Real-time Lane Detection Based on Image Edge Feature and Hough Transform

Fallah, A.; Soliemani, A.; Khosravi, H.

doi:10.22061/jecei.2021.7659.418

Document Type : Original Research Paper

Authors

Department of Electronics, Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran

https://doi.org/10.22061/jecei.2021.7659.418

Abstract

Background and Objectives: Lane detection systems are an important part of safe and secure driving by alerting the driver in the event of deviations from the main lane. Lane detection can also save the lifes of car occupants if they deviate from the road due to driver distraction.
Methods: In this paper, a real-time and illumination invariant lane detection method on high-speed video images is presented in three steps. In the first step, the necessary preprocessing including noise removal, image conversion from RGB colour to grey and the binarizing input image is done. Then, a polygon area as the region of interest is chosen in front of the vehicle to increase the processing speed. Finally, edges of the image in the region of interest are obtained with edge detection algorithm and then lanes on both sides of the vehicle are identified by using the Hough transform.
Results: The implementation of the proposed method was performed on the IROADS database. The proposed method works well under different daylight conditions, such as sunny, snowy or rainy days and inside the tunnels. Implementation results show that the proposed algorithm has an average processing time of 28 milliseconds per frame and detection accuracy of 96.78%.
Conclusion: In this paper a straightforward method to identify road lines using the edge feature is described on high-speed video images.

======================================================================================================
Copyrights
©2021 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.
======================================================================================================

Keywords

20.1001.1.23223952.2021.9.2.7.5

Main Subjects

Image Processing

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

Publisher’s Note

JECEI Publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Publisher

Shahid Rajaee Teacher Training University

References

[1] J. R. Mani, N.D. Gangadhar, V.K. Reddy, “A real-time video processing based driver assist system,” SASTech, 9: 9-16, 2010.

[2] F. You, R. Zhang, L. Zhong, H. Wang, J. Xu, “Lane detection algorithm for night-time digital image based on distribution feature of boundary pixels,” J. Opt. Soc. Korea, 17: 188-199, 2013.

[3] A. Saha, D.D. Roy, T. Alam, K. Deb, “Automated road lane detection for intelligent vehicles,” Global J. Comput. Sci. Tech., 12(6): 1-6, 2012.

[4] M. Rashid, M.A. Khan, M. Alhaisoni, S.H. Wang, S.R. Naqvi, A. Rehman, T. Saba, “A sustainable deep learning framework for object recognition using multi-layers deep features fusion and selection,” Sustainability, 12(12): 5037, 2020.

[5] N. Hussain, M.A. Khan, M. Sharif, S.A. Khan, A.A. Albesher, T. Saba, A. Armaghan, “A deep neural network and classical features based scheme for objects recognition: an application for machine inspection,” Multimed. Tool. Appl., 2020.

[6] M. Rashid, M.A. Khan, M. Sharif, M. Raza, M.M. Sarfraz, F. Afza, “Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features,” Multimed. Tool. Appl., 78: 15751–15777, 2019.

[7] A.D. Forrest, M. Konca, “Autonomous Cars & Society,” Worcester Polytechnic Institute, 2007.‏

[8] K.H. Lee, J.H. Bak, C.H. Lee, “Study on active steering control of vehicle for safe driving in highway with GPS information,” in Proc. IEEE Intelligent Vehicles Symposium: 554-557, 2012.

[9] M.A. Khan, K. Javed, S.A. Khan, T. Saba, U. Habib, J.A. Khan, A.A. Abbasi, “Human action recognition using fusion of multiview and deep features: an application to video surveillance,” Multimed. Tool. Appl., 2020.‏

[10] M. Bertozzi, A. Broggi, “GOLD: A parallel real-time stereo vision system for generic obstacle and lane detection,” IEEE Trans. Image Process., 7: 62-81, 1998.

[11] H. Arshad, M.A. Khan, M.I. Sharif, M. Yasmin, J.M.R. Tavares, Y.D. Zhang, S.C. Satapathy, “A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition,” Expert Syst., 2020.‏

[12] M. Bertozzi, A. Broggi, “Vision-based vehicle guidance,” Computer, 30: 49-55, 1997.‏

[13] N. Naheed, M. Shaheen, S.A. Khan, M. Alawairdhi, M.A. Khan, “Importance of features selection, attributes selection, challenges and future directions for medical imaging data: a review,” CMES-Comp. Model. Eng. Sci. CMES, 125: 314-344, 2020.‏

[14] P.N. Bhujbal, S.P. Narote, “Lane departure warning system based on Hough transform and Euclidean distance,” in Proc. 2015 Third International Conference on Image Information Processing (ICIIP): 370-373, 2015.

[15] J. Son, H. Yoo, S. Kim, K. Sohn, “Real-time illumination invariant lane detection for lane departure warning system,” Expert Syst. Appl., 42: 1816-1824, 2015.

[16] T.T. Duong, C.C. Pham, T.H. P. Tran, T.P. Nguyen, J.W. Jeon, “Near real-time ego-lane detection in highway and urban streets,” in Proc. 2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia): 1-4, 2016.

[17] R. Jiang, M. Terauchi, R. Klette, S. Wang, T. Vaudrey, “Low-level image processing for lane detection and tracking,” in Proc International Conference on Arts and Technology: 190-197, 2009.

[18] A. Mehmood, M.A. Khan, M. Sharif, S.A. Khan, M. Shaheen, T. Saba, N. Riaz, I. Ashraf, “Prosperous human gait recognition: An end-to-end system based on pre-trained CNN features selection,” Multimed. Tool. Appl., 2020.‏

[19] O.G. Lotfy, A.A. Kassem, E.M. Nassief, H.A. Ali, M.R. Ayoub, M.A. El-Moursy, M.M. Farag, “Lane departure warning tracking system based on score mechanism,” in Proc. IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS): pp. 1-4, 2016.

[20] U. Ozgunalp, S. Kaymak, “Lane detection by estimating and using restricted search space in Hough domain,” Procedia Comput. Sci. 120: 148-155., 2017.

[21] M. Li, Y. Li, M. Jiang, “Lane detection based on connection of various feature extraction methods,” Adv. Multimedia, 2018: 1-14, 2018.‏

[22] D.C. Andrade, F. Bueno, F.R. Franco, R.A. Silva, J.H.Z. Neme, E. Margraf, W.T. Omoto, F.A. Farinelli, A.M. Tusset, S. Okida, M.M.D. Santos, “A novel strategy for road lane detection and
tracking based on a vehicle’s forward monocular camera,” IEEE Trans. Intell. Transp. Syst., 20: 1497-1507, 2018.

[23] M. Fakhredanesh, S. Roostaie, “Action change detection in video based on HOG,” J. Electr. Comput. Eng. Innovations (JECEI), 8(1); 135-144, 2020.

[24] M.A. Khan, Y.D. Zhang, S.A. Khan, M. Attique, A. Rehman, S. Seo, “A resource conscious human action recognition framework using 26-layered deep convolutional neural network,” Multimed. Tool. Appl., 2020.‏

[25] Y. Sun, J. Li, Z. Sun, “Multi-stage hough space calculation for lane markings detection via IMU and vision fusion,” Sensors, 19(10): 1-14, 2019.‏

[26] M. Taheri, M. Rastgarpour, A. Koochari, “A novel method for medical image segmentation based on convolutional neural networks with SGD optimization,” J. Electr. Comput. Eng. Innovations (JECEI), 9(1): 37-46, 2021.

[27] H. Arshad, M.A. Khan, M. Sharif, M. Yasmin, M.Y. Javed, “Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution,” Int. J. Mach. Learn. Cybern., 10: 3601–3618, 2019.‏

[28] M. Marzougui, A. Alasiry, Y. Kortli, J. Baili, “A lane tracking method based on progressive probabilistic hough transform,” IEEE Access, 8: 84893-84905, 2020.‏

[29] H. Xu, H. Li, “Study on a robust approach of lane departure warning algorithm,” presented at the 2nd International Conference on Signal Processing Systems, Dalian, China, 2010.

[30] N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern.: Syst., 9: 62-66., 1979.

[31] W. Rong, Z. Li, W. Zhang, L. Sun, “An improved CANNY edge detection algorithm,” in Proc. 2014 IEEE International Conference on Mechatronics and Automation: 577-582, 2014.

[32] M. Rezaei, M. Terauchi, “Vehicle detection based on multi-feature clues and Dempster-Shafer fusion theory,” in Proc. 2013 Pacific-Rim Symposium on Image and Video Technology: 60-72, 2013.

[33] J. Cao, C. Song, S. Song, F. Xiao, S. Peng, “Lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments,” Sensors, 19(14): 1-21, 2019.‏

[34] J. Hu, S. Xiong, Y. Sun, J. Zha, C. Fu, “Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI),” Appl. Sci., 10(7): 1-22, 2020.‏

LETTERS TO EDITOR

Journal of Electrical and Computer Engineering Innovations (JECEI) welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in JECEI should be sent to the editorial office of JECEI within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.

[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.

[3] Letters can be no more than 300 words in length.

[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.

[5] Anonymous letters will not be considered.

[6] Letter writers must include their city and state of residence or work.

[7] Letters will be edited for clarity and length.

Name *

Email Address *

Affiliation *

Comments *

Security Code *

Journal of Electrical and Computer Engineering Innovations (JECEI)

Real-time Lane Detection Based on Image Edge Feature and Hough Transform

References

References

Send comment about this article

Volume 9, Issue 2
July 2021
Pages 193-202

Real-time Lane Detection Based on Image Edge Feature and Hough Transform

References

References

Send comment about this article

Volume 9, Issue 2July 2021Pages 193-202

Volume 9, Issue 2
July 2021
Pages 193-202