Experimental of Vision-based Lane Markings Segmentation Methods in Lane Detection Application

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.