Aggregate segmentation of asphaltic mixes using digital image processing

• Autorzy: Reyes-Ortiz Oscar R. , Mejía M. , Useche-Castelblanco J. S.

Identyfikatory

DOI

10.24425/bpas.2019.128115

• Języki publikacji: EN

Abstrakty

EN

The study of the different engineering materials according to their mechanical and dynamic characteristics has become an area of research interest in recent years. Several studies have verified that the mechanical properties of the material are directly affected by the distribution and size of the particles that compose it. Such is the case of asphalt mixtures. For this reason, different digital tools have been developed in order to be able to detect the structural components of the elements in a precise, clear and efficient manner. In this work, a segmentation model is developed for different types of dense-graded asphalt mixtures with grain sizes from 9.5 mm to 0.0075 mm, using sieve size reconstruction of the laboratory production curve. The laboratory curve is used to validate the particles detection model that uses morphological operations for elements separation. All this with the objective of developing a versatile tool for the analysis and study of pavement structures in a non-destructive test. The results show that the model presented in this work is able to segment elements with an area greater than 0.0324 mm2 and reproduce the sieve size curves of the mixtures with a high percentage of precision.

Słowa kluczowe

EN

digital image processing; asphalt mixture; morphological operations; particle segmentation

PL

cyfrowe przetwarzanie obrazów; mieszanki asfaltowe; operacje morfologiczne; segmentacja cząstek

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2019
• Tom: Vol. 67, nr 2
• Strony: 279--287
• Opis fizyczny: Bibliogr. 24 poz., rys., wykr., tab.

Twórcy

autor

Reyes-Ortiz Oscar R.

• Nueva Granada Military University, Faculty of Engineering, Bogotá D.C., Colombia

autor

Mejía M.

• Nueva Granada Military University, Faculty of Engineering, Bogotá D.C., Colombia

autor

Useche-Castelblanco J. S.

• Nueva Granada Military University, Faculty of Engineering, Bogotá D.C., Colombia

Bibliografia

• [1] F. Bianconi, F. Di Maria, C. Micale, A. Fernández, and R.W. Harvey, “Grain-size assessment of fine and coarse aggregates through bipolar area morphology,” Mach. Vis. Appl., vol. 26, no. 6, pp. 775–789, 2015.
• [2] F. Di Maria, F. Bianconi, C. Micale, S. Baglioni, and M. Marionni, “Quality assessment for recycling aggregates from construction and demolition waste: An image-based approach for particle size estimation,” Waste Manag., vol. 48, pp. 344–352, 2016.
• [3] J. Han, K. Wang, X. Wang, and P.J.M. Monteiro, “2D image analysis method for evaluating coarse aggregate characteristic and distribution in concrete,” Constr. Build. Mater., vol. 127, pp. 30–42, 2016.
• [4] A.J. Ramme, K. Voss , J. Lesporis, M.S. Lendhey, T.R. Coughlin, E.J. Strauss, and O.D. Kennedy, “Automated Bone Segmentation and Surface Evaluation of a Small Animal Model of Post-Traumatic Osteoarthritis,” Ann. Biomed. Eng., vol. 45, no. 5, pp. 1227–1235, 2017.
• [5] M. Abhik, C. Debashish, B. Kousik, and H. Arpan, “Development of a mass model in estimating weight-wise particle size distribution using digital image processing,” Int. J. Min. Sci. Technol., vol. 27, pp. 435–443, 2017.
• [6] J.S. Athertya and G. Saravana Kumar, “Automatic segmentation of vertebral contours from CT images using fuzzy corners,” Comput. Biol. Med., vol. 72, pp. 75–89, 2016.
• [7] S. Yin, Y. Qian, and M. Gong, “Unsupervised hierarchical image segmentation through fuzzy entropy maximization,” Pattern Recognit. J., vol. 68, pp. 245–259, 2017.
• [8] W. Wang, C. Wu, C. Wu, and W. Regression, “Image Segmentation by Correlation Adaptive Weighted Regression,” Neurocomputing, vol. 17, 2017.
• [9] P. Zarychta, P. Badura, and E. Pietka, “Comparative analysis of selected classifiers in posterior cruciate ligaments computer aided diagnosis,” Bull. Pol. Ac.: Tech., vol. 65, no. 1, 2017.
• [10] C.G. Berrocal, I. Löfgren, K. Lundgren, N. Görander, and C. Halldén, “Characterisation of bending cracks in R/FRC using image analysis,” Submitt. to Mater. Struct., vol. 90, pp. 104–116, 2016.
• [11] I. Michalska-Pożoga, R. Tomkowski, T. Rydzkowski, and V.K. Thakur, “Towards the usage of image analysis technique to measure particles size and composition in wood-polymer composites,” Ind. Crops Prod., vol. 92, pp. 149–156, 2016.
• [12] A. Stankiewicz, T. Marciniak, A. Dąbrowski, M. Stopa, P. Rakowicz, and E. Marciniak, “Denoising methods for improving automatic segmentation in OCT images of human eye,” Bull. Pol. Ac.: Tech., vol. 65, no. 1, 2017.
• [13] G. Vladic, S. Dedijer, L. Koltai, I. Juric, and N. Kašikovic, “Image processing based quality control of coated paper folding,” Measurement, vol. 100, pp. 99–109, 2017.
• [14] M. Mejía and M. Alzate, “Clasificación automática de formas patológicas de eritrocitos humanos Automatic classification of pathological shapes in human erythrocytes,” Rev. Ing., vol. 21, no. 1, pp. 31–48, 2015.
• [15] K. Espinoza, D.L. Valera, J.A. Torres, A. López, and F.D. Molina-aiz, “Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture,” Comput. Electron. Agric., vol. 127, pp. 495–505, 2016.
• [16] A. Tedeschi and F. Benedetto, “A real-time automatic pavement crack and pothole recognition system for mobile Android-based devices,” Adv. Eng. Informatics, vol. 32, pp. 11–25, 2017.
• [17] H. Yoo and Y. Kim, “Development of a Crack Recognition Algorithm from Non-routed Pavement Images using Artificial Neural Network and Binary Logistic Regression,” KSCE J. Civ. Eng., vol. 20, pp. 1151–1162, 2016.
• [18] C. Sha, J. Hou, and H. Cui, “A robust 2D Otsu’s thresholding method in image segmentation q,” J. Vis. Commun. Image R. J., vol. 41, pp. 339–351, 2016.
• [19] S.M.E. Harb, N. Ashidi, M. Isa, and S.A. Salamah, “Improved image magnification algorithm based on Otsu,” Comput. Electr. Eng. J., vol. 46, pp. 338–355, 2015.
• [20] S. Mohammad, A. Hasan, and K. Ko, “Depth edge detection by image-based smoothing and morphological operations,” J. Comput. Des. Eng., vol. 3, pp. 191–197, 2016.
• [21] S.R. Borra, G.J. Reddy, E.S. Reddy, J. Reddy, and S. Reddy, “Classification of Fingerprint Images with the aid of Morphological Operation and AGNN Classifier,” Appl. Comput. Informatics, 2017.
• [22] J.E. Arco, J.M. Górriz, J. Ramírez, I. Álvarez, and C.G. Puntonet, “Digital image analysis for automatic enumeration of malaria parasites using morphological operations,” Expert Syst. Appl., vol. 42, pp. 3041–3047, 2015.
• [23] R.C. Gonzalez and R.E. Woods, Digital Image Processing, vol. 21. Pearson Education, 2011.
• [24] X. Bai, “Morphological center operator based infrared and visible image fusion through correlation coefficient,” Infrared Phys. Technol., vol. 76, pp. 546–554, 2016.

Uwagi

EN

The authors wish to thank the Vice-Rector for research at the Nueva Granada Military University, in particular for the financing of the high impact research project IMP-ING-2132.

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).