Track Cone Barrels Detection and Recognition Method Based on YOLO v4 Model
In order to quickly detect and accurately identify the track cone barrel,a track cone barrel detection and recognition method based on YOLO v4 model is proposed.Firstly,according to the complex and changeable track scene,multiple cone barrel images are collected as the original data of the data set,and the cone barrel data set is made,trained and selected on the indus-trial computer;Then build a cone barrel detection and recognition system based on YOLO v4 model,and select three common track scenes for real vehicle test.The experimental results show that this method can still quickly detect and accurately identify the target cone barrel under different lighting conditions,especially in the scene where the cone barrel is dense and multiple cone bar-rel targets overlap,the confidence is more than 0.91,has strong robustness,and the average frame rate of real-time detection is 35f/s,it can meet the needs of driverless formula racing for the accuracy and real-time performance of the sensing system.
Formula Car DriverlessYOLO v4Track ConeTarget DetectionCone Barrel Recognition
万方数据
维普
