A Lane Detection Method Based on Semantic Segmentation

This paper proposes a novel method of lane detection, which adopts VGG16 as the basis of convolutional neural network to extract lane line features by cavity convolution, wherein the lane lines are divided into dotted lines and solid lines. Expanding the field of experience through hollow convolution, the full connection layer of the network is discarded, the last largest pooling layer of the VGG16 network is removed, and the processing of the last three convolution layers is replaced by hole convolution. At the same time, CNN adopts the encoder and decoder structure mode, and uses the index function of the maximum pooling layer in the decoder part to upsample the encoder in a counter-pooling manner, realizing semantic segmentation. And combined with the instance segmentation, and finally through the fitting to achieve the detection of the lane line. In addition, the currently disclosed lane line data sets are relatively small, and there is no distinction between lane solid lines and dashed lines. To this end, our work made a lane line data set for the lane virtual and real identification, and based on the proposed algorithm effective verification of the data set achieved by the increased segmentation. The final test shows that the proposed method has a good balance between lane detection speed and accuracy, which has good robustness.