A high definition map lane line detection and extraction method based on UAV images
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Abstract:
High definition map is an essential infrastructure to realize automatic driving technology, and lane line is an important part of lane level road network of high definition map. Currently, lane detection of high definition map is mostly based on the data of vehicle camera, which is low efficient due to limited imaging range and need for perspective transformation and multiple stitching. In this paper, based on UAV aerial images, U-Net network is used to identify road areas and filter noise in non-road areas. HSL color transform and Sobel operator are used to calculate lane color and edge gradient features respectively. Otsu algorithm is used to automatically determine feature segmentation threshold to obtain binary lane feature map. Local maximum algorithm is used to determine the initial position of sliding window. Finally, lane lines are fitted by sliding window algorithm and polynomial detection. The experimental results show that with certain detection accuracy, the detection length of a single lane line exceeds 100 m, and the road detection efficiency reaches 25.2 m/s. Compared with the lane line detection algorithms based on vehicle-mounted camera data, the proposed method is obviously more efficient.
