Fast Traffic Flow Detection Algorithm Based on Deep Learning

The field of intelligent detection is a very potential research direction. Deep learning technology can collect vehicle flow data, detection signals and pedestrian location information in automatic stations in real time, and obtain relevant basic data of the current road state. During the development process, this paper also uses the MATLAB platform to build a user interface, so that users can easily and quickly extract the results and perform functions such as display and video playback. Finally, the algorithm is simulated and tested in this paper. The test results show that the fast traffic flow detection algorithm based on deep learning can well reflect the multiple small errors existing in each movement trajectory of the vehicle during driving, and its scalability is above 0.65. In addition, the use of encoders generates simple and unambiguous identifiers that can be followed by control system identification algorithms. The application of these technologies will bring a huge impetus to the development of intelligent vehicles.