Deployment of Lane Detection,Object Detection,and Drivable Area Segmentation Algorithms Based on YOLOP
This paper points out that,at present,multi-task learning in panoramic autonomous driving perception has made significant achievements and achieved remarkable results.Among them,object detection and segmentation tasks are extremely important,which can help in decision-making,route planning and safety information.However,object detection and segmentation still have limitations,requiring large amounts of data and prior information.In order to make multi-task learning in automatic driving more efficient and accurate,this paper integrates the embedded platform into the research of lane detection,integrating lane detection and obstacle detection,which can effectively improve the computing efficiency and environment perception.This paper also deploys deep learning using NVIDIA's embedded platform and maintain advanced performance.
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