Técnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentos

Oscar Javier Reyes Ortiz, Marcela Mejia, Juan Sebastian Useche Castelblanco


Debido al incremento en los costos de mantenimiento, rehabilitación y construcción de vías, estudiar las estructuras de pavimento para determinar su comportamiento y sus características mecánicas propias analizando la distribución y posición de sus partículas, se ha vuelto un campo de gran importancia en la ingeniería. Las nuevas herramientas de análisis buscan hacer este estudio más eficiente reduciendo su costo y tiempo de ejecución mediante el procesamiento digital de imágenes. El procesamiento digital tradicional está limitado en su sensibilidad ante perturbaciones externas que puedan modificar la imagen, por eso se han implementado diferentes técnicas de inteligencia artificial (IA) para optimizar los algoritmos. Este trabajo presenta una revisión de las diferentes aplicaciones de técnicas de IA recientes en el procesamiento de  imágenes. Después se revisan los trabajos realizados específicamente con imágenes de pavimentos y se proponen posibles aplicaciones para implementar en este campo con inteligencia artificial

Palabras clave

Pavimentos; Inteligencia Artificial; Procesamiento Digital de Imágenes

Texto completo:



Abdullah, S. & Abdolrazzagh-nezhad, M., 2014. Fuzzy job-shop scheduling problems : A review. Information Sciences, 278, pp.380–407. Available at: http://dx.doi.org/10.1016/j.ins.2014.03.060.

Ali, M. et al., 2015. An image watermarking scheme in wavelet domain with optimized compensation of singular value decomposition via artificial bee colony. Information Sciences, 301, pp.44–60. Available at: http://dx.doi.org/10.1016/j.ins.2014.12.042.

Apostolidis, G.K. & Hadjileontiadis, L.J., 2017. Swarm decomposition : A novel signal analysis using swarm intelligence. Signal Processing journal, 132, pp.40–50.

Arjmandzadeh, Z., Safi, M. & Nazemi, A., 2017. A new neural network model for solving random interval linear programming problems. Neural Networks, 89, pp.11–18.

Athertya, J.S. & Saravana Kumar, G., 2016. Automatic segmentation of vertebral contours from CT images using fuzzy corners. Computers in Biology and Medicine, 72, pp.75–89.

Babashamsi, P. et al., 2016. Evaluation of pavement life cycle cost analysis : Review and analysis. International Journal of Pavement Research and Technology, 9, pp.241–254. Available at: http://dx.doi.org/10.1016/j.ijprt.2016.08.004.

Banitalebi, A. et al., 2015. Enhanced compact artificial bee colony. INFORMATION SCIENCES, 298, pp.491–511. Available at: http://dx.doi.org/10.1016/j.ins.2014.12.015.

Bayar, N. et al., 2015. Fault detection , diagnosis and recovery using Arti fi cial Immune Systems : A review. Engineering Applications of Artificial Intelligence, 46, pp.43–57.

Berrocal, C.G.. et al., 2016. Characterisation of bending cracks in R/FRC using image analysis. submitted to: Materials and Structures, 90, pp.104–116.

Bessa, I.S., Castelo Branco, V.T.F. & Soares, J.B., 2012. Evaluation of different digital image processing software for aggregates and hot mix asphalt characterizations. Construction and Building Materials, 37, pp.370–378.

Bianconi, F. et al., 2015. Grain-size assessment of fine and coarse aggregates through bipolar area morphology. Machine Vision and Applications, 26(6), pp.775–789. Available at: http://link.springer.com/10.1007/s00138-015-0692-z.

Bose, A. & Mali, K., 2016. Fuzzy-based artificial bee colony optimization for gray image segmentation. Signal, Image and Video Processing, 10, pp.1089–1096.

Bouchet, A. et al., 2016. Fuzzy mathematical morphology for color images defined by fuzzy preference relations. Pattern Recognition, 60, pp.720–733.

Cao, W. et al., 2017. A review on neural networks with random weights. Neurocomputing, 0, pp.1–10.

Casti, P. et al., 2015. Analysis of structural similarity in mammograms for detection of bilateral asymmetry. IEEE Trans. Med. Imaging, 34(2), pp.662–671.

Cordeiro, F.R., Santos, W.P. & Silva-Filho, A.G., 2016. A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images. Expert Systems with Applications, 65, pp.116–126.

Costa, G., Matos, W. & Martinez, R., 2017. Artificial immune systems applied to fault detection and isolation : A brief review of immune response-based approaches and a case study. Applied Soft Computing Journal, 57, pp.118–131. Available at: http://dx.doi.org/10.1016/j.asoc.2017.03.031.

Cristea, V., Leblebici, Y. & Almási, A., 2016. Neurocomputing Review of advances in neural networks : Neural design technology stack. Neurocomputing, 174, pp.31–41.

Cui, L. et al., 2017. A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization. Information Sciences, 414, pp.53–67.

Espinoza, K. et al., 2016. 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. Computers and Electronics in Agriculture, 127, pp.495–505.

Ferrari, A., Lombardi, S. & Signoroni, A., 2017. Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging. Pattern Recognition journal, 61, pp.629–640.

Galdámez, P.L., Raveane, W. & Arrieta, A.G., 2017. A brief review of the ear recognition process using deep neural networks. Journal of Applied Logic, 24, pp.62–70. Available at: http://dx.doi.org/10.1016/j.jal.2016.11.014.

Gong, T. et al., 2016. Magnetic resonance imaging-clonal selection algorithm : An intelligent adaptive enhancement of brain image with an improved immune algorithm. Engineering Applications of Artificial Intelligence, (October).

Han, J. et al., 2016. 2D image analysis method for evaluating coarse aggregate characteristic and distribution in concrete. Construction and Building Materials, 127, pp.30–42.

Hatata, A.Y. & Sedhom, B.E., 2017. Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system. Alexandria Engineering Journal.

Hekayati, J. & Rahimpour, M.R., 2017. Estimation of the saturation pressure of pure ionic liquids using MLP arti fi cial neural networks and the revised isofugacity criterion.

Journal of Molecular Liquids journal, 230, pp.85–95.

Helena, G., Miranda, B. & Cezar, J., 2015. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. , 64, pp.334–346.

Hu, L. et al., 2016. Effect of three-dimensional macrotexture characteristics on dynamic frictional coefficient of asphalt pavement surface. Construction and Building Materials, 126, pp.720–729.

Huang, C., Li, H. & Li, W., 2017. Store classification using Text-Exemplar-Similarity and Hypotheses- Weighted-CNN. J. Vis. Commun. Image R. journal, 44, pp.21–28.

Hugo, V. et al., 2009. Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images. NDT & E International, 42, pp.644–651.

Islam, S. et al., 2014. Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier. Waste Management, 34, pp.281–290.

Jani, D.B., Mishra, M. & Sahoo, P.K., 2017. Application of arti fi cial neural network for predicting performance of solid desiccant cooling systems – A review. Renewable and Sustainable Energy Reviews, 80(November 2016), pp.352–366. Available at: http://dx.doi.org/10.1016/j.rser.2017.05.169.

Jiang, J. et al., 2017. Effect of the contact structure characteristics on rutting performance in asphalt mixtures using 2D imaging analysis. Construction and Building Materials, 136, pp.426–435.

Knabben, R.M. et al., 2016. Evaluation of sound absorption capacity of asphalt mixtures. Applied Acoustics, 114, pp.266–274. Available at: http://dx.doi.org/10.1016/j.apacoust.2016.08.008.

Kumar, D. & Mishra, K.K., 2017. Portfolio optimization using novel co-variance guided Artificial Bee Colony algorithm. Swarm and Evolutionary Computation journal, 33(April 2016), pp.119–130.

Kuo, R.., Tseng, Y.. & Yao Chen, Z., 2016. Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data. Journal of Intelligent Manufacturing, 27, pp.1191–1207.

Li, B. et al., 2016. Optik Image retrieval via balance-evolution artificial bee colony algorithm and lateral inhibition. , 127, pp.11775–11785.

Li, Q. et al., 2011. FoSA: F* Seed-growing Approach for crack-

line detection from pavement images. Image and Vision Computing, 29(12), pp.861–872.

Lim, C.H., Vats, E. & Seng, C., 2015. Fuzzy human motion analysis : A review. Pattern Recognition journal, 48, pp.1773–1796.

Magna, G. et al., 2015. Adaptive classification model based on artificial immune system for breast cancer detection. Proceedings of the Eighteenth AISEM Annual Conference.

Magna, G. et al., 2016. Identification of mammography anomalies for breast cancer detection by an ensemble of classification models based on artificial immune system. Knowle dge-Based Systems, 101, pp.60–70.

Mashaly, A.F. & Alazba, A.A., 2016. MLP and MLR models for instantaneous thermal efficiency prediction of solar still under hyper-arid environment. Computers and Electronics in Agriculture, 122, pp.146–155.

Mavrovouniotis, M., Li, C. & Yang, S., 2017. A survey of swarm intelligence for dynamic optimization : Algorithms and applications. Swarm and Evolutionary Computation journal, 33(January), pp.1–17.

Mejía, M. & Alzate, M., 2015. Clasificación automática de formas patológicas de eritrocitos humanos Automatic classification of pathological shapes in human erythrocytes. Revista Ingeniería, 21(1), pp.31–48.

Michalska-Po??oga, I. et al., 2016. Towards the usage of image analysis technique to measure particles size and composition in wood-polymer composites. Industrial Crops and Products, 92, pp.149–156.

Mollajan, A., Ghiasi-Freez, J. & Memarian, H., 2016. Improving pore type identification from thin section images using an integrated fuzzy fusion of multiple classifiers. Journal of Natural Gas Science and Engineering, 31, pp.396–404.

Nebti, S. & Boukerram, A., 2017. Swarm intelligence inspired classi fi ers for facial recognition. Swarm and Evolutionary Computation, 32, pp.150–166.

Ngan, S., 2017. A unified representation of intuitionistic fuzzy sets , hesitant fuzzy sets and generalized hesitant fuzzy sets based on their u-maps. , 69, pp.257–276.

Priya, E. & Srinivasan, S., 2016. ScienceDirect Automated object and image level classification of TB images using support vector neural network classifier. , pp.1–9.

Radopoulou, S.C. & Brilakis, I., 2015. Patch detection for pavement assessment. Automation in Construction, 53, pp.95–104.

Reza, M. et al., 2011. Permanent deformation analysis of asphalt mixtures using soft computing techniques. Expert Systems with Applications, 38, pp.6081–6100.

Schmidt, B. et al., 2017. Optimizing an artificial immune system algorithm in support of flow-Based internet traffic classification. Applied Soft Computing j, 54, pp.1–22.

Sebari, I. & He, D., 2013. Automatic fuzzy object-based analysis of VHSR images for urban objects extraction. ISPRS Journal of Photogrammetry and Remote Sensing, 79, pp.171–184.

Sebari & He, 2009. Approach to nonparametric cooperative multiband segmentation with adaptive threshold. Applied Optics, 20, pp.3967–3978.

Shafabakhsh, G. & Tanakizadeh, A., 2015. Investigation of loading features effects on resilient modulus of asphalt mixtures using Adaptive Neuro-Fuzzy Inference System. Construction and Building Materials, 76, pp.256–263.

Shafabakhsh, G.H., Ani, O.J. & Talebsafa, M., 2015. Artificial neural network modeling ( ANN ) for predicting rutting performance of nano-modified hot-mix asphalt mixtures containing steel slag aggregates. Construction and Building Materials journal, 85, pp.136–143.

Shang, R. et al., 2014. Engineering Applications of Arti fi cial Intelligence Change detection in SAR images by arti fi cial immune multi-objective clustering. Engineering Applications of Artificial Intelligence, 31, pp.1–15. Available at: http://dx.doi.org/10.1016/j.engappai.2014.02.004.

Sun, L., Ning, G. & Tan, S., 2014. AUTOMATIC PAVEMENT CRACK RECOGNITION BASED ON BP NEURAL NETWORK. Swarm Intelligence in Transportation Engineering, 26(1), pp.11–22.

Tan, J.H. et al., 2017. Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. Journal of Computational Science.

Tedeschi, A. & Benedetto, F., 2017. A real-time automatic pavement crack and pothole recognition system for mobile Android-based devices. Advanced Engineering Informatics, 32, pp.11–25.

Yangyan, L. et al., 2015. Joint Embeddings of Shapes and Images via CNN Image Purification. ACM Transactions on Graphics, 34(6), pp.1–12.

Yoo, H. & Kim, Y., 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, pp.1151–1162.

Yu, X. et al., 2016. Infrared Physics & Technology Target extraction of banded blurred infrared images by immune dynamical algorithm with two-dimensional minimum distance immune field. , 77, pp.94–99.

Zhang, G. et al., 2017. SIFT Matching with CNN Evidences for Particular Object Retrieval. Neurocomputing, 0, pp.1–11.

Zhang, W. et al., 2016. Learning structure of stereoscopic image for no-reference quality assessment with convolutional neural network. Pattern Recognition journal, 59, pp.176–187.

DOI: https://doi.org/10.24050/reia.v16i31.1215

Métricas de artículo

Vistas de resumen

Cargando métricas ...

Enlaces refback

  • No hay ningún enlace refback.

Copyright (c) 2019 Revista EIA

Licencia de Creative Commons
Este obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.







Sede de Las Palmas: Km 2 + 200 Vía al Aeropuerto José María Córdova Envigado, Colombia. Código Postal: 055428
Tel: (574) 354 90 90. Fax: (574) 386 11 60

Sede de Zúñiga: Calle 25 Sur 42-73 Envigado, Colombia. Código Postal: 055420
Tel: (574) 354 90 90. Fax: (574) 331 34 78
NIT: 890.983.722-6

Sistema OJS - Metabiblioteca |