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.

中文翻译：

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基于动态图像增强算法的不利视野下高速公路车道线及可行驶区域检测

摘要 车道线和可行驶区域的检测是开发先进驾驶辅助系统的基础。针对不利视觉条件(UVCs)下公路检测鲁棒性差的问题，提出了一种基于动态图像增强算法的公路检测新方法。利用灰度特征和清晰度特征得到不同UVCs下的图像分类，并根据分类结果选择合适的增强算法。基于清晰度评价模型获取图像清晰度参数，用于动态调整图像增强算法的参数。以这个为基础，将改进的概率霍夫变换算法和基于高斯模型的自适应区域增长算法分别应用于车道线和可行驶区域的检测。实验结果证明了该方法在 UVC 下的鲁棒适应性和实时有效性。