International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 10, No. 8, 8 Aug. 2018
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Simple Detection and Classification of Road Lanes based on Image Processing
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Author(s)
Lucia Vanesa Araya
1,*
Natacha Espada
1
Marcelo Tosini
1
Lucas Leiva
1,2
Index Terms
Lane detection, image processing, video processing
Abstract
Road accidents, besides being one of the main causes of mortality, have an economic impact on vehicle owners. Several conditions as driver imprudence, road conditions and obstacles are the main factor that will cause accidents. The most important automotive industries are incorporating technology to reduce risk in vehicles. In this way, lane detection systems have important attention, because from this data is possible to determine risk situations such as presence of obstacles, incorrect lane changes or lane departures. This paper proposes a technique for lane detection, based on image processing, which allows identifying the position of lateral lanes and their type. The method is composed of four stages: edge enhancement, potential lanes detection, post-processing and color lane estimation. The method was proved using image dataset and video captures over 12.000 frames. The accuracy of the system was of 91.9%.
Cite This Paper
Lucia Vanesa Araya, Natacha Espada, Marcelo Tosini, Lucas Leiva, "Simple Detection and Classification of Road Lanes based on Image Processing", International Journal of Information Technology and Computer Science(IJITCS), Vol.10, No.8, pp.38-45, 2018. DOI:10.5815/ijitcs.2018.08.06
Reference
[1]V.A. Olutayo, and A.A. Eludire, "Traffic Accident Analysis Using Decision Trees and Neural Networks", International Journal of Information Technology and Computer Science, vol.6, no.2, pp.22-28, 2014. doi: 10.5815/ijitcs.2014.02.03
[2]D. Vijayalaxmi and E. Rani, "Driver Fatigue Estimation Using Image Processing Technique", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.6, pp.66-72, 2016. doi: 10.5815/ijitcs.2016.06.09
[3]S. Godha, “On-road obstacle detection system for driver assistance,” Asia Pacific Journal of Engineering Science and Technology, 2017, vol. 3, no 1, p. 16-21. doi: 10.1109/IRDS.2002.1041361
[4]G. Liu, M. Zhou, L. Wang, H. Wang, X. Guo, “A blind spot detection and warning system based on millimeter wave radar for driver assistance,” Optik-International Journal for Light and Electron Optics, 2017, vol. 135, p. 353-365. doi: 10.1016/j.ijleo.2017.01.058
[5]A. Tawari, S. Sivaraman, M. Trivedi, T. Shannon, and M. Tippelhofer, “Looking-in and looking-out vision for urban intelligent assistance: Estimation of driver attentive state and dynamic surround for safe merging and braking,” Intelligent Vehicles Symposium Proceedings, 2014, pp. 115-120. doi: 10.1109/IVS.2014.6856600
[6]E. D. Dickmanns and B. D. Mysliwetz, “Recursive 3-D road and relative ego-state recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, no. 2, pp. 199–213, Feb. 1992. doi: 10.1109/34.121789
[7]F. Heimes and H.-H. Nagel, “Towards active machine-vision-based driver assistance for urban areas,” International Journal on Computer Vision, vol. 50, no. 1, pp. 5–34, Oct. 2002. doi: 10.1023/A:1020272819017
[8]B. Ma, S. Lakshmanan, and A. O. Hero, “Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion,” IEEE Trans. Intell. Transp. Syst., vol. 1, no. 5, pp. 135–147, Sep. 2000. doi: 10.1109/6979.892150
[9]S.P. Narote, P.N. Bhujbal, A.S. Narote, and D.M. Dhane, “A review of recent advances in lane detection and departure warning system,” Pattern Recognition, 2018, vol. 73, p. 216-234. doi: 10.1016/j.patcog.2017.08.014
[10]N. Apostoloff, and A. Zelinsky, "Robust vision based lane tracking using multiple cues and particle filtering." Intelligent Vehicles Symposium, 2003. Proceedings. IEEE. IEEE, 2003. doi: 10.1109/IVS.2003.1212973.
[11]M. Aly, "Real time detection of lane markers in urban streets," Intelligent Vehicles Symposium, 2008 IEEE. IEEE, 2008. doi; 10.1109/IVS.2008.4621152.
[12]Y. Wang, S. Dinggang Shen, and E. K. Teoh., "Lane detection using spline model." Pattern Recognition Letters, 2000, vol. 21, no 8, p. 677-689. doi: 10.1016/S0167-8655(00)00021-0
[13]L. Qing, N. Zheng, and H. Cheng. "Springrobot: A prototype autonomous vehicle and its algorithms for lane detection," IEEE Transactions on Intelligent Transportation Systems, 2004, vol. 5, no 4, p. 300-308. doi: 10.1109/TITS.2004.838220
[14]K. Kluge, and S. Lakshmanan, "A deformable-template approach to lane detection," Intelligent Vehicles' 95 Symposium, Proceedings of the. IEEE, 1995. doi: 10.1109/IVS.1995.52825
[15]W. Phueakjeen, N. Jindapetch, L. Kuburat, and N. Suvanvorn, “A study of the edge detection for road lane," Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2011 8th International Conference on. IEEE, 2011.doi: 10.1109/ECTICON.2011.5948010
[16]S. Zhou, Y. Jiang, J. Xi, J. Gong and G. Xiong. "A novel lane detection based on geometrical model and gabor filter," Intelligent vehicles symposium (IV), 2010 IEEE. IEEE, 2010. doi: 10.1109/IVS.2010.5548087
[17]Y. Wang, N. Dahnoun, and A. Achim. "A novel system for robust lane detection and tracking,” Signal Processing, 2012, vol. 92, no 2, p. 319-334. doi: doi>10.1016/j.sigpro.2011.07.019
[18]J. Heechul, M. Junggon, K. Junmo, "An efficient lane detection algorithm for lane departure detection," Intelligent Vehicles Symposium (IV), 2013 IEEE, vol.42, 23-26, June 2013. doi: 10.1109/IVS.2013.6629593
[19]J.G. Wang, C. Lin, S. Chen, “Applying fuzzy method to vision-based lane detection and departure warning system”, Expert Syst. Appl. 3 (1) (2010) 113–126. doi: doi:10.1016/j.eswa.2009.05.026
[20]M. Tosini, L. Leiva, “Detection of Lateral Borders on Unmarked Rural Roads”, Journal of Multidisciplinary Engineering Science and Technology (JMEST) ISSN: 2458-9403 Vol. 4 Issue 9, September - 2017
[21]T. Tan, S.Yin, P. Ouyang, L. Liu and S. Wei, "Efficient lane detection system based on monocular camera," 2015 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, 2015, pp. 202-203. doi: 10.1109/ICCE.2015.7066381
[22]J. He, H. Rong, J. Gong, and W. Huang, "A lane detection method for lane departure warning system." Optoelectronics and Image Processing (ICOIP), 2010 International Conference on. Vol. 1. IEEE, 2010. doi: 10.1109/ICOIP.2010.307
[23]A. Filonenko, D. C. Hernández, L. Kurnianggoro, D. Seo and K. H. Jo, "Real-time lane marking detection," Cybernetics (CYBCONF), 2015 IEEE 2nd International Conference on, Gdynia, 2015, pp. 125-128. doi: 10.1109/CYBConf.2015.7175918
[24]D.-J. Kang and M.-H. Jung, “Road lane segmentation using dynamic programming for active safety vehicles,” Pattern Recognit. Letters, vol. 24, no. 16, pp. 3177–3185, Dec. 2003. doi: 10.1016/j.patrec.2003.08.003
[25]H.J. Jang, B. Seung-Hae, and P. Soon-Yong, "Model-based curved lane detection using geometric relation between camera and road plane," Journal of Institute of Control, Robotics and Systems, 2015, vol. 21, no 2, p. 130-136
[26]M. Aly, “Real time detection of lane markers in urban streets,” IEEE Intelligent Vehicles Symposium, pp. 7-12, 2008. doi: 10.1109/IVS.2008.4621152
[27]C. Taylor, J. Košecká, R. Blasi, and J. Malik, “A comparative study of vision-based lateral control strategies for autonomous highway driving,” Int. J. Robot. Res., vol. 18, no. 5, pp. 442–453, May 1999. Doi: 10.1109/ROBOT.1998.680590
[28]S.C. Yi, C. Yeong-Chin and C. Ching-Haur, "A lane detection approach based on intelligent vision." Computers & Electrical Engineering, 2015, vol. 42, p. 23-29. doi: 10.1016/j.compeleceng.2015.01.002
[29]A. Katru and A. Kumar, "Improved Parallel Lane Detection Using Modified Additive Hough Transform", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.11, pp.10-17, 2016. doi: 10.5815/ijigsp.2016.11.02
[30]Q. Chen and W. Hong "A real-time lane detection algorithm based on a hyperbola-pair model," Intelligent Vehicles Symposium 2006, IEEE, 2006. doi: 10.1109/IVS.2006.1689679.
[31]H. Tan, Y. Zhou, Y. Zhu, D. Yao and K. Li, "A novel curve lane detection based on Improved River Flow and RANSAC," Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on , vol., no., pp.133-138, 8-11 Oct. 2014. doi: 10.1109/ITSC.2014.6957679
[32]H. Deusch, J. Wiest, S. Reuter, M. Szczot, M. Konrad, and K. Dietmayer, "A random finite set approach to multiple lane detection," Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on , vol., no., pp.270-275, 16-19 Sept. 2012. doi: 10.1109/ITSC.2012.6338772
[33]G. T. Shrivakshan, C. Chandrasekar, “A comparison of various edge detection techniques used in image processing,” IJCSI International Journal of Computer Science Issues, vol. 9, no 5, p. 272-276, 2012.
[34]Carnegie-Mellon-University, “CMU/VASC image database1997–2003, Available: http://vasc.ri.cmu.edu//idb/html/road/
[35]Araya, V., Espada, N., Tosini, M., & Leiva, L. (2016). “First approach to a framework for regional road-traffic accidents reduction system,” Journal of Software Engineering and Applications, vol. 9, no 05, pp. 175, May 2016.