基于空间特征聚合的车道线检测算法

车道线检测是无人驾驶任务中最重要的模块之一.由于车道线具有独特的结构,且容易受到各种各样复杂环境(比如光线、遮挡、模糊等)的影响,因此车道线检测也是一项很具有挑战性的任务.传统的卷积神经网络(CNN)难以直接学习到精细的车道线空间特征,本文使用空间特征聚合模块对CNN提取的特征在空间维度进行融合增强,为级联的车道线预测器提供了丰富的空间特征信息.实验证明,空间特征聚合模块通过聚合水平和垂直方向的特征图获取精细的全局信息,在多种复杂环境下都能提升车道线检测算法的性能,且不会影响检测的速度.

Lane Detection Algorithm Based on Spatial Feature Aggregation

Lane detection is one of the most important modules in self-driving tasks. Lane detection is a challenging task as the structure of the lane line is special, and the detection is easily affected by various environments (such as lighting transformation, obstruction, and the blur of the lane line). Considering the traditional Convolutional Neural Network (CNN) is unable to learn fine spatial features of the lane line directly, in this study, the spatial feature aggregation module is employed to enhance the features extracted by CNN in spatial dimensions, providing rich spatial features for the cascade lane predictor. The experiments show that the module learns fine global information by aggregating feature maps in horizontal and vertical directions and thus improves the performance of the lane detection algorithm in different environments without reducing the detection speed.