Complex lane detection based on dynamic constraint of the double threshold

In order to adapt to various complex lane environments, a general fast lane extraction algorithm was proposed. Firstly, according to the mutability of the local gray value and the characteristics of image saliency, the collected road image was preprocessed, and the region of interest was obtained by the proposed double threshold algorithm. Only in the significant region of the road were the image gray value change and image smoothing carried out to solve the problems of consuming too much time and poor noise resistance in lane detection. The lane line edge was then extracted based on the improved Canny operator. When the Otsu threshold was selected, the Kalman filter algorithm was introduced to quickly predict the optimal threshold of the subsequent image sequence in accordance with the characteristics of optimized autoregressive data processing.. Finally, for the straight line fitted by the Hough transform, an effective multi-layer evaluation function was established to realize the online correction of lane lines. Compared with the traditional lane line extraction algorithm, the experimental results show that the proposed algorithm has better accuracy, real-time performance and robustness.