An Efficient Method for Lane-Mark Extraction in Complex Conditions

Lane-mark detection is one of the most important parts in intelligent transportation systems (ITS). In this paper, we propose a lane-mark detection system which can overcome a lot of difficult situations, such as bad weather conditions, shadow effect, or road sign on the road. After the region of interest (ROI) of a road image is determined, we apply the Canny edge detector to investigate boundaries. In order to remove the noise edges, we divide the boundary image into sub-images to calculate local edge-orientation of each block and remove the edge with abnormal orientation. In this step, we produce a table to store the blocks which satisfy the assumption of lane-mark edge-orientation and use this information as an adaptive ROI of lane-marks. We propose the edge-pair scanning method to verify the edges which belong to lane-marks by using the relationship of adjacent edges of lane-marks and the width between these two edges. In the local adaptive threshold finding method, we also divide the image into sub-images and apply the feature that road lane-marks are always painted with high contrast colors with the road surface. Then, we use multi-adaptive thresholding method for each block. The system can work robustly under the situation that different parts of the image have different contrast for lane-marks. After eliminate the noise edges, we apply Hough Transform to fit the lane-marks as straight line models. The experiment results show that the proposed method can detect the lane-marks in real-time for various different environments.

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