Research On Lane And Vehicle Detection Algorithm Based On Deep Lerning

With the development of the times,artificial intelligence has been closely related to our lives.As a hot research topic in artificial intelligence,computer vision has attracted more and more researchers ’ attention.Due to the further improvement of theoretical technology and hardware facilities,deep learning has shown superior performance in the field of computer vision in recent years.Based on the method of deep learning,this paper focuses on two hot directions of lane and vehicle detection.Combined with the requirements of autonomous driving scene,the model accuracy and real-time performance are studied.(1)Aiming at the problems of low accuracy and poor robustness of traditional lane detection algorithms,a lane detection method based on PINet +RESA network is proposed.Firstly,the bottleneck module in PINet network is pruned to reduce network computation.Secondly,the RESA module is added before the module.By using the strong shape prior information of the lane line,the spatial information of the cross rows and columns between the feature images is captured,and the feature maps in the vertical and horizontal directions are superimposed regularly.So that each feature pixel after integration can obtain global information,in order to enhance the network extracted features.The improved algorithm is trained and tested on public dataset Tu Simple,CUlane,and self-made dataset Custom.The results show that the improved algorithm can greatly reduce the detection time while ensuring the detection accuracy,which is of reference significance for real-time lane detection.(2)For vehicle detection accuracy and real-time requirements.Starting from the network feature extraction ability of YOLOv5 s,the Swin Transformer attention mechanism is introduced to improve the ability of the network to obtain global information.In addition,a feature graph splicing method Self-Concat with weights is proposed,so that the network can adaptively adjust the weights corresponding to different features.So as to suppress the negative characteristics of the target and enhance the positive characteristics of the target.The experimental results show that the F1 score,m AP and detection speed have achieved good results.