Lane Instance Segmentation Algorithm Based on Convolutional Neural Network

Vehicle driving environment perception is a key and difficult problem of automatic driving field, among which lane detection is the foundation of vehicle driving environment perception. In view of the difficulty in distinguishing different lane instances, the high time complexity of existing distinguishing algorithms, and the need to manually adjust hyperparameters in different driving scenes, a three-branch lane instance segmentation algorithm is proposed in this paper, and the segmentation results are adaptively clustered to fit lanes of different instances. Considering the unbalanced characteristic of the image data obtained by the vehicle-mounted camera, a convolutional neural network is trained on the basis of the Tversky Loss function of the three-section field of view method. In view of the large curvature radius of the lane, the weight of the higher-order term is used as the regular term of a least square method to fit lanes. The test results on the TuSimple dataset show that the accuracy of the algorithm in identifying the lane of the considered example is 96. 23%. Compared with LaneNet, the time complexity of the algorithm is reduced by 23.67%. Additionally, it has a good detection effect for various vehicle driving scenes.