AASHTO. 2018. Standard Practice for Quantifying Cracks in Asphalt Pavement Surfaces from Collected Pavement Images Utilizing Automated Methods. AASHTO R 85-18. AASHTO, Washington, DC.
AASHTO. 2022. Standard Practice for Collecting Images of Pavement Surfaces for Distress Detection. AASHTO R 86-18. AASHTO, Washington, DC.
Aboah, A., and Y. Adu-Gyamfi. 2020. “Smartphone-based Pavement Roughness Estimation using Deep Learning with Entity Embedding.” Advances in Data Science and Adaptive Analysis. Volume 12, No. 03n04. World Scientific.
Ahmad, T., V. Gharehbaghi, J. Li, C. Bennett, and R. Lequesne. 2023. “Crack Segmentation in the Wild Using Convolutional Neural Networks and Bootstrapping.” Earthquake Engineering and Resilience, Volume 2(3), pp. 348–363.
Ahmadi, A., S. Khalesi, and A. Golroo. 2021. “An Integrated Machine Learning Model for Automatic Road Crack Detection and Classification in Urban Areas.” International Journal Pavement Engineering, Volume 23(10), 3536–3552.
Ai, D., G. Jiang, L. Siew Kei, and C. Li. 2018. “Automatic Pixel-Level Pavement Crack Detection Using Information of Multi-Scale Neighborhoods.” IEEE Access, Volume 6, pp. 24452–24463.
Ale, L., N. Zhang, and L. Li. 2018. Road Damage Detection using RetinaNet. 2018 IEEE International Conference on Big Data (Big Data). IEEE. New York, NY.
Anand, S., S. Gupta, V. Darbari, and S. Kohli. 2018. “Crack-Pot: Autonomous Road Crack and Pothole Detection.” Digital Image Computing: Techniques and Applications (DICTA), IEEE, pp. 1-6.
Arya, D., H. Maeda, S.K. Ghosh, D. Toshniwal, and Y. Sekimoto. 2021. “An Annotated Image Dataset for Automatic Road Damage Detection using Deep Learning.” Data in Brief. Volume 36. Elsevier.
ASTM. 2023. Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys. ASTM D6433. ASTM International, West Conshohocken, PA.
Bridgelall, R. 2015. Pavement Performance Evaluation using Connected Vehicles. Doctoral dissertation. North Dakota State University, Fargo, ND.
Bridgelall, R., and D. Tolliver. 2018. “Accuracy Enhancement of Roadway Anomaly Localization using Connected Vehicles.” International Journal of Pavement Engineering, Volume 19, Issue 1. Taylor & Francis Online.
Cao, W., Q. Liu, and Z. He. 2020. “Review of Pavement Defect Detection Methods.” IEEE Access, Volume 8.
Cao, M.T., K.T. Chang, N.M. Nguyen, V.D. Tran, X.L. Tran, and N.D. Hoang. 2021. “Image Processing-Based Automatic Detection of Asphalt Pavement Rutting using a Novel Metaheuristic Optimized Machine Learning Approach.” Soft Computing, Volume 25, pp. 2839–12855.
Cha, Y.J., W. Choi, and O. Büyüköztürk. 2017. “Deep Learning-based Crack Damage Detection using Convolutional Neural Networks.” Computer-Aided Civil and Infrastructure Engineering, Volume 32, Issue 5. Wiley Online.
Chen, T., Z. Cai, X. Zhao, C. Chen, X. Liang, T. Zou, and P. Wang. 2020. “Pavement Crack Detection and Recognition using the Architecture of segNet.” Journal of Industrial Information Integration, Volume 18.
Chitale, P.A., K.Y. Kekre, H.R. Shenai, R. Karani, and J.P. Gala. 2020. “Pothole Detection and Dimension Estimation System using Deep Learning (Yolo) and Image Processing.” Proceedings, 35th International Conference on Image and Vision Computing. IEEE.
Chou, J., W.A. O’Neill, and H.D. Cheng. 1994. “Pavement Distress Classification using Neural Networks.” Proceedings, International Conference on Systems, Man, and Cybernetics, Volume 1. IEEE.
Chun, P.J., T. Yamane, and Y. Tsuzuki. 2021. “Automatic Detection of Cracks in Asphalt Pavement using Deep Learning to Overcome Weaknesses in Images and GIS Visualization.” Volume 11, Issue 3. Applied Sciences.
Commandre, B., D. En-Nejjary, L. Pibre, M. Chaumont, C. Delenne, and N. Chahinian. 2017. “Manhole Cover Localization in Aerial Images with a Deep Learning Approach.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Volume XLII-1/W1, pp. 6–9.
Cord, A., and S. Chambon. 2012. “Automatic Road Defect Detection by Textural Pattern Recognition Based on AdaBoost.” Computer-Aided Civil and Infrastructure Engineering. Volume 27, Issue 4.
Cubero-Fernandez, A., F.J. Rodriguez-Lozano, R. Villatoro, J. Olivares, and J.M. Palomares. 2017. “Efficient Pavement Crack Detection and Classification.” Journal on Image and Video Processing. EURASIP.
Daniel, A., and V. Preeja. 2014. “Automatic Road Distress Detection and Analysis.” International Journal of Computer Applications. Volume 101, Issue 10.
Del Rio-Barral, P., M. Soilan, S.M. González-Collazo, and P. Arias. 2022. “Pavement Crack Detection and Clustering via Region-growing Algorithm from 3D MLS Point Clouds.” Remote Sensing. Volume 14, Issue 22.
Du, Z., J. Yuan, F. Xiao, and C. Hettiarachchi. 2021. “Application of Image Technology on Pavement Distress Detection: A Review.” Measurement. Volume 184. Elsevier.
Eisenbach, M., R. Stricker, D. Seichter, K. Amende, K. Debes, M. Sesselmann, D. Ebersbach, U. Stoeckert, and H.M. Gross. 2017. “How to Get Pavement Distress Detection Ready for Deep Learning? A Systematic Approach.” International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, pp. 2039–2047. IEEE.
Eriksson, J., L. Girod, B. Hull, R. Newton, S. Madden, and H. Balakrishnan. 2008. “The Pothole Patrol: Using a Mobile Sensor Network for Road Surface Monitoring.” Proceedings, 6th International Conference on Mobile Systems, Applications, and Services. Association for Computing Machinery, New York. NY.
Fan, R., M.J. Bocus, Y. Zhu, J. Jiao, L. Wang, F. Ma, S. Cheng, and M. Liu. 2019. “Road Crack Detection using Deep Convolutional Neural Network and Adaptive Thresholding. IEEE Intelligent Vehicles Symposium (IV), IEEE, pp. 474–479.
FHWA. 2015. The Long-Term Pavement Performance Program. FHWA-HRT-15-049. Washington, DC.
FHWA. n.d. InfoTechnology, https://infotechnology.fhwa.dot.gov/inertial-profiler-road-pavement/, accessed October 23, 2023.
Gao, J., C.H. Ho, I. Wiese, D. Zhang, and M. Gerosa. 2021. “Application of Machine Learning Based Technology in Pavement Condition Assessment and Prediction.” Paper No. TRBAM-21-03949. Transportation Research Board, Washington, DC.
Ghasemi, P., M. Aslani, D.K. Rollins, R. Williams, and V.R. Schaefer. 2018. “Modeling Rutting Susceptibility of Asphalt Pavement using Principal Component Pseudo Inputs in Regression and Neural Networks.” International Journal of Pavement Research and Technology. Elsevier.
Guerrieri, M., and G. Parla. 2022. “Flexible and Stone Pavements Distress Detection and Measurement by Deep Learning and Low-cost Detection Devices.” Journal of Engineering Failure Analysis. Volume 141. European Structural Integrity Society.
Hoang, N.D., and Q.L. Nguyen. 2019. “A Novel Method for Asphalt Pavement Crack Classification Based on Image Processing and Machine Learning.” Journal of Engineering with Computers. Volume 35.
Hoang, N.D., Q.L. Nguyen, and D. Tien Bui. 2018. “Image Processing–based Classification of Asphalt Pavement Cracks using Support Vector Machine Optimized by Artificial Bee Colony.” Journal of Computing in Civil Engineering. Volume 32, Issue 5. ASCE.
Hou, Y., Q. Li, C. Zhang, G. Lu, Z. Ye, Y. Chen, L. Wang, and D. Cao. 2021. “The State-of-the-art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis.” Engineering. Volume 7, Issue 6. Elsevier.
Hsieh, Y.A., and Y. Tsai. 2021. “Automated Asphalt Pavement Raveling Detection and Classification using Convolutional Neural Network and Macrotexture Analysis.” Transportation Research Record 2675. Journal of the Transportation Research Board, Washington, DC.
Huang, Y., and B. Xu. 2006. “Automatic Inspection of Pavement Cracking Distress.” Journal of Electronic Imaging. Volume 15, Issue 1. SPIE and the Society for Imaging Science and Technology.
Huyan, J., W. Li, S. Tighe, Z. Xu, and J. Zhai. 2020. “CrackU-net: A Novel Deep Convolutional Neural Network for Pixelwise Pavement Crack Detection.” Journal of Structural Control and Health Monitoring. Volume 27, Issue 8. Wiley Hindawi.
Islam, S., W.G. Buttlar, R.G. Aldunate, and W.R. Vavrik. 2014. “Measurement of Pavement Roughness using Android-based Smartphone Application.” Transportation Research Record 2457. Journal of the Transportation Research Board, Washington, DC.
Konig, J., M.D. Jenkins, P. Barrie, M. Mannion, and G. Morison. 2019. “A Convolutional Neural Network for Pavement Surface Crack Segmentation using Residual Connections and Attention Gating.” Proceedings - International Conference on Image Processing, pp. 1460–1464.
Lei, X., C. Liu, L. Li, and G. Wang. 2020. “Automated Pavement Distress Detection and Deterioration Analysis Using Street View Map.” IEEE Access. Volume 8, pp. 76163–76172.
Li, B., K.C.P. Wang, A. Zhang, E. Yang, and G. Wang. 2018. “Automatic Classification of Pavement Crack using Deep Convolutional Neural Network.” International Journal of Pavement Engineering, 21(4), 457–463.
Li, B., K.C.P. Wang, A. Zhang, Y. Fei, and G. Sollazzo. 2019. “Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images.” Journal Advanced Transportation, Volume 2019, Article ID 1813763.
Li, H., D. Song, Y. Liu, and B. Li. 2019. “Automatic Pavement Crack Detection by Multi-Scale Image Fusion.” IEEE Transactions on Intelligent Transportation Systems, Volume 20, No. 6, pp. 2025-2036.
Li, N., X. Hou, X. Yang, and Y. Dong. 2009. “Automation Recognition of Pavement Surface Distress Based on Support Vector Machine.” Second International Conference on Intelligent Networks and Intelligent Systems. IEEE.
Li, P., W. Chao, L. Shuangmiao, and F. Baocai. 2015. “Research on Crack Detection Method of Airport Runway Based on Twice-threshold Segmentation.” Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control. IEEE.
Lin, J., and Y. Liu. 2010. “Potholes Detection based on SVM in the Pavement Distress Image.” Proceedings, 9th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, Hong Kong, China. pp. 544–547.
Liu, Q., L. Sun, A. Kornhauser, J. Sun, and N. Sangwa. 2019. “Road Roughness Acquisition and Classification using Improved Restricted Boltzmann Machine Deep Learning Algorithm.” Sensor Review. Volume 39, Issue 6.
Maeda, H., Y. Sekimoto, T. Seto, T. Kashiyama, and H. Omata. 2018. “Road Damage Detection and Classification using Deep Neural Networks with Smartphone Images.” Computer-Aided Civil and Infrastructure Engineering. Volume 33, pp. 1127-1141.
Majidifard, H., Y. Adu-Gyamfi, and W.G. Buttlar. 2020a. “Deep Machine Learning Approach to Develop a New Asphalt Pavement Condition Index.” Construction and Building Materials. Volume 247. Elsevier.
Majidifard, H., P. Jin, Y. Adu-Gyamfi, and W.G. Buttlar. 2020b. “Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses.” Transportation Research Record 2674. Journal of the Transportation Research Board, Washington, DC.
McGhee, K. 2004. NCHRP Synthesis 334: Automated Pavement Distress Collection Techniques. National Cooperative Highway Research Program, Transportation Research Board, Washington DC.
Medina, J.R., R. Salim, B.S. Underwood, and K. Kaloush. 2020. “Experimental Study for Crowdsourced Ride Quality Index Estimation using Smartphones.” Journal of Transportation Engineering, Part B: Pavements, Volume 146, Issue 4. ASCE.
Miller, J. S., and W. Y. Bellinger. 2014. Distress Identification Manual for the Long-Term Pavement Performance Program (Fifth Revised Edition). Report No. FHWA-HRT-13-092. FHWA, McLean, VA.
Moussa, G., and K. Hussain. 2011. “A New Technique for Automatic Detection and Parameters Estimation of Pavement Crack.” 4th International Multi-Conference on Engineering Technology Innovation. Volume 2011. IMETI.
Nguyen, T.S., M. Avila, and S. Begot. 2009. “Automatic Detection and Classification of Defect on Road Pavement using Anisotropy Measure.” 17th European Signal Processing Conference. IEEE.
Nguyen, N.T.H., T.H. Le, S. Perry, and T.T. Nguyen. 2018. Pavement Crack Detection using Convolutional Neural Network. Proceedings, 9th International Symposium on Information and Communication Technology.
Nie, M., and K. Wang. 2018. “Pavement Distress Detection Based on Transfer Learning.” 5th International Conference on Systems and Informatics, pp. 435–439.
Oliveira, H., and P.L. Correia. 2009. “Automatic Road Crack Segmentation using Entropy and Image Dynamic Thresholding.” 17th European Signal Processing Conference. IEEE.
Oliveira, H., and P.L. Correia. 2012. Automatic Road Crack Detection and Characterization. IEEE Transactions on Intelligent Transportation Systems, Volume 14(1), pp. 155–168.
Pan, Y., X. Zhang, J. Tian, X. Jin, L. Luo, and K. Yang. 2017. “Mapping Asphalt Pavement Aging and Condition using Multiple Endmember Spectral Mixture Analysis in Beijing, China.” Journal of Applied Remote Sensing, Volume 11, No. 1.
Pan, Y., X. Zhang, G. Cervone, and L. Yang. 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, Volume 11, No. 10.
Pierce, L.M., and N.D. Weitzel. 2019. NCHRP Synthesis 531: Automated Pavement Condition Surveys. National Cooperative Highway Research Program, Transportation Research Board, Washington DC.
Praveena, M., and V. Jaiganesh. 2017. “A Literature Review on Supervised Machine Learning Algorithms and Boosting Process.” International Journal of Computer Applications. Volume 169, Issue 8.
Ranyal, E., A. Sadhu, and K. Jain. 2022. “Road Condition Monitoring using Smart Sensing and Artificial Intelligence: A Review.” Sensors. Volume 22, Issue 8.
Schapire, R.E. 2003. “The Boosting Approach to Machine Learning: An Overview.” Nonlinear Estimation and Classification. Springer, New York, NY.
Schapire, R.E., and Y. Freund. 2013. “Boosting: Foundations and Algorithms.” Kybernetes. Volume 42, Issue 1.
Shah, S., and C. Deshmukh. 2019. “Pothole and Bump Detection using Convolution Neural Networks.” Transportation Electrification Conference (India). IEEE.
Sholevar, N., A. Golroo, and S.R. Esfahani. 2022. “Machine Learning Techniques for Pavement Condition Evaluation.” Automation in Construction. Volume 136.
Song, W., G. Jia, H. Zhu, D. Jia, and L. Gao. 2020. “Automated Pavement Crack Damage Detection using Deep Multiscale Convolutional Features.” Journal of Advanced Transportation.
Takanashi, M., Y. Ishii, S.I. Sato, N. Sano, and K. Sanda. 2020. “Road-Deterioration Detection using Road Vibration Data with Machine-learning Approach.” International Conference on Prognostics and Health Management. IEEE.
Tsai, Y.C.J., and F. Li. 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. Volume 138, Issue 5.
UK Department for Transport. 2021. Technical Note: Road Condition and Maintenance Data. Network Condition & Geography Statistics Branch, London, England.
USAF. 2004. Engineering Technical Letter (ETL) 04-9: Pavement Engineering Assessment (EA) Standards. Tyndall Air Force Base, FL.
Wang, K.C., J.Q. Li, and G. Yang. 2020. NCHRP Web-Only Document 288: Standard Definitions for Common Types of Pavement Cracking. National Cooperative Highway Research Program, Transportation Research Board, Washington, DC.
Wang, S., S. Qiu, W. Wang, D. Xiao, and K.C. Wang. 2017. “Cracking Classification using Minimum Rectangular Cover–based Support Vector Machine.” Journal of Computing in Civil Engineering, Volume 31, Issue 5.
Wang, X., and Z. Hu. 2017. “Grid-based Pavement Crack Analysis using Deep Learning.” 4th International Conference on Transportation Information and Safety. IEEE.
Xu, Y., and Z. Zhang. 2022. “Review of Applications of Artificial Intelligence Algorithms in Pavement Management.” Journal of Transportation Engineering, Part B: Pavements. Volume 148, Issue 3.
Yang, F., L. Zhang, S. Yu, D. Prokhorov, X. Mei, and H. Ling. 2019. “Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection.” Transactions on Intelligent Transportation Systems. Volume 21, Issue 4. IEEE.
Yang, F., L. Zhang, S. Yu, D. Prokhorov, X. Mei, and H. Ling. 2020. “Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection.” IEEE Transactions on Intelligent Transportation Systems. Volume 21, pp. 1525–1535.
Zhang, A., K.C.P. Wang, B. Li, E. Yang, X. Dai, Y. Peng, Y. Fei, Y. Liu, J.Q. Li, C. Chen. 2017. “Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces using a Deep-Learning Network,” Computer-Aided Civil and Infrastructure Engineering, Volume 32(10), pp. 805–819, https://doi.org/10.1111/MICE.12297
Zhang, A., K.C.P. Wang, Y. Fei, Y. Liu, C. Chen, G. Yang, J.Q. Li, E. Yang, and S. Qiu. 2019. “Automated Pixel-level Pavement Crack Detection on 3D Asphalt Surfaces with a Recurrent Neural Network.” Computer-Aided Civil and Infrastructure Engineering. Issue 34(3).
Zhang, D., Q. Zou, H. Lin, X. Xu, L. He, R. Gui, and Q. Li. 2018. “Automatic Pavement Defect Detection using 3D Laser Profiling Technology.” Automation in Construction. Volume 96.
Zhou, Y., F. Wang, N. Meghanathan, and Y. Huang. 2016. “Seed-based Approach for Automated Crack Detection from Pavement Images.” Transportation Research Record 2589. Journal of the Transportation Research Board, Washington, DC.
Zhu, J., J. Zhong, T. Ma, X. Huang, W. Zhang, and Y. Zhou. 2022. “Pavement Distress Detection using Convolutional Neural Networks with Images Captured via UAV.” Automation in Construction. Volume 133.
Zhu, W., J. Wu, T. Fu, J. Wang, J. Zhang, and Q. Shangguan. 2021. “Dynamic Prediction of Traffic Incident Duration on Urban Expressways: A Deep Learning Approach Based on LSTM and MLP.” Journal of Intelligent and Connected Vehicles. Volume 4(2).