A Novel Target Detection Method Based on Improved YOLO Network

Real-time traffic flow prediction based on artificial intelligence refers to the use of large data of vehicles in intelligent transportation system to calculate the depth of artificial intelligence and then predict real-time traffic flow. However, the related work of AI is facing many challenges, such as the large number of traffic physical objects, the low accuracy of spatial location of traffic information and the diversity of vehicles, which will have a great impact on real-time traffic flow forecasting. Therefore, this paper studies the artificial intelligence algorithms of traffic physical object (TPO) and traffic information space (TIS) under the background of intelligent transportation system. A state-based filtering module is used to improve the prediction accuracy of ITS. The theoretical analysis and experimental results show that the proposed method has high execution efficiency and improves the accuracy of real-time traffic flow prediction.