面向园区场景的车道线局部定位检测方法研究

面向校园驾驶场景,提出一种综合性能表现较好的通用车道线检测算法,即局部定位检测法。 首先,采用经典图形学与基于噪声容忍的递归神经网络的学习模型相结合的方法,完成车道线所在局部区域 的检测,对目标车道线的灰度图像进行霍夫变换和灰度拉伸,设计递归神经网络学习模型,以梯度数据作为引导,对算法模型训练学习,以排除梯度信息相似的干扰物,并识别几何属性相关的不完整车道线形态,进而完成补全工作,应用稀疏惩罚,设计具有噪声容忍的递归学习模型,最大效率地利用具有被污染数据标注的 自建车道线图像数据集,以此为基础,采用深度强化学习方法,通过6个标识点对目标车道线进行精确定位, 并基于6个准确的定位点,检测和绘制车道线。

Research on Local Positioning Detection Method of Lane Lines in Park Scenes

In the campus driving scene, this paper proposes a general lane line detection algorithm with better comprehensive performance, that is, a local positioning detection method. First, a combination of classical graphics and a learning model based on a noise-tolerant recurrent neural network is used to complete the detection of the local area where the lane line is located, perform Hough transform and gray stretching on the grayscale image of the target lane line, design a recurrent neural network learning model, use the gradient data as a guide to train the algorithm model to learn in order to exclude distractors with similar gradient information and identify geometric attributes related of incomplete lane line morphology, and then complete the complementary work, apply sparse penalties, design recursive learning models with noise tolerance, and maximize the efficiency of using the self-built lane line image dataset with contaminated data annotation. Based on this, a deep reinforcement learning method is used to accurately locate the target lane line through six identification points, and based on the six accurate positioning points, the lane line is detected and drawn.