Real-time tracking of highway center markings is a key part of the vis ual flight of highway inspection drones.Aiming at the problem of leaky detection accuracy and speed of traditional object tracking method.This paper proposes an optimized algorithm b ased on YOLO (you only look once) and Deep-SORT (simple online real-time tracking) models to improve the accuracy and the speed of highway center marking tracking.This model proposes a new backbone network by i ntroducing CSPnet (cross stage partial network) which has replaced the original network.To further heighten the performance of proposed model,this study also replaces the origina l activation function in the network to improve the ability of proposed model.In this study, Deep-SORT method is used to track the detected objects .Compared with several types of typical target tracking models,the processing speed is increas ed several times under the condition that the tracking accuracy is basically unchanged.