LANE DETECTION USING CANNY EDGE DETECTION AND HOUGH TRANSFORM ON RASPBERRY PI

Main Article Content

Roshan Jahan
Preetam Suman
Deepak Kumar Singh

Abstract

Many of the researchers are developing an efficient technology for automated alarm system while crossing through lanes on roads. Lane detection through image processing is one of the major tasks. A camera can be mounted in the front of vehicle to take real time images; and a fast processor can be use to automatically detect lanes according to image processing algorithms. This paper is based on algorithm development using canny edge detector and Houge transform. The algorithm is implemented on OpenCV and Python. The Raspberry Pi is used for real time processing of image. Algorithm and it’s implementation is discussed in this paper.

 

Downloads

Download data is not yet available.

Article Details

Section
Articles

References

http://indianexpress.com/article/india/road-accidents-in-india-2016-17-deaths-on-roads-every-hour-chennai-and-delhi-most-dangerous-4837832/

http://www.who.int/violence_injury_prevention/road_safety_status/2015/en/

C. B. Wu, L. H. Wang and K. C. Wang, "Ultra-low Complexity Block-based Lane Detection and Departure Warning System," in IEEE Transactions on Circuits and Systems for Video Technology, vol. PP, no. 99, pp. 1-1.

Q. Gao, Y. Feng and L. Wang, "A real-time lane detection and tracking algorithm," 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China, 2017, pp. 1230-1234.

V. Q. Nguyen, C. Seo, H. Kim and K. Boo, "A study on detection method of vehicle based on lane detection for a driver assistance system using a camera on highway," 2017 11th Asian Control Conference (ASCC), Gold Coast, Australia, 2017, pp. 424-429.

Y. Xu, X. Shan, B. Y. Chen, C. Chi, Z. F. Lu and Y. Q. Wang, "A lane detection method combined fuzzy control with RANSAC algorithm," 2017 7th International Conference on Power Electronics Systems and Applications - Smart Mobility, Power Transfer & Security (PESA), Hong Kong, Hong Kong, 2017, pp. 1-6.

M. Kodeeswari and P. Daniel, "Lane line detection in real time based on morphological operations for driver assistance system," 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, 2017, pp. 316-320.

Z. Yang, H. Li, S. Ali, Y. Ao and S. Guo, "Lane Detection by Combining Trajectory Clustering and Curve Complexity Computing in Urban Environments," 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, 2017, pp. 240-246.

H. Li, Z. Zhang, X. Zhao and M. He, "Two-stage hough transform algorithm for lane detection system based on TMS320DM6437," 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, 2017, pp. 1-5.

Vipul H. Mistry, Dr. Ramji Makwana, “Survey: Vision based Road Detection Techniques†(IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4741-4747

Graovac, Stevica; Goma, Ahmed, “Detection of road borders based on texture classification,†International Journal of Advanced Robotic Systems . Dec2012, Vol. 9, pp.1-12.

Stoklasa, Roman a Petr Matula, “Road Detection Using Similarity Search,†In Roland Stelzer and Karim Jafarmadar. 2nd International Conference on Robotics in Education, Vienna: Neuveden, 2011, pp. 95 -102.

Falola, O., Osunmakinde, I. O. and Bagula A, “Supporting Drivable Region Detection by Minimising Salient Pixels Generated Through Robot Sensors,†Proceedings of the Twenty-First Annual Conference of the Pattern Recognition Association of South Africa (PRASA), MIAPR, 2010, pp. 87-92.

Alvarez, J.M.A.; Ĺopez, A.M., "Road Detection Based on Illuminant Invariance," Intelligent Transportation Systems, IEEE Transactions on , vol.12, no.1, March 2011, pp.184,193

Erke Shang, Xiangjing An, Jian Li, Lei Ye and Hangen He, “Robust Unstructured Road Detection: The Importance of Contextual Information,†Int J Adv Robot Syst, 2013, 10:179.

Dezhi Gao, Wei Li, Jianmin Duan, Banggui Zheng, "A practical method of road detection for intelligent vehicle," Automation and Logistics, 2009. ICAL '09. IEEE International Conference on , vol., no., 5-7 Aug. 2009, pp.980,985.

H. Kong, J.Y. Audibert, and J. Ponce, “General road detection from a single image. Image Processing,†IEEE Transactions on, 19(8), 2010, pp. 2211–2220.

Tsung-Ying Sun; Shang-Jeng Tsai; Chan, V., "HSI color model based lane-marking detection," Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE , vol., no., 17-20 Sept. 2006, pp.1168-1172.

K.-Y. Chiu and S.-F. Lin, “Lane detection using color-based segmentation,†in Proc. IEEE Intelligent Vehicles Symp., 2005, pp. 706–711.

Olusanya Y. Agunbiade, Tranos Zuva, Awosejo O. Johnson, Keneilwe Zuva, “Enhancement performance of road recognition system of autonomous robots in shadow scenario,â€, An International Journal (SIPIJ) Vol.4, No.6, December 2013.

B. C. Maheshwari, J. Burns, M. Blott and G. Gambardella, "Implementation of a scalable real time canny edge detector on programmable SOC," 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA), Ras Al Khaimah, 2017, pp. 1-5.

A. Salman, A. Semwal, U. Bhatt and V. M. Thakkar, "Leaf classification and identification using Canny Edge Detector and SVM classifier," 2017 International Conference on Inventive Systems and Control (ICISC), Coimbatore, 2017, pp. 1-4.

D. G. Bailey, "Hough transform line reconstruction on FPGA using back-projection," 2017 International Conference on Field Programmable Technology (ICFPT), Melbourne, Australia, 2017, pp. 283-286.

S. Seth and K. Palodhi, "An efficient algorithm for segregation of white and red blood cells based on modified hough transform," 2017 IEEE Calcutta Conference (CALCON), Kolkata, India, 2017, pp. 465-468.

P. D. Wulaning Ayu and G. A. Pradipta, "Egg's diameter detection using fuzzy C-means and Iterative Random Hough Transform," 2017 1st International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia, 2017, pp. 53-58.

G. A. Pradipta and P. D. Wulaning Ayu, "Fetal weight prediction based on ultrasound image using fuzzy C means clustering and Itterative Random Hough Transform," 2017 1st International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia, 2017, pp. 71-76.

https://opensource.com/resources/raspberry-pi