Lane marking detection based on adaptive threshold segmentation and road classification
A new robust lane marking detection algorithm for monocular vision is proposed. It is designed for the urban roads with disturbances and with the weak lane markings. The primary contribution of the paper is that it supplies a robust adaptive method of image segmentation, which employs jointly prior knowledge, statistical information and the special geometrical features of lane markings in the bird's-eye view. This method can eliminate many disturbances while keep points of lane markings effectively. Road classification can help us extract more accurate and simple characteristics of lane markings, so the second contribution of the paper is that it uses the row information of image to classify road conditions into three kinds and uses different strategies to complete lane marking detection. The experimental results have shown the high performance of our algorithm in various road scenes.