Aiming at the characteristics of unmanned formula racing cars requiring precision and real-time performance of the sensing system, a target detection system based on the fusion of lidar and camera information was designed. The sensor perception model is established. The visual information uses the yolo v4 image recognition algorithm. The distance information is obtained by filtering and clustering the point cloud. The tilt of the point cloud is corrected by a rotation processing algorithm based on ground fitting. Coordinates and point cloud coordinates adopt Euclidean distance strategy for target information fusion. The test results show that the radar algorithm can complete three-dimensional target detection. The average accuracy of the yolo v4 (you only look once-4) image recognition algorithm for traffic cone detection is 97.5%, the average pixel error rate in the direction is 1%, and the target detection The system meets actual driving requirements in terms of accuracy and real-time performance.