Design And Application Of Lane Line Detection Algorithm Based On Semantic Segmentation Of Convolutional Neural Network

With the rapid development of science and technology,autonomous driving,driving assistant and other cutting-edge technologies are more and more close to our lives,and lane line detection technology is the basic support and key technology.Only the lane line detection system can detect lane lines accurately and stably,can it provide accurate and reliable guidance information for the driving of vehicles.Therefore,this paper thoroughly studied the lane line detection algorithm,designed an algorithm suitable for complex driving environment,and applied it to the lane line detection system.The main contents of this paper are as follows:(1)Research on lane line detection algorithm based on semantic segmentation of convolution neural network.In view of the current lane line detection methods,which focuses on lane line information separately and ignores the relevance of different entities in traffic scenes,resulting in the reduction of lane line detection accuracy and stability in complex driving environment,a multi task feature aggregation semantic segmentation network was designed.In this network,multi task learning and feature aggregation methods are used to mine the relationship between lane lines and lane regions,and the performance and stability of lane line segmentation are improved by supplementing related semantic information.In view of the imbalance of positive and negative samples in lane line segmentation,a boundary constraint loss function is proposed.The loss function models the boundary of lane lines,which makes the network pay more attention to lane lines rather than background information.In view of the imperfection of lane line segmentation result,a morphological post-processing method was designed to denoise the segmentation mask output and divide it into lane line instances.The lane line detection algorithm designed in this paper can get 96.43% detection accuracy on Tu Simple dataset,and only 3.36% false positive rate and 3.04% false negative rate,which is better than other methods.(2)Design of lane detection system and its implementation in Android embedded platform.In order to solve the problem of missing or false detection in a few video frames caused by jitter,strong interference and other factors in video detection,Kalman filter tracking method is used to build lane tracking model to achieve stable lane detection.In order to run the lane detection system in Android embedded platform,we lightweight the neural network model,convert and migrate it to inference framework,built the system interface and system processing logic in Android platform and realized the complete system functions.In this paper,the designed algorithm is applied to the actual detection system,which has a certain practical value.