Improved Lane Detection with Multilevel Features in Branch Convolutional Neural Networks

Existing smart vehicles heavily depend on success of precise positioning, optical radar, visual detection and recognition to determine their road conditions and perfect routes. The visual-based approach is the simplest and most effective enabling technology to reach the goal. In performing such an overtaking maneuver, this vision-based driving assistant system must be able to precisely recognize the lane marks first. The traditional approach needs a classifier with hand-crafted features and an adjusted threshold to achieve a robust lane detection in various environment conditions. In this paper, we adopt the deep learning approach to achieve the robust lane detection. The proposed lane detection network inspired by LaneNet model, which uses semantic segmentation concepts utilizes multiple level features of the encoder and designs enhanced binary segmentation and reduced pixel embedding branches. By reduction of computation in decoders, the proposed network effectively utilizes multilevel features to precisely predict the high quality lane maps. The experiments on Tusimple and CuLane datasets verify that the proposed lane detection network achieves better accuracy performance than LaneNet and faster computation than the existed methods for real-time applications.