Review of convolutional neural network and its application in intelligent transportation system

From the perspectives of feature transmission mode, spatial dimension and feature dimension, the improvement directions of convolution neural network structure in recent years were reviewed. The working principles of the convolution layer, pooling layer, activation function and optimization algorithm were introduced, and the recent developments of pooling methods in terms of value, level, probability, and transformation domain were summarized. The comparison of some representative activation functions, and the working principle and characteristics of the gradient descent algorithm and its improved and adaptive optimization algorithm were given. The application and research status of convolutional neural network in intelligent transportation fields such as license plate recognition, vehicle type recognition, traffic sign recognition, and short-term traffic flow prediction were reviewed. The convolutional neural network algorithm was compared with the support vector machine, differential integrated moving average regression model, Kalman filter, error back propagation neural network, and long-term and short-term memory network algorithms from the advantages and disadvantages and main application scenarios in the field of intelligent transportation. The issues of poor robustness and poor real-time performance of convolutional neural network in the field of intelligent transportation were analyzed. The development trend of convolutional neural network was evaluated in terms of algorithm optimization, parallel computing, and supervised learning to unsupervised learning. Research results show that the convolutional neural network has strong advantages in the field of vision. It is mainly used for traffic sign, license plate, vehicle type recognition, traffic event detection, and traffic state prediction in intelligent transportation system. Compared with other algorithms, the convolutional neural network can extract more comprehensive features. It can effectively improve the recognition accuracy and speed and has great application value. The convolutional neural network will bring new breakthroughs to intelligent transportation in the future through the optimization of network structure, the improvement of algorithm and computing power, and the enhancement of benchmark data sets. © 2021, Editorial Department of Journal of Traffic and Transportation Engineering. All right reserved.