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Research Article

A review on automated pavement distress detection methods

& | (Reviewing Editor)
Article: 1374822 | Received 05 May 2017, Accepted 29 Aug 2017, Published online: 17 Sep 2017

References

  • Abdi, I., Fridman, L., Marchi, E., Brown, D., Angell, W., & Reimer, B. (2015, December 4–8). Detecting road surface wetness from audio: A deep learning approach. In Pattern recognition (pp. 1–5). Cancun: IEEE. doi:10.1109/ICPR.2016.7900169
  • Ahmad, N., Wistuba, M., & Lorenzl, H. (2012, June 4–8). GPR as a crack detection tool for asphalt pavements. In Proceedings of the 14th International Conference on Ground Penetrating Radar (GPR) (pp. 551–555). Shanghai. doi:10.1109/ICGPR.2012.6254925
  • ASCE. (2016). Closing the infrastructure for America’s economic future investment gap to failure to act : The impact of infrastructure.
  • Becerik-Gerber, B., Masri, S. F., & Jahanshahi, M. R. (2015). An inexpensive vision-based approach for the autonomous detection, localization, and quantification of pavement defects. Innovations Deserving Exploratory Analysis, 169.
  • Benedetto, A., Tosti, F., Bianchini Ciampoli, L., & D’Amico, F. (2016). An overview of ground-penetrating radar signal processing techniques for road inspections. Signal Processing, 132, 201–209. doi:10.1016/j.sigpro.2016.05.016
  • Bennett, C. R., De Solminihac, H., & Chamorro, A. (2006). Data collection technologies for road management. Roads and Rural Transport Thematic Group, 30, 1–8.
  • Busuioc, D., Anstey, K., Rappaport, C., Birken, R., Doughty, J., & Wang, M. (2011). Novel low-cost millimeter-wave system for road surface characterization. Security, 7983, 79831H–79831H–9. doi:10.1117/12.880025
  • Buttlar, W. G., & Islam, M. S. (2014). Integration of smart-phone-based pavement roughness data collection tool with asset management system. Retrieved from https://www.purdue.edu/discoverypark/nextrans/assets/pdfs/098IY04IntegrationofSmartphone-Based-PavementRoughnessdatacollectiontoolwithassetmanagementsystem.pdf
  • Casas-avellaneda, D. A., & López-parra, J. F. (2016). Detection and localization of potholes in roadways using smartphones Detección y localización de imperfecciones viales utilizando smartphones. DYNA, 83, 44919. doi:10.15446/dyna.v83n195.44919
  • Chiculita, C., & Frangu, L. (2015). A low-cost car vibration acquisition system. In 21st international symposium/or design and technology in electronic packaging (SIITME) (pp. 281–285). Oradea: IEEE. doi:10.1109/SIITME.2016.7777295
  • Cord, A., & Chambon, S. (2012). Automatic road defect detection by textural pattern recognition based on adaboost. Computer-Aided Civil and Infrastructure Engineering, 27, 244–259. doi:10.1111/j.1467-8667.2011.00736.x
  • Du, Y., Liu, C., Wu, D., & Li, S. (2016). Application of vehicle mounted accelerometers to measure pavement roughness. International Journal of Distributed Sensor Networks, 1–8 (Article ID 8413146). doi:10.1155/2016/8413146
  • Erdogan, G., Alexander, L., & Rajamani, R. (2011). Estimation of tire-road friction coefficient using a novel wireless piezoelectric tire sensor. IEEE Sensors Journal, 11, 267–279. doi:10.1109/JSEN.2010.2053198
  • Gavilán, M., Balcones, D., Marcos, O., Llorca, D. F., Sotelo, M. A., Parra, I., … Amírola, A. (2011). Adaptive road crack detection system by pavement classification. Sensors, 11, 9628–9657. doi:10.3390/s111009628
  • Hadjidemetriou, G. M., Christodoulou, S. E., & Vela, P. A. (2016, April 18–20). Automated detection of pavement patches utilizing support vector machine classification. In 2016 18th Mediterranean Electrotechnical Conference (MELECON) (pp. 1–5). Limassol. doi:10.1109/MELCON.2016.7495460
  • Hartmann, A., & Dewulf, G. (2009, December 9–11). Contradictions in infrastructure management – The introduction of performance-based contracts at the Dutch highways and waterways agency. In 2009 2nd International Conference on Infrastructure Systems and Services: Developing 21st Century Infrastructure Networks, INFRA. Chennai: IEEE. doi:10.1109/INFRA.2009.5397881
  • He, Y., Wang, J., Qiu, H., Zhang, W., & Xie, J. (2011, October 15–17). A research of pavement potholes detection based on three-dimensional projection transformation. In Proceedings - 4th International Congress on Image and Signal Processing, CISP (Vol. 4, pp. 1805–1808). IEEE. doi:10.1109/CISP.2011.6100646
  • Huang, Y., Hempel, P., & Copenhaver, T. (2011). Texas department of transportation 3D transverse profiling system for high speed rut measurement. Journal of Infrastructure Systems, 19, 54. doi:10.1061/(ASCE)IS.1943-555X.0000088
  • Huidrom, L., Das, L. K., & 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
  • Jo, Y., & Ryu, S. (2015). Pothole detection system using a black-box camera. Sensors, 15, 29316–29331. doi:10.3390/s151129316
  • Joubert, D., Tyatyantsi, A., Mphahlehle, J., & Manchidi, V. (2011, November 23–25). Pothole tagging system. In 4th Robotics and Mechatronics Conference of South Africa. Pretoria: CSIR. Retrieved from https://hdl.handle.net/10204/5384
  • Kamal, K., Mathavan, S., Zafar, T., Moazzam, I., Ali, A., Ahmad, S. U., & Rahman, M. (2016). Performance assessment of Kinect as a sensor for pothole imaging and metrology. International Journal of Pavement Engineering, 8436, 1–12. doi:10.1080/10298436.2016.1187730
  • Karaşahin, M., Saltan, M., & Çetin, S. (2014). Determination of seal coat deterioration using image processing methods. Construction and Building Materials, 53, 273–283. doi:10.1016/j.conbuildmat.2013.11.090
  • Katicha, S. W., El Khoury, J., & Flintsch, G. W. (2016). Assessing the effectiveness of probe vehicle acceleration measurements in estimating road roughness. International Journal of Pavement Engineering, 17, 698–708. doi:10.1080/10298436.2015.1014815
  • Kertész, I., Lovas, T., & Barsi, A. (2008). Photogrammetric pavement detection system. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, doi:10.1201/9780203882191.ch85
  • Kim, T., & Ryu, S.-K. (2014a). Review and analysis of pothole detection methods. Journal of Emerging Trends in Computing and Information Sciences, 5, 603–608. Retrieved from http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CC0QFjAA&url=http://www.cisjournal.org/journalofcomputing/archive/vol5no8/vol5no8_3.pdf&ei=bl5SVNzWNsK4OJK9gdAC&usg=AFQjCNFDjwiYdD82Wei1Mfkpu6c8UpBgBg&bvm=bv.78597519,d.ZWU%5Cnhttp://www.cis
  • Kim, T., & Ryu, S. K. (2014b). Pothole DB based on 2D Images and Video Data. Sensors, 5, 527–531. doi:10.3390/s151129316
  • Koch, C., & Brilakis, I. (2011). Pothole detection in asphalt pavement images. Advanced Engineering Informatics, 25, 507–515. doi:10.1016/j.aei.2011.01.002
  • Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2015). A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, 29, 196–210. doi:10.1016/j.aei.2015.01.008
  • Koch, C., Jog, G. M., & Brilakis, I. (2012). Towards automated pothole distress assessment using asphalt pavement video data. Journal of Computing in Civil Engineering, 27, 167. doi:10.1061/(ASCE)CP.1943-5487.0000232
  • Kongrattanaprasert, W., Nomura, H., Kamakura, T., & Ueda, K. (2010, August 23–27). Detection of road surface states from tire noise using neural network analysis. In IEEJ Transactions on Industry Applications (Vol. 20, pp. 920–925). doi:10.1541/ieejias.130.920
  • Landa, J., & Prochazka, D. (2014). Automatic road inventory using LiDAR. In Procedia economics and finance (Vol. 12, pp. 363–370). Brno, Cz: Elsevier B.V. doi:10.1016/S2212-5671(14)00356-6
  • Lei, J., Wang, E., Zeng, J., Wang, W., & Wu, J. (2014, October 8–11). Research of acquisition method for pavement surface texture based on photometric stereo techniques. In 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014 (pp. 1596–1601). Qungdoa, CN: IEEE. doi:10.1109/ITSC.2014.6957921
  • Li, Q., Yao, M., Yao, X., & Xu, B. (2009). A real-time 3D scanning system for pavement distortion inspection. Measurement Science and Technology, 21(15702), 1–8. doi:10.1088/0957-0233/21/1/015702
  • Li, Q., Zou, Q., Zhang, D., & Mao, Q. (2011). FoSA: F* seed-growing approach for crack-line detection from pavement images. Image and Vision Computing, 29, 861–872. doi:10.1016/j.imavis.2011.10.003
  • Madli, R., Hebbar, S., Pattar, P., & Golla, V. (2015). Automatic detection and notification of potholes and humps on roads to aid drivers. IEEE Sensors Journal, 15, 4313–4318. doi:10.1109/JSEN.2015.2417579
  • Mahmoudzadeh, A., Firoozi Yeganeh, S., & Golroo, A. (2015, November). Kinect, a novel cutting edge tool in pavement data collection. In ISPRS - International archives of the photogrammetry, remote sensing and spatial information sciences, XL-1-W5 (pp. 425–431). doi:10.5194/isprsarchives-XL-1-W5-425-2015
  • Mahone, D. C., & Runkle, S. N. (1972). Pavement friction needs. Highway Research Board.
  • Mancini, A., Malinverni, E. S., Frontoni, E., & Zingaretti, P. (2013). Road pavement crack automatic detection by MMS images. In 2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings (Vol. 21, pp. 1589–1596). Platanias-Chania. doi:10.1109/MED.2013.6608934
  • Mathavan, S., Kamal, K., & Rahman, M. (2015). A review of three-dimensional imaging technologies for pavement distress detection and measurements. IEEE Transactions on Intelligent Transportation Systems, 16, 2353–2362. doi:10.1109/TITS.2015.2428655
  • Mathavan, S., Rahman, M., & Kamal, K. (2015). Use of a self-organizing map for crack detection in highly textured pavement images. Journal of Infrastructure Systems, 21(3), 1–11. doi:10.1061/(ASCE)IS.1943-555X.0000237
  • Mathavan, S., Rahman, M., Stonecliffe-Jones, M., & Kamal, K. (2014). Pavement raveling detection and measurement from synchronized intensity and range images. Transportation Research Record: Journal of the Transportation Research Board, 2457, 3–11. doi:10.3141/2457-01
  • Mednis, A., Strazdins, G., Liepins, M., Gordjusins, A., & Selavo, L. (2010). RoadMic: Road surface monitoring using vehicular sensor networks with microphones. Communications in Computer and Information Science, 88, 417–429. doi:10.1007/978-3-642-14306-9_42
  • Mertz, C., Varadharajan, S., Jose, S., Sharma, K., Wander, L., & Wang, J. (2015). City-wide road distress monitoring with smartphones. In Proceedings of ITS World Congress, September, 2014 (pp. 1–9). Pittsburg, PA.
  • Miah, S., Uus, A., Liatsis, P., Roberts, S., Twist, S., Hovens, M., & Godding, H. (2015). Design of multidimensional sensor fusion system for road pavement inspection. In 2nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015 (pp. 304–308). doi:10.1109/IWSSIP.2015.7314236
  • Miller, J. S., & Bellinger, W. Y. (2014). Distress identification manual.
  • Moazzam, I., Kamal, K., Mathavan, S., Usman, S., & Rahman, M. (2013, October 6–9). Metrology and visualization of potholes using the Microsoft Kinect sensor. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC (pp. 1284–1291). The Hague, NL: IEEE. doi:10.1109/ITSC.2013.6728408
  • Moghadas Nejad, F., & Zakeri, H. (2011). A comparison of multi-resolution methods for detection and isolation of pavement distress. Expert Systems with Applications, 38, 2857–2872. doi:10.1016/j.eswa.2010.08.079
  • Nguyen, T. S., Avila, M., & Bardet, J. (2012). Detection of defects in road surface by a vision system. Detection of Defects in Road Surface by a Vision System, 14(1). doi:10.1109/MELCON.2008.4618541
  • Nguyen, T. S., Begot, S., Duculty, F., & Avila, M. (2011). Free-form anisotropy: A new method for crack detection on pavement surface images. Proceedings - International Conference on Image Processing, ICIP, 1069–1072. doi:10.1109/ICIP.2011.6115610
  • Oliveira, H., & Correia, P. L. (2009). Automatic road crack segmentation using entropy and image dynamic thresholding. European Signal Processing Conference, 622–626.
  • Oliveira, H., & Correia, P. L. (2013, March 1). Automatic road crack detection and characterization. In IEEE Transactions on Intelligent Transportation Systems (Vol. 14, pp. 155–168). doi:10.1109/TITS.2012.2208630
  • Pagliari, D., & Pinto, L. (2015). Calibration of Kinect for Xbox One and comparison between the two generations of microsoft sensors. Sensors, 15, 27569–27589. doi:10.3390/s151127569
  • Plati, C., Georgouli, K., & Loizos, A. (2012). Review of NDT assessment of road pavement using GPR. Nondestructive Testing of Materials and Structures. RILEM Bookseries, 6, 855–860. doi:10.1007/978-94-007-0723-8
  • Prasanna, P., Dana, K., Gucunski, N., & Basily, B. (2012). Computer-vision based crack detection and analysis. In SPIE smart structures and materials+ nondestructive evaluation and health monitoring (April 26) (p. 834542). San Diego, CA. doi:10.1117/12.915384
  • Premachandra, C., Premachandra, H. W. H., Parape, C. D., & Kawanaka, H. (2015). Road crack detection using color variance distribution and discriminant analysis for approaching smooth vehicle movement on non-smooth roads. International Journal of Machine Learning and Cybernetics, 6, 545–553. doi:10.1007/s13042-014-0240-6
  • Puan, O. C., Mustaffar, M., & Ling, T.-C. (2007). Automated Pavement Imaging Program (APIP) for pavement cracks classification and quantification. Malaysian Journal of Civil Engineering, 19(1), 1–16.
  • Quintana, M., Torres, J., & Menendez, J. M. (2015). A simplified computer vision system for road surface inspection and maintenance. IEEE Transactions on Intelligent Transportation Systems, 17, 608–619. doi:10.1109/TITS.2015.2482222
  • Radopoulou, S. C., & Brilakis, I. (2015). Patch detection for pavement assessment. Automation in Construction, 53, 95–104. doi:10.1016/j.autcon.2015.03.010
  • Rajab, M. I. (2013). Profiling of external deformation in asphalt pavement rutting using image analysis method. In Proceedings - 2013 IEEE 9th International Colloquium on Signal Processing and its Applications, CSPA (May 8-10) (pp. 99–102). IEEE. doi:10.1109/CSPA.2013.6530022
  • Rehman, S. K. U., Ibrahim, Z., Memon, S. A., & Jameel, M. (2016). Nondestructive test methods for concrete bridges: A review. Construction and Building Materials, 107, 58–86. doi:10.1016/j.conbuildmat.2015.12.011
  • Roadroid. (2013). Road conditioning monitoring using smart phones. Quick Start Version 1.2.1.
  • Ryu, S. K., Kim, T., & Kim, Y. R. (2015). Image-based pothole detection system for ITS service and road management system. Mathematical Problems in Engineering, 2015(ID 968361). doi:10.1155/2015/968361
  • Sayers, M. W., & Karamihas, S. M. (1998). The little book of profiling. Basic information about measuring and interpreting road profiles.
  • Seaden, G., & Manseau, A. (2001). Public policy and construction innovation. Building Research & Information, 29, 182–196. doi:10.1080/09613210010027701
  • Shahin, M. Y., & Walther, J. A. (1990). Pavement maintenance management PAVER system. Technical department of the Army.
  • Singh, K. B., & Taheri, S. (2014). Estimation of tire–road friction coefficient and its application in chassis control systems. Systems Science & Control Engineering, 3, 39–61. doi:10.1080/21642583.2014.985804
  • Solla, M., Lagüela, S., González-Jorge, H., & Arias, P. (2014). Approach to identify cracking in asphalt pavement using GPR and infrared thermographic methods: Preliminary findings. NDT and E International, 62, 55–65. doi:10.1016/j.ndteint.2013.11.006
  • Tsai, Y.-C. J., & 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, 649–656. doi:10.1061/(ASCE)TE.1943-5436.0000353
  • Uddin, W. (2014). An overview of GPR applications for evaluation of pavement thickness and cracking. In Proceedings of the 15th International Conference on Ground Penetrating Radar, GPR 2014 (pp. 925–930). doi:10.1109/ICGPR.2014.6970561
  • Varadharajan, S., Jose, S., Sharma, K., Wander, L., & Mertz, C. (2014). Vision for road inspection. In 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 (pp. 115–122). Carnegie. doi:10.1109/WACV.2014.6836111
  • Vilaa, J. L., Fonseca, J. C., Pinho, A. C. M., & Freitas, E. (2010). 3D surface profile equipment for the characterization of the pavement texture - TexScan. Mechatronics, 20, 674–685. doi:10.1016/j.mechatronics.2010.07.008
  • Waltham, N. (2010). CCD and CMOS sensors. In ISSI Scientific Reports Series (1st ed., pp. 391–408). doi:10.1007/978-1-4614-7804-1_23
  • Wang, M., Birken, R., & Shamsabadi, S. S. (2015). Implementation of a multi-modal mobile sensor system for surface and subsurface assessment of roadways. In Smart sensor phenomena, technology, networks, and systems integration. SPIE. doi:10.1117/12.2084852
  • Yoo, H.-S., & Kim, Y.-S. (2016). Development of a crack recognition algorithm from non-routed pavement images using artificial neural network and binary logistic regression. KSCE Journal of Civil Engineering, 20, 1151–1162. doi:10.1007/s12205-015-1645-9
  • Yu, X., & Salari, E. (2011, May 15–17). Pavement pothole detection and severity measurement using laser imaging. In IEEE International Conference on Electro Information Technology. Mankato, MN: IEEE. doi:10.1109/EIT.2011.5978573
  • Yu, Y., Li, J., Guan, H., & Wang, C. (2014). 3D crack skeleton extraction from mobile LiDAR point clouds. In International Geoscience and Remote Sensing Symposium (IGARSS), July 13–18 (pp. 914–917). Quebec City, QC: IEEE. doi:10.1109/IGARSS.2014.6946574
  • Zalama, E., Gómez-Garcìa-Bermejo, J., Medina, R., & Llamas, J. (2014). Road crack detection using visual features extracted by Gabor filters. Computer-Aided Civil and Infrastructure Engineering, 29, 342–358. doi:10.1111/mice.12042
  • Zhang, J., Qiu, H., Shamsabadi, S. S., Birken, R., & Schirner, G. (2014). WiP abstract: System-level integration of mobile multi-modal multi-sensor systems. In 2014 ACM/IEEE 22d International Conference on Cyber-Physical Systems, ICCPS 2014 (p. 227). Berlin. doi:10.1109/ICCPS.2014.6843740
  • Zhang, Y., Mcdaniel, J. G., & Wang, M. L. (2013). Estimation of pavement macrotexture by principal component analysis of acoustic measurements. Journal of Transportation Engineering, 140(1), 1–12. doi:10.1061/(ASCE)TE.1943-5436.0000617
  • Zou, Q., Cao, Y., Li, Q., Mao, Q., & Wang, S. (2012). CrackTree: Automatic crack detection from pavement images. Pattern Recognition Letters, 33, 227–238. doi:10.1016/j.patrec.2011.11.004