Shallow Detail and Semantic Segmentation Combined Bilateral Network Model for Lane Detection

Lanes play a very critical and important role in maintaining the orderly operation of road traffic. Therefore, the automatic lane detection is very important and has significant potential value. Combining the existing lane detection methods based on deep learning, this paper proposes a new lane detection model, Bi-Lanenet, to solve the problems that still exist in the current methods, aiming to overcome the disadvantages in these methods and improve the practicality of the lane detection algorithm. For the task of lane detection, this paper improves the segmentation accuracy and improves the traditional lanenet model while ensuring that the network model is lighter weight and more robust. Then, we propose a new bilateral lane recognition network based on semantic segmentation and details, and use the random sample consensus (RANSAC) method to optimize the post-processing process. We conduct experiments on the TuSimple and CULane datasets and prove that the method can detect lanes in the image efficiently with 110 frame-per-second (FPS) and accurately at 97.08%.