Texton Based Segmentation for Road Defect Detection from Aerial Imagery

Adhi Prahara(1*), Son Ali Akbar(2), Ahmad Azhari(3),


(1) Universitas Ahmad Dahlan
(2) Universitas Malaysia Pahang
(3) Universitas Ahmad Dahlan
(*) Corresponding Author

Abstract


Road defect such as potholes and road cracks, became a problem that arose every year in Indonesia. It could endanger drivers and damage the vehicles. It also obstructed the goods distribution via land transportation that had major impact to the economy. To handle this problem, the government released an online complaints system that utilized information system and GPS technology. To follow up the complaints especially road defect problem, a survey was conducted to assess the damage. Manual survey became less effective for large road area and might disturb the traffic. Therefore, we used road aerial imagery captured by Unmanned Aerial Vehicle (UAV). The proposed method used texton combined with K-Nearest Neighbor (K-NN) to segment the road area and Support Vector Machine (SVM) to detect the road defect. Morphological operation followed by blob analysis was performed to locate, measure, and determine the type of defect. The experiment showed that the proposed method able to segment the road area and detect road defect from aerial imagery with good Boundary F1 score.

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References


R. A. Eaton, R. H. Joubert, and E. A. Wright, “Pothole Primer: A Public Administrator's Guide to Understanding and Managing the Pothole Problem (Rev. Dec. 1989.),” Special Report, US Army Corps of Engineers-Cold Regions Research & Engineering Laboratory, p. 34, 1989.

C. Koch and I. Brilakis, “Pothole detection in asphalt pavement images,” Adv. Eng. Informatics, vol. 25, no. 3, pp. 507–515, Aug. 2011.

K. Azhar, F. Murtaza, M. H. Yousaf, and H. A. Habib, “Computer vision based detection and localization of potholes in asphalt pavement images,” in 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2016, pp. 1–5.

P. Wang, Y. Hu, Y. Dai, and M. Tian, “Asphalt Pavement Pothole Detection and Segmentation Based on Wavelet Energy Field,” Math. Probl. Eng., vol. 2017, pp. 1–13, Feb. 2017.

Y. Jo, and S. Ryu, “Pothole Detection System Using a Black-box Camera,” Sensors, vol. 15, no. 11, pp. 29316–29331, Nov. 2015.

Z. Zhang, X. Ai, C. K. Chan, and N. Dahnoun, “An efficient algorithm for pothole detection using stereo vision,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, pp. 564–568.

K. Kamal et al., “Performance assessment of Kinect as a sensor for pothole imaging and metrology,” Int. J. Pavement Eng., vol. 19, no. 7, pp. 565–576, Jul. 2018.

M. Bellone and G. Reina, “Pavement distress detection and avoidance for intelligent vehicles,” Int. J. Veh. Auton. Syst., vol. 13, no. 2, p. 152, 2016.

J. Bridgers and T. Chiang, “Mobile pothole detection system and method,” United States patent-US 9,365,217, Booz Allen Hamilton Inc, 14 June 2016.

J. Malik, S. Belongie, T. Leung, and J. Shi, “Contour and Texture Analysis for Image Segmentation,” Int. J. Comput. Vis., vol. 43, no. 1, pp. 7–27, 2001.

T. Leung and J. Malik, “Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons,” Int. J. Comput. Vis., vol. 43, no. 1, pp. 29–44, 2001.

C. Schmid, “Constructing models for content-based image retrieval,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 2, p. II-39-II-45.

A. Prahara, I. T. R. Yanto, and T. Herawan, “Histogram Thresholding for Automatic Color Segmentation Based on k-means Clustering,” Springer, Cham, 2017, pp. 344–354.

G. Csurka, D. Larlus, and F. Perronnin, “What is a good evaluation measure for semantic segmentation?,” in Procedings of the British Machine Vision Conference 2013, 2013, p. 32.1-32.11.

J. Fritsch, T. Kuhnl, and A. Geiger, “A new performance measure and evaluation benchmark for road detection algorithms,” in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 2013, pp. 1693–1700.




DOI: https://doi.org/10.29099/ijair.v4i2.179

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