A New Open Curve Detection Algorithm for Extracting the Laser Lines on the Road

Abstract:

This paper proposes a new open curve detection algorithm for recognizing the laser rays on the road to verify the roughness of the road surface by parsing the curvature of the laser rays. The laser line in the image is first enhanced by the bright line enhance filtering to remove the clutters generated by the background. The enhanced line has the characteristics that its gray value is greater than that of background, so the established energy functional in open curve detection process has a gray value constraint to drive the evolution curve to the brightest place in image. The line on the road surface is an open curve throughout the whole image, so we fixed the two endpoints of the evolution curve to maintain it is an open curve in curve evolution. Moreover, we design an edge inhibit operator to restrain the influence of objects edges to ensure the accuracy of the test laser line. We compared our algorithm with the edge detection operators, and the comparison results showed that our method is more accurate and more robustness to the background.

• Periodical: Advanced Engineering Forum (Volumes 2-3)
• Pages: 205-210
• DOI: https://doi.org/10.4028/www.scientific.net/AEF.2-3.205
• Online since: December 2011
• Authors: Hui Jie Fan, Li Po Zhao, Si Yuan He, Yan Dong Tang
• Keywords: Edge Inhibit Operator, Line Enhance Filtering, Open Curve, Road Surface Roughness, Snake Model

References

[1] Lu Z.X., Zhao L.Y., Li X.Q. and Yuan J., Simulation of Road Surface Roughness Based on the Piecewise Fractal Function, International Federation for Information Processing 2011, p.294–305.

DOI: 10.1007/978-3-642-18369-0_34

[2] Youngguk Seo, Development and Implementation of Korea's First Percent within Limit (PWL) Specification for Road Pavements, Journal of Civil Engineering (2010), 14(3): pp.353-361.

DOI: 10.1007/s12205-010-0353-8

[3] Kirschke K.R. and Velinsky S., A histogram-based approach for automated pavement-cracks sensing, Journal of Transportation Engineering, ASCE, New York, NY, 1992, 1l9(3): pp.700-710.

DOI: 10.1061/(asce)0733-947x(1992)118:5(700)

[4] Chang H D, Shi X J and Glazier C, Real-Time Image Thresholding Based on Sample Space Reduction and Interpolation Approach，Journal of Computing in Civil Engineering, ASCE/October, 2003.

DOI: 10.1061/(asce)0887-3801(2003)17:4(264)

[5] Xu B.G. and Huang Y.X., Development ofan Automatic Pavement Surface Distress Inspection System, Final Report, October, 2003.

[6] Li Q., Zhang L. , You J., Zhang D. and Bhattacharya P., Dark line detection with line width extraction, 15th IEEE International Conference on Image Processing , ICIP 2008. San Diego, CA, 12-15 October, 2008, pp.621-624.

DOI: 10.1109/icip.2008.4711831

[7] Keokanlaya S., Somsak C., Shatoshi W., and Kazuhiko H., Fingerprint Image Enhancement with Second Derivative Gaussian Filter and Directional Wavelet Transform, 2010 Second International Conference on Computer Engineering and Applications, pp.112-116.

DOI: 10.1109/iccea.2010.178

[8] Li X.M., Zhu L.L. and Tang Y.D., Boundary Detection Using Open Spline Curve Based on Mumford-Shah Model, ACTA AUTOMATICA SINICA, 2009. Vol. 35, No. 2. pp.132-136.

DOI: 10.3724/sp.j.1004.2009.00132

[9] M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models, International Journal of Computer Vision, vol. 1, pp.321-331, (1987).

DOI: 10.1007/bf00133570