HYBRID HUMAN-ARTIFICIAL INTELLIGENCE APPROACH FOR PAVEMENT DISTRESS ASSESSMENT (PICUCHA)
DOI:
https://doi.org/10.14311/CEJ.2017.02.0013Klíčová slova:
Pavement Survey, Pavement Evaluation, Artificial Intelligence, PICUCHA MethodAbstrakt
[1] Xun, Y., Ming, Y., Xu, B., 2011. Automated Measurements of Road Cracks Using Line-Scan Imaging. Journal of Testing and Evaluation 39(4).
[2] Salari, E., Yu, X., 2011. Pavement distress detection and classification using a Genetic Algorithm. 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), IEEE p. 1–5.
[3] Lin, J., Liu, Y., 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, DCABES 2010: 544–7, Doi: 10.1109/DCABES.2010.115.
[4] Chambon, S., Moliard, J.M., 2011. Automatic road pavement assessment with image processing: Review and comparison. International Journal of Geophysics 2011, Doi: 10.1155/2011/989354.
[5] Ouyang, A., Luo, C., Zhou, C., 2010. Surface Distresses Detection of Pavement Based on Digital Image Processing. IFIP Advances in Information and Communication Technology, vol. 347 AICT. Nanchang, China p. 368–75.
[6] Ho, T., Chou, C., Chen, C., Lin, J., 2010. Pavement distress image recognition using k-means and classification algorithms. In: Tizani, W., editor. Computing in Civil and Building Engineering, Proceedings of the International Conference, Nottingham, UK: Nottingham University Press p. 73–8.
[7] Nguyen, T.S., Begot, S., Duculty, F., Bardet, J.-C., Avila, M., 2010. Pavement Cracking Detection Using an Anisotropy Measurement. 11ème IASTED International Conference on Computer Graphics and Imaging (CGIM), Innsbruck, Austria.
[8] Zou, Q., Cao, Y., Li, Q., Mao, Q., Wang, S., 2012. CrackTree: Automatic crack detection from pavement images. Pattern Recognition Letters 33(3): 227–38, Doi: 10.1016/j.patrec.2011.11.004.
[9] Wang, H., Chen, Z., Sun, L., 2013. Image Preprocessing Methods to Identify Micro-cracks of Road Pavement. Optics and Photonics Journal 3(2): 99–102, Doi: 10.4236/opj.2013.32B025.
[10] 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: 312–21, Doi: 10.1016/j.sbspro.2013.11.124.
[11] Gavilán, M., Balcones, D., Marcos, O., Llorca, D.F., Sotelo, M.A., Parra, I., et al., 2011. Adaptive road crack detection system by pavement classification. Sensors 11(10): 9628–57, Doi: 10.3390/s111009628.
[12] Radopoulou, S.-C., Brilakis, I., 2013. Patch Distress Detection in Asphalt Pavement Images. Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2013), Montreal.
[13] Salini, R., Xu, B., Lenngren, C.A., 2015. Application of artificial intelligence for optimization in pavement management. International Journal of Engineering and Technology Innovation 5(3): 189–97.
[14] Salini, R., 2010. INTELLIPave: Uma Abordagem Baseada em Inteligência Artificial para a Modelagem de Pavimentos Asfálticos. University of Minho, 2010.
[15] Salini, R., Neves, J., Abelha, A., 2009. INTELLIPave - Considering aside failure criteria and unknown
variables in evolutionary intelligence based models for asphalt pavement. Proceedings - 23rd European
Conference on Modelling and Simulation, ECMS 2009, Madrid, Spain p. 624–9.
[16] Salini, R., Xu, B., Souliman, M., 2016. Impact of Image Resolution on Pavement Distress Detection Using PICUCHA Methodology. Stavební Obzor - The Civil Engineering Journal 25(4), Doi:
10.14311/CEJ.2016.04.0024.
[17] Salini, R., Xu, B., Paplauskas, P., 2017. Pavement Distress Detection with PICUCHA Methodology for Area-Scan Cameras and Dark Images. Stavební Obzor - The Civil Engineering Journal (1): 34–45, Doi: 10.14311/CEJ.2017.01.0004.
[18] Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2(5): 359–66.
[19] Jain, A.K., Mao, J., Mohiuddin, K.M., 1996. Artificial neural networks: A tutorial. Computer 29(3): 31–
44.
Stažení
Reference
Xun, Y., Ming, Y., Xu, B., 2011. Automated Measurements of Road Cracks Using Line-Scan Imaging. Journal of Testing and Evaluation 39(4).
Salari, E., Yu, X., 2011. Pavement distress detection and classification using a Genetic Algorithm. 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), IEEE p. 1–5.
Lin, J., Liu, Y., 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, DCABES 2010: 544–7, Doi: 10.1109/DCABES.2010.115.
Chambon, S., Moliard, J.M., 2011. Automatic road pavement assessment with image processing: Review and comparison. International Journal of Geophysics 2011, Doi: 10.1155/2011/989354.
Ouyang, A., Luo, C., Zhou, C., 2010. Surface Distresses Detection of Pavement Based on Digital Image Processing. IFIP Advances in Information and Communication Technology, vol. 347 AICT. Nanchang, China p. 368–75.
Ho, T., Chou, C., Chen, C., Lin, J., 2010. Pavement distress image recognition using k-means and classification algorithms. In: Tizani, W., editor. Computing in Civil and Building Engineering, Proceedings of the International Conference, Nottingham, UK: Nottingham University Press p. 73–8.
Nguyen, T.S., Begot, S., Duculty, F., Bardet, J.-C., Avila, M., 2010. Pavement Cracking Detection Using an Anisotropy Measurement. 11ème IASTED International Conference on Computer Graphics and Imaging (CGIM), Innsbruck, Austria.
Zou, Q., Cao, Y., Li, Q., Mao, Q., Wang, S., 2012. CrackTree: Automatic crack detection from pavement images. Pattern Recognition Letters 33(3): 227–38, Doi: 10.1016/j.patrec.2011.11.004.
Wang, H., Chen, Z., Sun, L., 2013. Image Preprocessing Methods to Identify Micro-cracks of Road Pavement. Optics and Photonics Journal 3(2): 99–102, Doi: 10.4236/opj.2013.32B025.
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: 312–21, Doi: 10.1016/j.sbspro.2013.11.124.
Gavilán, M., Balcones, D., Marcos, O., Llorca, D.F., Sotelo, M.A., Parra, I., et al., 2011. Adaptive road crack detection system by pavement classification. Sensors 11(10): 9628–57, Doi: 10.3390/s111009628.
Radopoulou, S.-C., Brilakis, I., 2013. Patch Distress Detection in Asphalt Pavement Images. Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2013), Montreal.
Salini, R., Xu, B., Lenngren, C.A., 2015. Application of artificial intelligence for optimization in pavement management. International Journal of Engineering and Technology Innovation 5(3): 189–97.
Salini, R., 2010. INTELLIPave: Uma Abordagem Baseada em Inteligência Artificial para a Modelagem de Pavimentos Asfálticos. University of Minho, 2010.
Salini, R., Neves, J., Abelha, A., 2009. INTELLIPave - Considering aside failure criteria and unknown
variables in evolutionary intelligence based models for asphalt pavement. Proceedings - 23rd European
Conference on Modelling and Simulation, ECMS 2009, Madrid, Spain p. 624–9.
Salini, R., Xu, B., Souliman, M., 2016. Impact of Image Resolution on Pavement Distress Detection Using PICUCHA Methodology. Stavební Obzor - The Civil Engineering Journal 25(4), Doi:
14311/CEJ.2016.04.0024.
Salini, R., Xu, B., Paplauskas, P., 2017. Pavement Distress Detection with PICUCHA Methodology for Area-Scan Cameras and Dark Images. Stavební Obzor - The Civil Engineering Journal (1): 34–45, Doi: 10.14311/CEJ.2017.01.0004.
Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2(5): 359–66. [19] Jain, A.K., Mao, J., Mohiuddin, K.M., 1996. Artificial neural networks: A tutorial. Computer 29(3): 31–
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