Citation

Wong, Eng Kiong and Em, Poh Ping and Hossen, Jakir and Imaduddin, Fitrian and Sabino, Ubaidillah (2019) Experimental of Vision-based Lane Markings Segmentation Methods in Lane Detection Application. Journal of Engineering Science and Technology Review, 12 (1). pp. 185-195. ISSN 1791-9320

Text 211.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Lane departure collisions have contributed into the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Hence, a vision-based lane detection framework (VBLD) is proposed to detect lane markings on the road for unindented lane departure. The proposed VBLD framework is composed of colour space conversion, region of interest, lane marking segmentation, Hough transformation and peak detection, reverse Hough transformation, and draw detected lines on original image. Besides, finite impulse response saturation autothreshold (FIRSA) lane marking segmentation method is also proposed for lane edges extraction. For performance evaluation on the proposed VBLD framework and proposed FIRSA lane markings segmentation method, real-life datasets of road footages are collected using an instrumented vehicle. The outputs of lane detection frames from Clip #1, #2, #3, and #4 involving variety of road conditions are evaluated using detection rate, false positive rate, and false negative rate assessment metrics where the number of frames are manually counted using visual inspection. Experimental results have shown the evidences of the proposed VBLD framework using proposed FIRSA lane markings segmentation method obtained satisfactory lane detection results compared to benchmark lane marking segmentation methods

Item Type:	Article
Uncontrolled Keywords:	Lane Boundary Detection; Lane Markings Segmentation; False Positive Rate; False Negative Rate
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL1-484 Motor vehicles. Cycles
Divisions:	Faculty of Engineering and Technology (FET) Faculty of Engineering (FOE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	24 Feb 2022 04:16
Last Modified:	24 Feb 2022 04:16
URII:	http://shdl.mmu.edu.my/id/eprint/9187

Downloads

Edit (login required)