i-manager Publications

Lane Detection System to Avoid Accidents using Image Processing and Deep Learning

Shivani*, Vishnu Priya**, Yogapriya***, Bhagyalakshmi****, Vanshika*****

*-*****Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.

Periodicity:July - September'2021
DOI : https://doi.org/10.26634/jip.8.3.18193

Abstract

To reduce the frequency of road accidents, Lane detection system can be implemented in four wheelers to identify lane borders on the road and further prompt the driver if he switches and moves to erroneous lane markings. There are many algorithms used in this system namely, perspective, sliding window algorithms and many more, so collectively these can be called as a library. Lane boundaries are detected using a camera that captures the view of the road. The camera mounted on the top of the car takes images which serves as the basic input.

Keywords

Lane Detection System, Image Processing, Deep Learning, Perspective, Sliding Window Algorithms

How to Cite this Article?

Shivani, Priya, V., Yogapriya, Bhagyalakshmi, and Vanshika. (2021). Lane Detection System to Avoid Accidents using Image Processing and Deep Learning. i-manager's Journal on Image Processing, 8(3), 27-31. https://doi.org/10.26634/jip.8.3.18193

References

[1]. Borkar, A., Hayes, M., & Smith, M. T. (2011). A novel lane detection system with efficient ground truth generation. IEEE Transactions on Intelligent Transportation Systems, 13(1), 365-374.

[2]. Danti, A., & Hiremath, P. S. (2013). A Realtime Road Boundary Detection and Vehicle Detection for Indian Roads. International Journal of Applied Information Systems (IJAIS), 5(4), 25-35.

[3]. Deusch, H., Wiest, J., Reuter, S., Szczot, M., Konrad, M., & Dietmayer, K. (2012, September). A random finite set approach to multiple lane detection. In 2012, 15th International IEEE Conference on Intel l igent Transportation S y stems (pp. 270-275). IEEE.

[4]. Hur, J., Kang, S. N., & Seo, S. W. (2013, June). Multilane detection in urban driving environments using conditional random fields. In 2013, IEEE Intelligent Vehicles Symposium (IV) (pp. 1297-1302). IEEE.

[5]. Jung, H., Min, J., & Kim, J. (2013, June). An efficient lane detection algorithm for lane departure detection. In 2013, IEEE Intelligent Vehicles Symposium (IV) (pp. 976-981). IEEE.

[6]. Shen, Y., Bi, Y., Yang, Z., Liu, D., Liu, K., & Du, Y. (2021). Lane line detection and recognition based on dynamic ROI and modified firefly algorithm. International Journal of Intelligent Robotics and Applications, 5(2), 143-155.

[7]. Takahashi, A., Ninomiya, Y., Ohta, M., Nishida, M., & Yoshikawa, N. (2003). Image processing technology for rear view camera (1): Development of lane detection system. R&D Review of Toyota CRDL, 38(2), 31-36.

[8]. Tu, C., Van Wyk, B. J., Hamam, Y., Djouani, K., & Du, S. (2013). Vehicle position monitoring using Hough transform. IERI Procedia, 4, 316-322.

Lane Detection System to Avoid Accidents using Image Processing and Deep Learning

Abstract

Keywords

How to Cite this Article?

References

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