An Improved Lane Detection Algorithm Based on Nested Residuals Network and Its'Application
Lane detection is one of the core steps in autopilot.A residual network lane detection model based on nested structure is proposed to deal with the complex and changeable real road environment in this paper.Firstly,the network structure of R2U-Net is reconstructed with the model,and then the deep learning network is used to learn and detect the lane data set.Based on the large-scale lane de-tection data set released by Tucson Company,a large number of comparative experiments are carried out within this model.The results show that the nested residual network structure model achieves high detection effect in lane detection,and the detection accuracy reaches 91％,which has a significant ad-vantage compared with other similar models.
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