对于车道检测，大多数现有的算法都基于手工制作的低级特征[1][2][3]（Aly 2008; Son 等 2015; Jung，Youn 和 Sull 2016），限制了其处理恶劣条件的能力。只有 Huval 等人[4]。（2015） 首次尝试在车道检测中采用深度学习，但没有大量的一般数据集。而对于语义分割，基于 CNN 的方法已成为主流并取得了巨大成功[5][6]（Long，Shelhamer，and Darrell 2015; Chen et al。2017 ）。在神经网络中还有一些尝试利用空间信息。 Visin 等人[7]（2015）和 Bell 等人 [8]（2016）使用递归神经网络沿每行或每列传递信息，因此在一个 RNN 层中，每个像素位 置只能接收来自同一行或列的信息。 Liang 等人（2016a; 2016b）[9][10]提出了 LSTM 的变体 来利用语义对象解析中的上下文信息，但是这样的模型在计算上是昂贵的。研究人员还尝试 将 CNN 与 MRF 或 CRF 等图形模型结合起来，其中消息传递通过与大内核进行卷积实现（Liu et al。2015; Tompson et al。2014; Chu et al。2016）[11][12][13]。在 SCNN 中，（1）顺序消息 传递方案比传统的密集 MRF / CRF 具有更高的计算效率，（2）消息传播为残差，使得 SCNN 易于训练， （3）SCNN 是灵活的，可以应用于任何层次的深度神经网络。 车道检测仍然是机器视觉研究的沃土领域;因此，已经提出了许多方法来完成这项任务。 然而，霍夫变换的变化仍然是最常用和最常用的方法之一。在这些方法中，输入图像首先使 用 Canny 边缘检测器[14]或可控滤波器[15]预处理以找到边缘，然后是阈值。经典的霍夫变 换然后用于在二值图像中找到直线，这通常对应于车道边界。随机 Hough 变换[16]，也是一 种更快，更经典的 Hough 变换记忆效率的对应物，也被用于车道检测[17]，[18]。当道路大 部分直线时，用于线路寻找的经典霍夫变换效果很好;然而，对于弯曲道路，样条[19]和双曲 线拟合[20]通常用于提供支撑。分段线拟合显示出一些改进，通过对路段图像进行霍夫变换 以产生曲线并处理许多关于阴影和道路图案的问题[21,22]。此外，边缘方向的结合也被用来 消除一些错误的信号[23]，[24]。不幸的是，通过霍夫变换，通常很难确定一条线是否与伪 像或车道边界相对应。在色彩分割方法中，RGB 图像经常转换为 YCbCr，HSI 或自定义色彩 空间。在这些替代色彩空间中，一个像素的亮度和色度分量被单独建模。结果，可以大大减 少阴影和颜色分量中的动态照明的影响。因此，这种转换通常会增强对黄色车道标记等有色 物体的检测[32]，[25]。然而，由于这些方法在像素级别运行，它们通常对来自路灯或类似 照明源的环境光颜色的变化敏感。 [26]中已经显示了使用直方图来分割车道标记。然而， 为了进行直方图计算，需要在水平频带中存在一部分车道标记。立体视觉和 3D 也被用于车 道检测。在[27]和[28]中，立体声摄像头用于提供路面的两个有利位置，希望通过单摄像头 方法改善结果。具体而言，在每个视图中检测车道标记，然后使用极线几何和相机校准信息 将结果合并。在[27]和[28]中，假设车辆始终位于车道中心，固定搜索区域用于查找车道标 记。学习方法，如人工神经网络[33]和支持向量机[29]也已用于车道检测;但是，他们可能不 会遇到没有遇到过训练遇到的道路条件时表现良好[30]。最后，[31]中的综合文献综述总结 了当今大多数突出的车道检测技术。 虽然车道和道路标记检测似乎是一个简单的问题，但该算法必须在各种环境下准确，并 且计算时间快。基于手工特征的车道检测方法[34-40]检测通用形状的标记，并尝试拟合线或 样条来定位通道。这组算法在某些情况下表现良好，但在不熟悉的条件下表现不佳。在道路 标记检测算法的情况下，大部分作品都基于手工制作的特征。陶等人。 [41]将多个感兴趣 区域提取为最大稳定极值区域（MSER）[42]，并依靠 FAST [43]和 HOG [44]特征为每个道路标 记建立模板。同样，格林哈尔等人。 [45]利用 HOG 特征和支持向量机训练生成类标签。然 而，正如在车道检测案例中，这些方法在不熟悉的条件下表现出性能下降。 最近，深度学习方法在计算机视觉领域取得了巨大成功，包括车道检测。 [47,46]提出 了一种基于 CNN 的车道检测算法。 Jun Li et al。 [48]同时使用 CNN 和递归神经网络（RNN） 来检测车道边界。在这项工作中，CNN 提供了车道结构的几何信息，并且该信息被 RNN 用 来检测车道。贝赫等人。 [49]提出使用双视角卷积中性网络（DVCNN）框架进行车道检测。 

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