622–641.
   Web of Science ®Google Scholar
• Zhan, Y., et al., 2020. Friction-ResNets: deep residual network architecture for pavement skid resistance evaluation. Journal of Transportation Engineering, Part B: Pavements, 146 (3), 04020027.
   Web of Science ®Google Scholar
• Zhang, D., et al., 2018a. Automatic pavement defect detection using 3D laser profiling technology. Automation in Construction, 96, 350–365.
   Web of Science ®Google Scholar
• Zhang, Y., et al., 2018c. A Kinect-Based Approach for 3D Pavement Surface Reconstruction and Cracking Recognition. Transactions on Intelligent Transportation Systems.
   Google Scholar
• Zhang, Z., et al., 2018d. Road profile reconstruction using connected vehicle responses and wavelet analysis. Journal of Terramechanics, 80, 21–30.
   Web of Science ®Google Scholar
• Zhang, A., et al., 2019. Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network. Computer-Aided Civil and Infrastructure Engineering, 34 (3), 213–229.
   Web of Science ®Google Scholar
• Zhang, J., et al., 2020. Automatic detection of moisture damages in asphalt pavements from GPR data with deep CNN and IRS method. Automation in Construction, 113, 103119.
   Web of Science ®Google Scholar
• Zhang, K., Cheng, H., and Zhang, B, 2018b. Unified approach to pavement crack and sealed crack detection using preclassification based on transfer learning. Journal of Computing in Civil Engineering, 32 (2), 04018001.
   Web of Science ®Google Scholar
• Zhou, J., Huang, P. S., and Chiang, F.-P, 2006. Wavelet-based pavement distress detection and evaluation. Optical Engineering, 45 (2), 027007.
   Web of Science ®Google Scholar