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Innovative Infrastructure Solutions
Published in: Innovative Infrastructure Solutions 5/2022

01-10-2022 | Technical paper

An efficient approach for highway lane detection based on the Hough transform and Kalman filter

Authors: Sunil Kumar, Manisha Jailia, Sudeep Varshney

Published in: Innovative Infrastructure Solutions | Issue 5/2022

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Abstract

The advances in lane detection technologies and computer vision enabled the evolution of lane-keeping systems, driver assistance and lane departure warning in traffic management for road safety. However, it is very challenging to identify and track the lane lines due to improper marking of lane lines and blind turns on the road. The present work proposes effective and efficient vision-based real-time lane markings and tracking lane detection methods for straight and curved lane lines. That can adapt to various environmental conditions. Further, the Hough transform optimization is performed to identify lane lines accurately, and the Kalman filter is employed to track lane lines detected in the ROI by the Sobel operator. The proposed approaches show their significance by achieving real-time response and high accuracy for a vehicle in lane change assistant system on highways. While comparing, the proposed methods show better results in terms of detection rate and processing time for straight lanes and detection accuracy, precision, recall and F1-Score for the curved lanes. The result of processing time and accuracy rate for straight lane detection is 16.7 fps, 96.3%, respectively, and the accuracy, precision, recall and F1-scores for curved lane detection are 97.74%, 98.15%, 97.35% and 97.75% in videos, respectively.
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Literature
1.
Dinakaran K, Sagayaraj AS, Kabilesh SK, Mani T, Anandkumar A, Chandrasekaran G (2020) Advanced lane detection technique for structural highway based on computer vision algorithm. Mater Today: Proceed 45:2073–2081
2.
Nguyen V, Kim H, Jun S, Boo K (2018) A study on real-time detection method of lane and vehicle for lane change assistant system using vision system on highway. Eng Sci Technol Int j 21(5):822–833
3.
Dorj B, Hossain S, Lee DJ (2020) Highly curved lane detection algorithms based on kalman filter. Appl Sci 10(7):2372CrossRef
4.
Gayko JE (2012) Lane departure and lane keeping. Handbook of Intelligent Vehicles. Springer, London, pp 689–708CrossRef
5.
Visvikis C et al (2008) Study on lane departure warning and lane change assistant systems. Trans Res Lab Proj Rpt PPR 374:1–13
6.
Muthalagu R, Bolimera A, Kalaichelvi V (2021) Vehicle lane markings segmentation and keypoint determination using deep convolutional neural networks. Multimed Tools Appl 80(7):11201–11215CrossRef
7.
Sugawara T, Altmannshofer H, Kakegawa S (2017) Applications of road edge information for advanced driver assistance systems and autonomous driving. Advanced microsystems for automotive applications. Springer, London, pp 71–86
8.
9.
Amaradi P, Sriramoju N, Li Dang GS, Tewolde, Kwon J (2016) Lane following and obstacle detection techniques in autonomous driving vehicles. In: IEEE International Conference on Electro Information Technology (EIT), Grand Forks, ND, pp 0674–0679
10.
11.
Talib ML, Rui X, Ghazali KH, Mohd. Zainudin N, Ramli S, (2013) Comparison of edge detection technique for lane analysis by improved hough transform, vol 8237. Springer, Cham, pp 176–183
12.
Yan X, Li Y (2017) A method of lane edge detection based on Canny algorithm. In: 2017 Chinese Automation Congress (CAC). pp 2120–2124
13.
Canny J (1986) A computational approach to edge detection [J]. IEEE Trans Pattern Anal Mach Intell 8(6):679–698CrossRef
14.
Richard OD, Peter EH (1972) Use of the Hough transformation to detect lines and curves in picture. Graphics Image Process 15(1):11–15
15.
Ye YY, Hao XL, Chen HJ (2018) Lane detection method based on lane structural analysis and CNNs. IET Intel Transport Syst 12(6):513–520CrossRef
16.
Kuk JG, An JH, Ki H, Cho (2010) Fast lane detection and tracking based on Hough transform with reduced memory requirement. In IEEE conference on intelligent transportation systems. 1344–1349
17.
Hsiao PY, Yeh CW (2006) A portable real-time lane departure warning system based on embedded calculating technique. In: 2006 IE 63rd Vehicular Technology Conference. 6:2982–2986
18.
Dinakaran K, Sagayaraj AS, Kabilesh SK, Mani T, Anandkumar A, Chandrasekaran G (2021) Advanced lane detection technique for structural highway based on computer vision algorithm. Mater Today: Proceed 45:2073–2081
19.
Ding Y, Xu Z, Zhang Y, Sun K (2017) Fast lane detection based on bird’s eye view and improved random sample consensus algorithm. Multimed Tools Appl 76(21):22979–22998CrossRef
20.
Collado, Hilario C, Escalera A, Armingol JM (2005) Detection and classification of road lanes with a frequency analysis. IEEE Intelligent Vehicles Symposium. Nevada, USA, 7883
21.
Wang Y, Teoh E, Shen D (2004) Lane detection and tracking using b-snake. Image Vis Comput 22(4):269–280CrossRef
22.
King HL, Kah PS, Li-Minn A (2009) Lane detection and kalman-based linear-parabolic lane tracking. In: Proceedings of IEEE international conference on intelligent human-machine systems and cybernetics. IEEE, Hangzhou, p 351–354
23.
Lu WN, Zheng YC, Ma YQ, et al. (2008) An integrated approach to recognition of lane marking and road boundary. In: Proceedings of International Workshop on Knowledge Discovery and Data Mining. University of Adelaide, Australia, p 649–653
24.
Chen Q, Wang H (2006) A real-time lane detection algorithm based on a hyperbola-pair model. IEEE Intelligent Vehicles Symposium. IEEE, Tokyo, p 510–515
25.
Teo TY, Sutopo R, Lim JMY, Wong K (2021) Innovative lane detection method to increase the accuracy of lane departure warning system. Multimed Tools Appl 80(2):2063–2080CrossRef
26.
Dorj B, Lee DJ (2016) A precise lane detection algorithm based on top view image transformation and least-square approaches. J Sens 6:2016
27.
28.
Chen C, Tang L, Wang Y, Qian Q (2019) Study of the lane recognition in haze based on kalman filter. In: 2019 International conference on artificial intelligence and advanced manufacturing (AIAM). pp. 479–483
29.
30.
Pan X, Shi J, Luo P, Wang X, Tang X (2018) Spatial cnn for traffic scene understanding. In: 32nd AAAI Conf Artif Intell. 1–8
31.
Yu F, Chen H, Wang X, Xian W, Chen Y, Liu F, Madhavan V, Darrell T (2020) BDD100K: a diverse driving dataset for heterogeneous multitask learning. In: IEEE Conf Int’l Conf Comput Vis Pattern Recognit. 2636–2645
32.
Xing Y, Lv C, Chen L, Wang H, Wang H, Cao D, Velenis E, Wang FY (2018) Advances in vision-based lane detection: Algorithms, integration, assessment, and perspectives on ACP-based parallel vision. IEEE/CAA J Autom Sin 5:645–661CrossRef
33.
Ko Y, Jun J, Ko D, Jeon M, Key points estimation and point instance segmentation approach for lane detection. arXiv 2020, arXiv:​2002.​06604
34.
Liang D, Guo YC, Zhang SK, Mu TJ, Huang X (2020) Lane Detection: a survey with new results. J Comput Sci Technol 35:493–505CrossRef
35.
Yang W, Zhang X, Lei Q, Shen D, Huang Y (2020) lane position detection based on long short-term memory (LSTM). Sensors 20:3115CrossRef
36.
Zou Q, Jiang H, Dai Q, Yue Y, Chen L, Wang Q (2020) Robust lane detection from continuous driving scenes using deep neural networks. IEEE Trans Veh Technol 69:41–54CrossRef
37.
Jiang L, Li J, Ai W (2019) Lane line detection optimization algorithm based on improved Hough transform and r-least squares with dual removal. In 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Vol. 1, pp. 186–190. IEEE
38.
Dubey A, Bhurchandi KM (2015) Robust and real time detection of curvy lanes (curves) with desired slopes for driving assistance and autonomous vehicles. arXiv preprint arXiv:​1501.​03124
39.
Ma C, Mao L, Zhang Y, Xie M (2010, July) Lane detection using heuristic search methods based on color clustering. In 2010 international conference on communications, circuits and systems (ICCCAS) pp. 368–372. IEEE
40.
Srivastava S, Lumb M, Singal R (2014) Improved lane detection using hybrid median filter and modified Hough transform. Int J Adv Res Comput Sci Softw Eng 4(1):30–37
41.
Wang J, Ma H, Zhang X, Liu X (2018) Detection of lane lines on both sides of road based on monocular camera. In 2018 IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 1134–1139). IEEE
42.
Li Y, Zhang W, Ji X, Ren C, Wu J (2019) Research on lane a compensation method based on multi-sensor fusion. Sensors 19(7):1584CrossRef
43.
Haselhoff A, Kummert A (2009) 2D line filters for vision-based lane detection and tracking. In 2009 International Workshop on Multidimensional (nD) Systems pp. 1–5. IEEE
44.
Piao J, Shin H (2017) Robust hypothesis generation method using binary blob analysis for multi-lane detection. IET Image Proc 11(12):1210–1218CrossRef
45.
Salarpour A, Salarpour A, Fathi M, Dezfoulian M (2011) Vehicle tracking using Kalman filter and features. Sign Image Process 2(2):1–3
46.
Wu Y, Chen Z (2016) A detection method of road traffic sign based on inverse perspective transform. In 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS). pp. 293–296. IEEE
47.
Jung, C. R., & Kelber, C. R. (2004, June). A lane departure warning system based on a linear-parabolic lane model. In IEEE Intelligent Vehicles Symposium, 2004. pp. 891–895. IEEE
48.
Lee, D. H., & Liu, J. L. (2021). End-to-end deep learning of lane detection and path prediction for real-time autonomous driving. arXiv preprint arXiv:​2102.​04738
49.
Lee, M., Lee, J., Lee, D., Kim, W., Hwang, S., & Lee, S. (2021). Robust lane detection via expanded self-attention. arXiv preprint arXiv:​2102.​07037
50.
Muthalagu R, Bolimera A, Kalaichelvi V (2020) Lane detection technique based on perspective transformation and histogram analysis for self-driving cars. Comput Electr Eng 85:106653CrossRef
51.
Li M, Li Y, Jiang M (2018) Lane detection based on connection of various feature extraction methods. Advances in Multimedia, 2018
52.
Zheng F, Luo S, Song K, Yan CW, Wang MC (2018) Improved lane line detection algorithm based on Hough transform. Pattern Recognit Image Anal 28(2):254–260CrossRef
53.
Wang JG, Lin CJ, Chen SM (2010) Applying fuzzy method to vision-based lane detection and departure warning system. Expert Syst Appl 37(1):113–126CrossRef
54.
Son J, Yoo H, Kim S, Sohn K (2015) Real-time illumination invariant lane detection for lane departure warning system. Expert Syst Appl 42(4):1816–1824CrossRef
55.
Zhang X, Zhu X (2019) Autonomous path tracking control of intelligent electric vehicles based on lane detection and optimal preview method. Expert Syst Appl 121:38–48CrossRef
56.
Kim KB, Song DH (2017) Real time road lane detection with RANSAC and HSV Color transformation. J Inform Comm Convergence Eng 15(3):187–192
57.
Mammeri A, Boukerche A, Lu G (2014) Lane detection and tracking system based on the MSER algorithm, hough transform and kalman filter. In Proceedings of the 17th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems. pp. 259–266
58.
Huang Y, Li Y, Hu X, Ci W (2018) Lane detection based on inverse perspective transformation and Kalman filter. KSII Trans Internet Inform Syst (TIIS) 12(2):643–661
59.
Yan X, Li Y (2017) A method of lane edge detection based on Canny algorithm. In 2017 Chinese Automation Congress (CAC). pp. 2120–2124. IEEE
60.
Hoang TM, Hong HG, Vokhidov H, Park KR (2016) Road lane detection by discriminating dashed and solid road lanes using a visible light camera sensor. Sensors 16(8):1313CrossRef
61.
Talib ML, Rui X, Ghazali KH, Zainudin NM, Ramli S (2013) Comparison of Edge Detection Technique for Lane Analysis by Improved Hough Transform. In: International Visual Informatics Conference. pp. 176–183. Springer: Cham
62.
Lee JW, Yi UK (2005) A lane-departure identification based on LBPE, Hough transform, and linear regression. Comput Vis Image Underst 99(3):359–383CrossRef
63.
Olson CF (1999) Constrained Hough transforms for curve detection. Comput Vis Image Underst 73(3):329–345CrossRef
64.
Cao J, Song C, Song S, Xiao F, Peng S (2019) Lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments. Sensors 19(14):3166CrossRef
65.
Lim KH, Seng KP, Ang LM, Chin SW (2009) Lane detection and Kalman-based linear-parabolic lane tracking. In 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics. Vol. 2, pp. 351–354. IEEE
66.
Wang Z, Li X, Jiang Y, Shao Q, Liu Q, Chen B, Huang D (2015) swDMR: a sliding window approach to identify differentially methylated regions based on whole genome bisulfite sequencing. PLoS ONE 10(7):e0132866CrossRef
67.
Yi SC, Chen YC, Chang CH (2015) A lane detection approach based on intelligent vision. Comput Electr Eng 42:23–29CrossRef
68.
Wu BF, Huang HY, Chen CJ, Chen YH, Chang CW, Chen YL (2013) A vision-based blind spot warning system for daytime and nighttime driver assistance. Comput Electr Eng 39(3):846–862CrossRef
Metadata
Title
An efficient approach for highway lane detection based on the Hough transform and Kalman filter
Authors
Sunil Kumar
Manisha Jailia
Sudeep Varshney
Publication date
01-10-2022
Publisher
Springer International Publishing
Published in
Innovative Infrastructure Solutions / Issue 5/2022
Print ISSN: 2364-4176
Electronic ISSN: 2364-4184
DOI
https://doi.org/10.1007/s41062-022-00887-9

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