A Computer Vision based Lane Detection Approach

Full Text (PDF, 1122KB), PP.27-34

Views: 0 Downloads: 0

Author(s)

Md. Rezwanul Haque 1,* Md. Milon Islam 1 Kazi Saeed Alam 1 Hasib Iqbal 1 Md. Ebrahim Shaik 2

1. Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh

2. Department of Civil Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2019.03.04

Received: 25 Oct. 2018 / Revised: 13 Dec. 2018 / Accepted: 17 Jan. 2019 / Published: 8 Mar. 2019

Index Terms

Lane Detection, Computer Vision, Gradient Thresholding, HLS Thresholding

Abstract

Automatic lane detection to help the driver is an issue considered for the advancement of Advanced Driver Assistance Systems (ADAS) and a high level of application frameworks because of its importance in drivers and passerby safety in vehicular streets. But still, now it is a most challenging problem because of some factors that are faced by lane detection systems like as vagueness of lane patterns, perspective consequence, low visibility of the lane lines, shadows, incomplete occlusions, brightness and light reflection. The proposed system detects the lane boundary lines using computer vision-based technologies. In this paper, we introduced a system that can efficiently identify the lane lines on the smooth road surface. Gradient and HLS thresholding are the central part to detect the lane lines. We have applied the Gradient and HLS thresholding to identify the lane line in binary images. The color lane is estimated by a sliding window search technique that visualizes the lanes. The performance of the proposed system is evaluated on the KITTI road dataset. The experimental results show that our proposed method detects the lane on the road surface accurately in several brightness conditions.

Cite This Paper

Md. Rezwanul Haque, Md. Milon Islam, Kazi Saeed Alam, Hasib Iqbal, Md. Ebrahim Shaik, " A Computer Vision based Lane Detection Approach", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.3, pp. 27-34, 2019. DOI: 10.5815/ijigsp.2019.03.04

Reference

[1]“Reported road casualties Great Britain 2009,” Stationery Office Dept. Transp., London, U.K., 2010.

[2]J. M. Clanton, D. M. Bevly, and A. S. Hodel, "A Low-Cost Solution for an Integrated Multisensor Lane Departure Warning System," in IEEE Transactions on Intelligent Transportation Systems, vol. 10, no. 1, pp. 47-59, Mar. 2009.

[3]Q. Li, L. Chen, M. Li, S. Shaw and A. Nüchter, "A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios," in IEEE Transactions on Vehicular Technology, vol. 63, no. 2, pp. 540-555, Feb. 2014.

[4]M. Aly, "Real-time detection of lane markers in urban streets," in Proc. IEEE Intelligent Vehicles Symposium, Eindhoven, pp. 7-12, 2008.

[5]H. Yoo, U. Yang, and K. Sohn, “Gradient-enhancing conversion for illumination-robust lane detection,” in IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1083–1094, Sep. 2013.

[6]J. Deng, J. Kim, H. Sin, and Y. Han, “Fast lane detection based on the B-Spline fitting,” International Journal of Research in Engineering and Technology, vol. 2, no. 4, pp. 134–137, 2013.

[7]Tsung-Ying Sun, Shang-Jeng Tsai, and V. Chan, "HSI color model based lane-marking detection," in Proc. IEEE Intelligent Transportation Systems Conference, Toronto, Ont., pp. 1168-1172, 2006.

[8]R. Ke, Z. Li, J. Tang, Z. Pan and Y. Wang, "Real-Time Traffic Flow Parameter stimation From UAV Video Based on Ensemble Classifier and Optical Flow," in IEEE Transactions on Intelligent Transportation Systems. no.99, pp. 1-11, Mar. 2018.

[9]H. Kong, J. Audibert and J. Ponce, "General Road Detection From a Single Image," in IEEE Transactions on Image Processing, vol. 19, no. 8, pp. 2211-2220, Aug. 2010. 

[10]P. Moghadam and J. F. Dong, "Road direction detection based on vanishing-point tracking," in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura,  pp. 1553-1560, 2012. 

[11]C. Lee and J. H. Moon, "Robust Lane Detection and Tracking for Real-Time Applications," in IEEE Transactions on Intelligent Transportation Systems, vol. no.99, pp.1-6, Feb. 2018.

[12]W. Song, Y. Yang, M. Fu, Y. Li and M. Wang, "Lane Detection and Classification for Forward Collision Warning System Based on Stereo Vision," in IEEE Sensors Journal, vol. 18, no. 12, pp. 5151-5163, Jun. 2018.

[13]C. Wu, L. Wang and K. Wang, "Ultra-low Complexity Block-based Lane Detection and Departure Warning System," in IEEE Transactions on Circuits and Systems for Video Technology, Feb. 2018.

[14]J. H. Yoo, S. Lee, S. Park and D. H. Kim, "A Robust Lane Detection Method Based on Vanishing Point Estimation Using the Relevance of Line Segments," in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 12, pp. 3254-3266, Dec. 2017.

[15]U. Ozgunalp, R. Fan, X. Ai and N. Dahnoun, "Multiple Lane Detection Algorithm Based on Novel Dense Vanishing Point Estimation," in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 3, pp. 621-632, March 2017.

[16]J. Piao and H. Shin, "Robust hypothesis generation method using binary blob analysis for multi-lane detection," in IET Image Processing, vol. 11, no. 12, pp. 1210-1218, Dec. 2017.

[17]S. Jung, J. Youn, and S. Sull, "Efficient Lane Detection Based on Spatiotemporal Images," in IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 1, pp. 289-295, Jan. 2016.

[18]A. Borkar, M. Hayes, and M. T. Smith, “A novel lane detection system with efficient ground truth generation,” in IEEE Transactions on Intelligent Transportation Systems vol. 13, no. 1, pp. 365–374, Mar. 2012.

[19]S. Kang, S. Lee, J. Hur and S. Seo, "Multi-lane detection based on accurate geometric lane estimation in highway scenarios," in Proc. IEEE Intelligent Vehicles Symposium, Dearborn, MI, pp. 221-226, 2014.

[20]J. Fritsch, T. Kühnl and A. Geiger, "A new performance measure and evaluation benchmark for road detection algorithms," in Proc. 16th International IEEE Conference on Intelligent Transportation Systems, pp. 1693-1700, 2013. 

[21]M. Sezgin, and B. Sankur, “Survey over image thresholding techniques and quantative performance evaluation,” Journal of Electronic Imaging, vol.13, no.1, pp.146–165, 2004.

[22]G. P. Balasubramanian, E. Saber, V. Misic, E. Peskin, and M. Shaw, “Unsupervised color image segmentation using a dynamic color gradient thresholding algorithm,” in Proc. SPIE Human Vis. Electron. Imag. XIII, pp. 680 61H-1–680 61H-9, Feb. 2008. 

[23]J. H. Yoo, D. Hwang, and K. Y. Moon, "Human body segmentation based on background estimation in modified HLS color space," in Proc. 9th WSEAS international conference on signal, speech and image processing (SSIP'09), pp. 152-155, 2009. 

[24]Z. Liu, S. Wang and X. Ding, "ROI perspective transform based road marking detection and recognition," in Proc. International Conference on Audio, Language and Image Processing, Shanghai,  pp. 841-846, 2012.