基于实例分割的车道线检测及自适应拟合算法

车道线检测是智能驾驶系统的重要组成部分。传统车道线检测方法高度依赖手动选取特征，工作量大，在受到物体遮挡、光照变化和磨损等复杂场景的干扰时精度不高，因此设计一个鲁棒的检测算法面临着很大挑战。为了克服这些缺点，提出了一种基于深度学习实例分割方法的车道线检测模型。该模型基于改进的Mask R-CNN模型，首先利用实例分割模型对道路图像进行分割，提高车道特征信息的检测能力；然后使用聚类模型提取离散的车道线特征信息点；最后提出一种自适应拟合的方法，结合直线和多项式两种拟合方法对不同视野内的特征点进行拟合，生成最优车道线参数方程。实验结果表明，该方法提高了检测速度，在不同场景下都具有较好的检测精度，能够实现对各种复杂实际条件下的车道线信息的鲁棒提取。

Instance segmentation based lane line detection and adaptive fitting algorithm

Lane line detection is an important part of intelligent driving system. The traditional lane line detection method relies heavily on manual selection of features, which requires a large amount of work and has low accuracy when it is interfered by complex scenes such as object occlusion, illumination change and road abrasion. Therefore, designing a robust detection algorithm faces a lot of challenges. In order to overcome these shortcomings, a lane line detection model based on deep learning instance segmentation method was proposed. This model is based on the improved Mask R-CNN model. Firstly, the instance segmentation model was used to segment the lane line image, so as to improve the detection ability of lane line feature information. Then, the cluster model was used to extract the discrete feature information points of lane lines. Finally, an adaptive fitting method was proposed, and two fitting methods, linear and polynomial, were used to fit the feature points in different fields of view, and the optimal lane line parameter equation was generated. The experimental results show that the method improves the detection speed, has better detection accuracy in different scenes, and can achieve robust extraction of lane line information in various complex practical conditions.