Research on Image Detection Algorithm of Rail Traffic Congestion Degree Based on Convolutional Neural Networks

With the sustainable development of the social economy and the continuous maturity of science and technology, urban rail transit has developed rapidly. It solved the problems of urban road load and people’s travel and brought about the problem of rail transit passenger congestion. The image detection algorithm for rail transit congestion is established based on the convolutional neural networks (CNN) structure to realize intelligent video image monitoring. The CNN structure is optimized through the backpropagation (BP) algorithm so that the model can detect and analyze the riding environment through the monitoring camera and extract the relevant motion characteristics of passengers from the image. Furthermore, the crowding situation of the riding environment is analyzed to warn the rail transit operators. In practical application, the detection accuracy of the algorithm reached 91.73%, and the image processing speed met the second-level processing. In the performance test, the proposed algorithm had the lowest mean absolute error (MAE) and mean square error (MSE). In Part B, the MAE and MSE values of the model were 16.3 and 24.9, respectively. The error values were small, so the performance was excellent. The purpose of this study is to reduce the possibility of abnormal crowd accidents at stations and provide new ideas for intelligent management of rail transit.