Abstract
To improve the accuracy of lane detection in complex scenarios, an adaptive lane feature learning algorithm which can automatically learn the features of a lane in various scenarios is proposed. First, a two-stage learning network based on the YOLO v3 (You Only Look Once, v3) is constructed. The structural parameters of the YOLO v3 algorithm are modified to make it more suitable for lane detection. To improve the training efficiency, a method for automatic generation of the lane label images in a simple scenario, which provides label data for the training of the first-stage network, is proposed. Then, an adaptive edge detection algorithm based on the Canny operator is used to relocate the lane detected by the first-stage model. Furthermore, the unrecognized lanes are shielded to avoid interference in subsequent model training. Then, the images processed by the above method are used as label data for the training of the second-stage model. The experiment was carried out on the KITTI and Caltech datasets, and the results showed that the accuracy and speed of the second-stage model reached a high level.
Highlights
To meet the needs of intelligent driving, the lane detection algorithm must have high accuracy and real-time response [1,2]
Bounini et al [4] proposed an algorithm for road boundary and lane detection based on the Hough transform, which combines the Canny edge detector, least square method, and Kalman filter to predict the location of a road boundary
The method for lane detection based on the Hough transform has the shortcoming that, when there are many lines in the image, the false rate of a lane is relatively high [7,8]
Summary
To meet the needs of intelligent driving, the lane detection algorithm must have high accuracy and real-time response [1,2]. Low et al [3] fitted the lane by detecting the edge of a lane using Canny operators, finding the best line using the Hough transform. Bounini et al [4] proposed an algorithm for road boundary and lane detection based on the Hough transform, which combines the Canny edge detector, least square method, and Kalman filter to predict the location of a road boundary. Ding et al [5] proposed a vision-based road ROI (Region of Interest) determination algorithm, which uses the information on the location of vanishing points to detect road regions, and uses the Hough transform to detect line segments. The method for lane detection based on the Hough transform has the shortcoming that, when there are many lines in the image, the false rate of a lane is relatively high [7,8]
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