In view of the slow detection speed of the current algorithm of the target detection system for Chinese college students’ driverless formula racing cars, the low detection accuracy and serious missing and false detection in different scenarios are easy to occur. In the recognition module, first of all, in order to improve the detection speed and recognition accuracy of the original YOLOv5 basic model, the paper uses CIoU as the boundary box regression loss function. To solve the problems of slow convergence speed and low recognition accuracy of the algorithm during training, the original weighted nonmax suppression method is changed to DIoU_NMS in this paper, the test accuracy is 0. 963, which is 2. 1% higher than the original algorithm. The results show that the improved algorithm is more suitable for cone color recognition in the competition scene. Secondly, in the tracking module, the depth apparent feature cone color recognition model is trained, and the single target tracking algorithm is changed to be able to track multiple types of targets. Compared with a single target detection algorithm, the phenomenon of missing detection is effectively reduced. Finally, the ranging module is added to use the height information of the detection frame to distance the vehicle camera to the cone barrel. The average error within 90 meters is less than 9%. The frame rate of the whole system reaches 20 frames/ second, realizing cone color recognition and effective distance measurement, and providing more data support for the game.