At present, the number of vehicle owners is increasing, and the cars with autonomous driving functions have attracted more and more attention. The lane detection combined with cloud computing can effectively solve the drawbacks of traditional lane detection relying on feature extraction and high definition, but it also faces the problem of excessive calculation. At the same time, cloud data processing combined with edge computing can effectively reduce the computing load of the central nodes. The traditional lane detection method is improved, and the current popular convolutional neural network (CNN) is used to build a dual model based on instance segmentation. In the image acquisition and processing processes, the distributed computing architecture provided by edge-cloud computing is used to improve data processing efficiency. The lane fitting process generates a variable matrix to achieve effective detection in the scenario of slope change, which improves the real-time performance of lane detection. The method proposed in this paper has achieved good recognition results for lanes in different scenarios, and the lane recognition efficiency is much better than other lane recognition models.

中文翻译：

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边缘云计算中基于CNN的带实例分割的车道检测

当前，车主的数量正在增加，具有自动驾驶功能的汽车越来越受到关注。车道检测与云计算相结合可以有效地解决传统车道检测依靠特征提取和高清晰度的弊端，但也面临计算量大的问题。同时，云数据处理与边缘计算相结合可以有效减轻中心节点的计算量。改进了传统的车道检测方法，并使用当前流行的卷积神经网络（CNN）建立基于实例分割的对偶模型。在图像采集和处理过程中，边缘云计算提供的分布式计算架构被用来提高数据处理效率。车道拟合过程生成一个可变矩阵，以在坡度变化的情况下实现有效检测，从而提高了车道检测的实时性能。本文提出的方法在不同场景下对车道的识别效果都很好，并且车道识别效率比其他车道识别模型要好得多。