Deep Learning-Based Lane Detection: An In-depth Analysis

Abstract

Lane detection technology is mainly studied in the traditional method at first, but this method is not suitable for practice. Because in some unfavorable conditions usually do not have the required robustness of autonomous driving systems. Later, Deep learning-based techniques that could increase recognition speed and accuracy were put forth. However, in the complex and changing environment, the recognition accuracy would be reduced, so the current methods need to be strengthened. This paper compares the classical methods based on image segmentation and object detection. The classical object detection algorithms such as VPGNet, Spatial CNN and spatial-temporal deep learning are introduced. In addition, the classical image segmentation methods such as Lanenet, UNet-ConvLSTM and MMA-Net are also introduced. By comparing the performance of these methods on different data sets, the advantages and problems of these methods are analyzed, and the development trend of these methods in this field is predicted. Overall, at the end of this review, the full text is summarized and prospected.