深度学习在车道线检测中的应用综述OA

With the rapid development of autonomous driving technology,lane detection,as the core task of environmental perception systems,faces key challenges such as occlusion,sudden changes in lighting,and geometric diversity in complex scenes.A systematic study is conducted on lane detection methods based on deep learning to address this issue,with a focus on analyzing the algorithm frameworks,implementation processes,and performance optimization strategies of 2D and 3D detection technologies.In terms of 2D detection,the limitations of traditional image processing methods are explored,and the applications of convolutional neural networks,fully convolutional networks,and Transformer architectures in feature extraction,instance segmentation,and lightweight design are elaborated in detail.In the aspect of 3D detection,the technical route based on LiDAR point cloud,stereo vision and multi-sensor fusion is described,and the advantages and disadvantages of different sensor combinations in geometric reconstruction and dynamic tracking are analyzed.By combining commonly used datasets and evaluation metrics,a standardized reference basis is provided for the standardized testing of algorithm performance.Analysis shows that multimodal data fusion and lightweight network design can effectively improve the stability of lane detection systems in complex scenarios such as occlusion and sudden changes in lighting,while 3D detection technology further enhances positioning accuracy by introducing spatial geometric information.Future research should focus on the construction of a universal modeling framework,innovation in multimodal complementary fusion mechanisms,enhancement of temporal reasoning capabilities in dynamic scenarios,and efficient and lightweight deployment of embedded platforms to promote the practical application of autonomous driving technology.