Detección automática de grietas de pavimento asfáltico aplicando características geométricas y descriptores de forma
Palabras clave:
Ajuste de Contraste, Características Geométricas y Descriptores de Forma, Corrección de Intensidad, Detección de Grietas, Eliminación de Ruido, Operaciones Morfológicas.Resumen
Las grietas son el principal daño en la superficie del pavimento, porque de estas se derivan los demás tipos de deterioros. La mayoría de grietas en imágenes de pavimento se encuentran con objetos no deseados y desconectadas. Para resolver este problema, se aplica el filtro mediana, para el suavizado de la imagen; el ajuste de contraste, para realzar la grieta; la segmentación, aplicando la media y la desviación estándar de los niveles de gris, para delimitar las grietas; el procesamiento morfológico, para fusionar separaciones estrechas; la eliminación de grietas falsas, aplicando características geométricas y descriptores de forma; y la conexión de grietas, para obtener grietas continuas. Los resultados experimentales fueron obtenidos de las imágenes de pavimento captadas por el sistema semiautomático y el algoritmo generador implementado. Las pruebas demostraron que las grietas fueron detectadas, con una sensibilidad de 81,72% y una especificidad de 99,96% para las imágenes captadas.
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[2] H. Rababaah, Asphalt Pavement Crack Classification: A Comparative Study of Three AI Approaches: Multilayer Perceptron, Genetic Algorithms, and Self-Organizing Maps. Thesis (Master of Sciences). Indiana University South Bend. Graduate Faculty. Department of Computer and Information Sciences. South Bend, 2005, 120 p.
[3] J. Miller and W. Bellinger, Distress Identification Manual for the Long-Term Pavement Performance Program. Virginia, 2003. 164 p.
[4] Invías y UNAL, Manual para la inspección visual de pavimentos flexibles. Bogotá D.C., Colombia, Oct. 2006. [Online] Disponible en: http://invias.gov.co/index.php/documentos-tecnicos-izq/doc_details/974-manual-parala-inspeccion-visual-de-pavimentosflexibles?tmpl=component
[5] ASTM International, Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys. Estados Unidos, May. 2013. [Online] Disponible en: http://enterprise1.astm.org/DOWNLOAD/D6433.1210479-1.pdf
[6] Gevial SAS, Ingeniería y Gestión Vial. Tecnología aplicada a la ingeniería. Bogotá D.C., Colombia, 2012. [Online] Disponible en: http://gevial.co/brochure.pdf
[7] M. S. Townes et al., National Cooperative Highway Research Program. Automated Pavement Distress Collection Techniques. Washington D. C.: Transportation Research Board of the National Academies, 2004. 85 p.
[8] X. Yu, Pavement Surface Distress Detection and Evaluation Using Image Processing Technology. Thesis (Master of Science Degree in Electrical Engineering). The University of Toledo. Graduate Faculty. The Department of Electrical Engineering. Toledo, 2011, 72 p.
[9] K. Wang, “Designs and Implementations of automated systems for pavement surface distress Survey”. Journal of Infrastructure Systems. Vol. 6, No. 1. ASCE: Transportation & Development Institute, March, 2000, pp. 24-32.
[10] M. Gunaratne, Investigation of Automated and Interactive Crack Measurement Systems. Report. The University of South Florida. College of Engineering. Department of Civil and Environmental Engineering. Tampa, 2008, 93 p.
[11] X. Yao, M. Yao and B. Xu, “Automated Measurements of Road Cracks Using Line-Scan Imaging”. Journal of Testing and Evaluation. Vol. 39, No 4. ASTM: American Society for Testing and Materials, February, 2011, pp. 1-9.
[12] S. Zhaoyun, W. Chaofan and S. Aimin, “Study of Image-Based Pavement Cracking Measurement Techniques”. ICEMI: The Ninth International Conference on Electronic Measurements & Instruments. IEEE, 2009, pp. 140-143.
[13] S. Chambon and J. Moliard, “Automatic Road Pavement Assessment with Image Processing: Review and Comparison”. International Journal of Geophysics. Vol. 2011. Hindawi Publishing Corporation, June, 2011, pp. 1-20.
[14] R. González, R. Woods and S: Eddins, Digital Image Processing Using Matlab. Third New Jersey: Prentice Hall, 2004, 609 p.
[15] R. Haas, W. Hudson and J. Zaniewski, Modern Pavement Management. Malabar, Florida: Krieger Publishing Company, 1994, 604 p.
[16] B. Xu and Y. Huang, Development of an Automatic Pavement Surface Distress Inspection System. Report. The University of Texas. Center for Transportation Research. Texas Department of Transportation. Austin, 2003, 28 p.
[17] F. Liu, G. Xu, Y. Yang, X. Niu and Y. Pan, “Novel Approach to Pavement Cracking Automatic Detection Based on Segment Extending”. International Symposium on Knowledge Acquisition and Modeling. IEEE, 2008, pp. 610-614.
[18] Y. Sun, E. Salari and E. Chou, “Automated Pavement Distress Detection Using Advanced Image Processing Techniques”. International Conference on Electro/Information Technology. IEEE, 2009, pp. 373-377.
[19] R. Haralick, K. Shanmugam and I. Dinstein, “Textural Features for Image Classification”. IEEE Transactions on Systems, Man and Cybernetics. Vol. SMC-3, 1973, pp. 610-621.
[20] Y. Hu, C. Zhao and H. Wang, “Automatic Pavement Crack Detection Using Texture and Shape Descriptors”. IETE Technical Review. Vol. 27, No 5, September, 2010, pp. 398-405.
[21] Q. Li, Q. Zou, and X. Liu, “Pavement Crack Classification via Spatial Distribution Features”. EURASIP Journal on Advances in Signal Processing. Vol. 2011, No. 1. Hindawi Publishing Corporation, March, 2011, pp. 649-675.
[22] M. Castillo Rangel y S. Mesar Ribero, Algoritmo para reconocimiento de fallas tipo fisura en pavimento rígido mediante tratamiento de imágenes digitales. Trabajo de grado (Ingeniero Civil). Universidad Industrial de Santander. Facultad Fisicomecánicas. Escuela de Ingeniería Civil. Bucaramanga, 2011, 95 p.
[23] SONY, Corp. DSC-HX9V Cámara Digital Compacta Sony. [Online] Disponible en: http://www.sony.es/product/dsc-h-series/dsc-hx9v/características#tab
[24] H. Freeman, “On the Encoding of Arbitrary Geometric Configurations”. IRE Transactions on Electronic Computers. Vol. 10, 1961, pp. 260-268.
[25] Consejo de Directores de Carreteras de Iberia e Iberoamérica. Catálogo de deterioros de pavimentos flexibles. Vol. 11. México, 2002, 25 p.
[26] S. Ramírez, Detección de grietas en la cinta asfáltica mediante análisis de imágenes. Tesis. Maestría en Tecnología Avanzada. Instituto Politécnico Nacional. Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada. Querétaro, 2012, 64 p.
[27] C. Wang and A. Sha, “Pavement Crack Classification based on Chain Code”. FSKD: Seventh International Conference on Fuzzy Systems and Knowledge Discovery. Rochester, N.Y., 2010, pp. 593- 597.
[28] P. Zhang and H. Li, “Recognition of Pavement Surface Crack”. ICNC: Sixth International Conference on Natural Computation, 2010, pp. 3393-3396.
[29] L. Ying, Beamlet Transform Based Technique for Pavement Image Processing and Classification. Thesis. Master of Science Degree in Electrical Engineering. The University of Toledo. Graduate Faculty. The Department of Electrical Engineering. Toledo, 2009, 60 p.
[30] H. Zhou, S. Yang and J. Zhu, “Illumination Invariant Enhancement and Threshold Segmentation Algorithm for Asphalt Pavement Crack Image”. Image Processing IEEE. 2010. pp. 2-5.
[31] Q. Zou, Y. Cao, Q. Li, Q. Mao and S. Wang, “CrackTree: Automatic Crack Detection from Pavement Images”. Pattern Recognition Letters. Vol. 33, No. 3. February, 2011, pp. 227-238.
[32] O. C. Puan, M. Mustaffar and T. C. Ling, “Automated Pavement Imaging Program (APIP) for Pavement Cracks Classification and Quantification”. No. i. Image Rochester, N.Y., 1998, pp. 1-17.
[33] A. Monem, A. Oloufa and H. Mahgoub, “Asphalt Crack Detection Using Thermography”. Inframation: Thermal Imaging’s Leading Users Conference, 2005. pp. 1-12.
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Publicado
2012-10-31
Cómo citar
Porras Díaz, H., Castañeda Pinzón, E. A., Sanabria Echeverry, D. Y., & Medina Pérez, G. M. (2012). Detección automática de grietas de pavimento asfáltico aplicando características geométricas y descriptores de forma. Inge Cuc, 8(1), 261–280. Recuperado a partir de https://ojstest.certika.co/ingecuc/article/view/265
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