Detection of highway lane lines and drivable regions based on dynamic image enhancement algorithm under unfavorable vision

Abstract

The detection of lane lines and drivable regions is the basis for the development of advanced driving assistance systems. Aiming at the problem of the poor robustness of highway detection under unfavorable visual conditions (UVCs), a new road detection method based on the dynamic image enhancement algorithm is proposed. The classification of images under different UVCs is obtained using gray feature and definition feature, and the classification result is employed to select an appropriate enhancement algorithm. The definition parameters of the images, which are used to dynamically adjust the parameters of the image enhancement algorithm, are acquired based on the definition evaluation model. On this basis, the improved probabilistic Hough transform algorithm and the adaptive region growth algorithm based on Gaussian model are applied to detect lane lines and drivable regions, respectively. The experimental results demonstrate the robust adaptation and real-time effectiveness of the approach under UVCs.