Multi-Lane Detection Based on Deep Convolutional Neural Network

In order to solve the problem of the poor robustness for extracting the multi-lane marking, a multi-lane detection algorithm based on deep convolutional neural network is proposed. Since the results of the feature extraction of lane boundary were affected by various factors, a deep convolutional neural network based on FCN network is used for lane boundary feature extraction, and the neural network can be used to classify the lane images at the pixel level. Then the network parameters are trained on the public Tusimple data set and evaluated at Caltech Lanes data set. The last part combines a linear model with a curved model to realize the establishment of the lane marking equation, Hough transform is used to determine the fit interval and the least square method is used to fit lane marking. The experimental results show that the average accuracy of the algorithm for identifying lane on the Tusimple data set is 98.74%, and the accuracy rate on the Caltech Lanes data set is 96.29%.