Machine Vision for the Various Road Surface Type Classification Based on Texture Feature

Susi Marianingsih, Widodo Widodo, Marla Sheilamita S. Pieter, Evanita Veronica Manullang, Hendry Y. Nanlohy


The mechanized ability to specify the way surface type is a piece of key enlightenment for autonomous transportation machine navigation like wheelchairs and smart cars. In the present work, the extracted features from the object are getting based on structure and surface evidence using Gray Level Co-occurrence Matrix (GLCM). Furthermore, K-Nearest Neighbor (K-NN) Classifier was built to classify the road surface image into three classes, asphalt, gravel, and pavement. A comparison of KNN and Naïve Bayes (NB) was used in present study. We have constructed a road image dataset of 450 samples from real-world road images in the asphalt, gravel, and pavement. Experiment result that the classification accuracy using the K-NN classifier is 78%, which is better as compared to Naïve Bayes classifier which has a classification accuracy of 72%. The paving class has the smallest accuracy in both classifier methods. The two classifiers have nearly the same computing time, 3.459 seconds for the KNN Classifier and 3.464 seconds for the Naive Bayes Classifier.


Co-occurrence matrix, image data set, K-Nearest Neighbor, Naïve Bayes, road surface types

Full Text:



G. Mao, C. Zhang, K. Shi, W. Ping, “Prediction of the performance and exhaust emissions of ethanol-diesel engine using different neural network,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. pp. 1-15, August 2019,

AK. Tahkur, RS. Kaviti, A. Gehlot, “Modelling the performance and emissions of ethanol-gasoline blend on a gasoline engine using ANFIS,” International Journal of Ambient Energy, January 2021,

H. Y. Nanlohy, I. N. G. Wardana, N. Hamidi, L. Yuliati, and T. Ueda, “The effect of Rh3+ catalyst on the combustion characteristics of crude vegetable oil droplets,” Fuel, vol. 220, pp. 220–232, May 2018, doi: 10.1016/J.FUEL.2018.02.001.

H. Y. Nanlohy, I. N. G. Wardana, M. Yamaguchi, and T. Ueda, “The role of rhodium sulfate on the bond angles of triglyceride molecules and their effect on the combustion characteristics of crude jatropha oil droplets,” Fuel, vol. 279, February 2020, doi: 10.1016/j.fuel.2020.118373.

H. Y. Nanlohy, “Performance and Emissions Analysis of BE85-Gasoline Blends on Spark Ignition Engine,” Automot. Exp., vol. 5, no. 1, pp. 40–48, 2022, doi:

D. Arya., “Deep learning-based road damage detection and classification for multiple countries,” Autom. Constr., vol. 132, p. 103935, 2021, doi: 10.1016/j.autcon.2021.103935.

J. Menegazzo, A. von Wangenheim, “Road surface type classification based on inertial sensors and machine learning,” Computing, vol. 103, pp. 2143–2170, 2021, doi:

T. Beilfuss, K. P. Kortmann, M. Wielitzka, C. Hansen, and T. Ortmaier, “Real-Time Classification of Road Type and Condition in Passenger Vehicles,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 14254–14260, 2020, doi: 10.1016/j.ifacol.2020.12.1161.

S. Marianingsih, F. Utaminingrum, and F. A. Bachtiar, “Road surface types classification using combination of K-nearest neighbor and Naïve Bayes based on GLCM,” Int. J. Adv. Soft Comput. its Appl., vol. 11, no. 2, pp. 15–27, 2019.

D. Fink, A. Busch, M. Wielitzka, and T. Ortmaier, “Resource efficient classification of road conditions through CNN pruning,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 13958–13963, 2020, doi: 10.1016/j.ifacol.2020.12.913.

F Utaminingrum, TA Kurniawan, MA Fauzi, R Maulana, D Syauqy,. “A laser-vision based obstacle detection and distance estimation for smart wheelchair navigation,” 2016 IEEE Int. Conf. Signal Image Process. ICSIP 2016, pp. 123–127, 2017, doi: 10.1109/SIPROCESS.2016.7888236.

M. D’Apuzzo, A. Evangelisti, and V. Nicolosi, “An exploratory step for a general unified approach to labelling of road surface and tyre wet friction,” Accid. Anal. Prev., vol. 138, p. 105462, 2020, doi: 10.1016/j.aap.2020.105462.

A. Septiarini, A. Sunyoto, H. Hamdani, A. A. Kasim, F. Utaminingrum, and H. R. Hatta, “Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features,” Sci. Hortic. (Amsterdam)., vol. 286, p. 110245, 2021, doi: 10.1016/j.scienta.2021.110245.

M. N. Khan and M. M. Ahmed, “Weather and surface condition detection based on road-side webcams: Application of pre-trained Convolutional Neural Network,” Int. J. Transp. Sci. Technol., pp. 1–16, 2021, doi: 10.1016/j.ijtst.2021.06.003.

C. Gorges, K. Öztürk, and R. Liebich, “Impact detection using a machine learning approach and experimental road roughness classification,” Mech. Syst. Signal Process., vol. 117, pp. 738–756, 2019, doi: 10.1016/j.ymssp.2018.07.043.

A. Gholamhosseinian and J. Seitz, “Safety-Centric Vehicle Classification Using Vehicular Networks,” Procedia Comput. Sci., vol. 191, pp. 238–245, 2021, doi: 10.1016/j.procs.2021.07.030.

B. Behera and R. Sikka, “Deep learning for observation of road surfaces and identification of path holes,” Mater. Today Proc., 2021, doi: 10.1016/j.matpr.2021.03.197.

S. Marianingsih and F. Utaminingrum, “Comparison of Support Vector Machine Classifier and Naïve Bayes Classifier on Road Surface Type Classification,” 3rd Int. Conf. Sustain. Inf. Eng. Technol. SIET 2018 - Proc., pp. 48–53, 2018, doi: 10.1109/SIET.2018.8693113.

L. Cheng, X. Zhang, and J. Shen, “Road surface condition classification using deep learning,” J. Vis. Commun. Image Represent., vol. 64, p. 102638, 2019, doi: 10.1016/j.jvcir.2019.102638.

M. A. Agebure, E. O. Oyetunji, and E. Y. Baagyere, “A three-tier road condition classification system using a spiking neural network model,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 5, pp. 1718-1729, 2022, doi: 10.1016/j.jksuci.2020.08.012.

S. Shim, J. Kim, S. W. Lee, and G. C. Cho, “Road surface damage detection based on hierarchical architecture using lightweight auto-encoder network,” Autom. Constr., vol. 130, p. 103833, 2021, doi: 10.1016/j.autcon.2021.103833.

S. Sattar, S. Li, and M. Chapman, “Developing a near real-time road surface anomaly detection approach for road surface monitoring,” Meas. J. Int. Meas. Confed., vol. 185, p. 109990, 2021, doi: 10.1016/j.measurement.2021.109990.

S. Kim, J. Lee, and T. Yoon, “Road surface conditions forecasting in rainy weather using artificial neural networks,” Saf. Sci., vol. 140, p. 105302, 2021, doi: 10.1016/j.ssci.2021.105302.

J. Li, T. Liu, X. Wang, J. Yu, “Automated asphalt pavement damage rate detection based on optimized GA-CNN,” Automation in Construction, vol. 136, April 2022, 104180.

G. Doğan, B. Ergen, “A new mobile convolutional neural network-based approach for pixel-wise road surface crack detection,” Measurement, vol. 195, May 2022, 111119.



  • There are currently no refbacks.

Copyright (c) 2022 Journal of Mechanical Engineering Science and Technology (JMEST)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View My Stats