A novel lane-detection method based on spatial continuity and long-range dependence capture

Convolutional neural network-based image segmentation has been successfully applied to lane detection. However, existing lane detection method cannot effectively capture the spatial continuity and long-range dependence of the lane line feature. This gives rise to two issues, i.e., discontinuity and structural divergence. In this paper, we propose a generative adversarial network (GAN) framework based on self-attention mechanisms and asymmetric convolution to achieve accurate lane detection. Specifically, in consideration of spatial continuity, we employ asymmetric convolution to capture the continuity relationship from both vertical and horizontal directions. Moreover, we append two types of self-attention mechanisms to enhance the long-range dependency between different channels and different positions of the feature maps. In the discriminator of the GAN, patch loss is used to discriminate the structural authenticity of predicted output. Experiments on the Tusimple, CULane and Caltech datasets show that the proposed method obtains competitive results compared with existing methods. Without any preprocessing or postprocessing, the mean intersection over union (IoU) reaches 53.17%, 59.9% and 73.37% respectively.