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.

Mixed Noise Parameter Estimation Based on Variance Stable Transform

The ultimate goal of image denoising from video is to improve the given image,which can reduce noise interference to ensure image quality.Through denoising technology,image quality can have effectively optimized,signal-to-noise ratio can have increased,and the original mage information can have better reflected.As an important preprocessing method,people have made extensive research on image denoising algorithm.Video denoising needs to take into account the various level of noise.Therefore,the estimation of noise parameters is particularly important.This paper presents a noise estimation method based on variance stability transformation,which estimates the parameters of variance stability transformation by minimizing the noise distribution peak,and improves the parameter accuracy of mixed peak estimation by comparing and analyzing the changes of parameters.The experimental results show that the new algorithm of noise estimation has achieved good effects,which are making the field of video denoising more extensive.