Lane Detection Combining Details and Integrity: an Advanced Method for Lane Detection

Abstract

Lane detection methods based on convolutional neural network have achieved excellent performance in recent years. Most of them treat lane detection as a semantic segmentation task which judges whether each pixel belongs to a lane. To make full use of the characteristics of lane shape, some researchers proposed to predict the whole lane. In this paper, we propose Lane Detection Combining Details and Integrity (LDCDI) which can explicitly leverage the advantages of both the segmentation-based methods and the regression-based methods. Specifically, we exploit an extra branch with regression-based methods as the auxiliary module after the main module. It not only maintains the advantages of the segmentation-based methods in lane detail segmentation, but also enables the model to have a sufficient understanding of the lane shape. Besides, the auxiliary module only takes part in the training, and there is no extra cost in the prediction. To further improve the quality of lane detection, we introduce a novel direction-sensitive block (DSB) based on ERFNet as the main module, which is more sensitive to the direction information of the image, so as to obtain better performance. Extensive experiments on the CULane dataset can demonstrate that our method outperforms other methods and achieves the state-of-the-art.