BSCNNLaneNet: A Novel Bidirectional Spatial Convolution Neural Network for Lane Detection

Accurately detecting lane lines is a hot topic in computer vision. How to effectively utilize the relationship between lane features for detection is still an open question. In this paper, we propose a novel lane detection model based on convolutional neural network (CNN), namely, the BSCNNLaneNet (Bidirectional Spatial CNN Lane Detection Network). The proposed model is based on the spatial CNN method and incorporates a bidirectional recurrent neural network (BRNN) block to learn the spatial relationships between slice features. Additionally, a convolutional block attention mechanism is introduced to gain global features, which enhance the global connection between slice features in different directions. We conduct extensive experiments on the TuSimple dataset. The results demonstrate that the proposed method surpasses the original spatial CNN method, achieving an increase in accuracy from 96.53% to 96.86%.