A Lane Line Recognition Method Based on Deep Learning
The lane line recognition method based on deep learning and semantic segmentation can recognize lane line images end-to-end and adapt to complex and ever-changing lane environments.This lane recognition model is based on deep learning and semantic segmentation,which is based on Segnet and consists of two parts:an encoder and a decoder.The encoder adopts a 4-level down sampling structure,which is mainly composed of the convolution layer and the maximum pooling layer,and uses the PRelu function as the Activation function of the convolution layer,which can effectively improve the fitting ability of the network and reduce the risk of over fitting;The decoder adopts a 4-level upsampling structure,mainly composed of upsampling layer,convolution layer,and batch standardization layer.In order to solve the problem that the number of lane lines and back-ground pixels in the lane line image is seriously unbalanced,the weighted Cross entropy function is used to calcu-late the loss value of the network,and the MFB algorithm is used to determine the weight value.Finally,valida-tion was conducted on the tuSimple dataset,and based on extensive experiments,good recognition performance and high robustness were achieved by modifying the weights.
Lane line recognitionSemantic segmentationDeep learningCNN
NETL
NSTL
万方数据
