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Detection of cracks in asphalt pavement during road inspection processes

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Road inspection is one of key processes of a pavement management system, whose function is to examine and describe the road infrastructure condition. When thoroughly performed, it provides the information required to implement an adequate road infrastructure maintenance policy and plan ad hoc repairs or refurbishments. This article discusses a solution for automatic asphalt pavement cracking detection, based on image-processing technology. This solution makes it possible to identify different crack types, i.e., transverse, longitudinal, alligator-type and technological cracks. The detection process is based on the application of various methods, including statistical difference identification for pre-assumed image analysis directions, i.e., in and opposite to the test vehicle running direction. The purpose of the morphological and filtering operations applied was to reduce the image noise level. The solution proposed was verified using video material in the form of a sequence of images recorded using the test vehicle.
Rocznik
Tom
Strony
175--184
Opis fizyczny
Bibliogr. 24 poz.
Twórcy
autor
  • Faculty of Transport, The Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, Poland
Bibliografia
  • 1. Czech Piotr. 2017. “Physically disabled pedestrians - road users in terms of road accidents.” In: E. Macioszek, G. Sierpiński, ed., Contemporary challenges of transport systems and traffic engineering. Lecture Notes in Network Systems, Vol. 2: 157-165. Springer. ISSN: 2367-3370. DOI: https://doi.org/10.1007/978-3-319-43985-3_14.
  • 2. Czech Piotr. 2017. “Underage pedestrian road users in terms of road accidents.” In: G. Sierpiński, ed., Intelligent Transport Systems and Travel Behaviour. Advances in Intelligent Systems and Computing, Vol. 505: 75-85. Springer. ISSN: 2194-5357. DOI: https://doi.org/10.1007/978-3-319-43991-4_4.
  • 3. Czech Piotr. 2012. “Diagnosis of industrial gearboxes condition by vibration and time-frequency, scale-frequency, frequency-frequency analysis.” Metalurgija 51(4): 521-524. ISSN: 0543-5846.
  • 4. Madej Henryk, Czech Piotr. 2010. “Discrete wavelet transform and probabilistic neural network in IC engine fault diagnosis.” Eksploatacja i Niezawodnosc – Maintenance and Reliability 4(48): 47-54. ISSN 1507-2711.
  • 5. Wang Kelvin C., Weiguo Gong. 2005. “Real-time automated survey system of pavement cracking in parallel environment.” Journal of Infrastructure Systems 11(3): 154-64. DOI: http://doi.org/10.1061/ASCE1076-0342200511:3154.
  • 6. Yaxiong Huang, Bugao Xu. 2006. “Automatic inspection of pavement cracking distress.” Journal of Electronic Imaging 15(1): 013017. DOI: http://doi.org/10.1117/1.2177650.
  • 7. Yukin Sun, Ezzatollah Salari, Ellie Chou. 2009. “Automated pavement distress detection using advanced image processing techniques.” In: Proceedings of 2009 IEEE International Conference on Electro/Information Technology, EIT 2009: 373-77. DOI: http://doi.org/10.1109/EIT.2009.5189645.
  • 8. Saha Arpita, Bhupendra Singh, Subhadip Biswas. 2017. “Effect of nano-materials on asphalt concrete mixes; a case study.” European Transport/Transporti Europei 65: 1-12. ISSN 1825-3997.
  • 9. Saumya Amarasiri, Manjriker Gunaratne, Sudeep Sarkar. 2010. “Modeling of crack depths in digital images of concrete pavements using optical reflection properties.” Journal of Transportation Engineering 136(6): 489-99. DOI: http://doi.org/10.1061/(ASCE)TE.1943-5436.0000095.
  • 10. Haas Carl. 1996. “Evolution of an automated crack sealer: a study in construction technology development.” Automation in Construction 4(4): 293-305. DOI: http://doi.org/10.1016/0926-5805(95)00010-0.
  • 11. Kim Young S., Hyun S. Yoo, Jeong H. Lee, Seung W. Han. 2009. “Chronological development history of X-Y table based pavement crack sealers and research findings for practical use in the field.” Automation in Construction 18(5): 513-524. DOI: http://doi.org/10.1016/j.autcon.2009.02.007.
  • 12. Nejad Moghadas, Fereidoon, Hamzeh Zakeri. 2011. “A comparison of multi-resolution methods for detection and isolation of pavement distress.” Expert Systems with Applications 38(3): 2857-2872. DOI: http://doi.org/10.1016/j.eswa.2010.08.079.
  • 13. Starck Jean-Luc, Fionn Murtagh, Emmanuel J. Candès, David L. Donoho. 2003. “Gray and color image contrast enhancement by the curvelet transform.” IEEE Transactions on Image Processing 12(6): 706-717. DOI: http://doi.org/10.1109/TIP.2003.813140.
  • 14. Tsai Yi-Chang, Kaul Vivek, Russell M Mersereau. 2010. “Critical assessment of pavement distress segmentation methods.” Journal of Transportation Engineering 136(1): 11-19. DOI: http://doi.org/10.1061/(ASCE)TE.1943-5436.0000051.
  • 15. Moussa Ghada, Hussain Khaled. 2011. “A new technique for automatic detection and parameters estimation of pavement crack.” In: Fourth International Multi-conference on Engineering Technology Innovation, IMETI. DOI: http://doi.org/10.13140/2.1.3191.2001.
  • 16. Boykov Yuri Y., Marie-Pierre Jolly. 2001. “Interactive graph cuts for optimal boundary & region segmentation of objects in ND images.” In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 20011:105-112. DOI: http://doi.org/10.1109/ICCV.2001.937505.
  • 17. Yin Li, Jian Sun, Chi-Keung Tang, Heung-Yeung Shum. 2004. “Lazy snapping.” ACM Transactions on Graphics 23(3): 303-308. DOI: http://doi.org/10.1145/1015706.1015719.
  • 18. Achanta Radhakrishna, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, Sabine Susstrunk 2010. “SLIC superpixels.” EPFL Technical Report 149300. DOI: http://doi.org/10.1109/TPAMI.2012.120.
  • 19. Varadharajan Srivatsan, Jose Sobhagya, Sharma Karan, Lars Wander, Christoph Mertz. 2014. “Vision for road inspection.” In: 2014 IEEE Winter Conference on Applications of Computer Vision: 115-122. DOI: http://doi.org/10.1109/WACV.2014.6836111.
  • 20. Žuraulis Vidas, Loreta Levulytė, Edgar Sokolovskij. 2014. “The impact of road roughness on the duration of contact between a vehicle wheel and road surface.” Transport 29(4): 431-439. DOI: http://doi.org/10.3846/16484142.2014.984330.
  • 21. Byrne Matthew, Tony Parry, Ricardo Isola, Andrew Dawson. 2013. “Identifying road defect information from smartphones.” Road and Transport Research 22(1): 39-50.
  • 22. Kodippily Sachi, Irina Holleran, Erik Glynn. 2016. “Characterising bitumen binders for pavements in the Auckland region.” Road & Transport Research: A Journal of Australian and New Zealand Research and Practice 25(4): 27-38. ISSN: 1037-5783.
  • 23. Cyganek, Boguslaw, J. Paul Siebert. 2009. An Introduction to 3D Computer Vision Techniques and Algorithms. Chichester, UK: John Wiley & Sons. DOI: http://doi.org/10.1002/9780470699720.
  • 24. Bowyer, Kevin, Narendra Ahuja. 1996. Advances in Image Understanding: A Festschrift for Azriel Rosenfeld. Los Alamitos, USA: Wiley-IEEE Computer Society Press. ISBN: 978-0-8186-7644-4.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c7edccc0-29cf-46e5-9e19-fbf7819c95a3
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