Lane Departure Warning based on Road Marking Detection using Mask Region-based Convolutional Neural Networks

Lane departure warning system (LDW) is a system where when the condition of the car being driven on the road and maneuvering out of the lane without the driver realizing it, there is a visual or audible warning that will allow the driver to correct the maneuver. In implementing this system, tracking is needed on road lane line markings. One way to detect road markings is to use a camera that is placed on the windshield in the middle of the car with various methods. In previous research, the method used was using image filters, namely Hough Transform and Canny Edge. This method is quite effective on straight road conditions and sunny conditions without any obstacles. The weakness is when the road conditions start to turn quite sharply and also in low light conditions. In this study, the proposed method for tracking road markings is Mask Regional-based Convolutional Neural Networks (Mask R-CNN). This method is the latest development of the existing image segmentation method. The tracking process is with input from the video camera and then pre-processing is carried out to reduce distortion and calibrate the incoming frame. After that, the detection process uses a Mask R-CNN and the output will be line fitting to get the middle point and the right and left lines. After that the last process is Lane Departure Warning where the midpoint when approaching the line will give a warning to the driver.