AR-NET: lane detection model with feature balance concerns for autonomous driving

Real-time accurate lane detection is essential task in autonomous driving systems. We propose a new inference model AR-NET to implement the real-time lane detection task. Compared to the pixel-level lane segmentation approach, the Grid classification method is used in AR-NET to reduce computational consumption. Although the deep feature extraction network can extract features effectively, it does not pay attention to the connection between different feature lanes. In order to effectively fuse the extracted features, we propose an interaction network. The lane information obtained from the encoding layer is sent to the interaction network, which uses interaction to fuse local and global features to achieve a balance of local and global attention. Attention is also used to tune target detail features, which learn contextual information by establishing correlations between the features extracted in convolution and the attention channel. Experiments on a publicly available lane detection benchmark dataset demonstrate that our method achieves excellent performance in terms of speed and accuracy. This method achieves 200 + frames per second(FPS) with an accuracy of 96.11% on TuSimple.