A Traffic Sign Image Recognition and Classification Approach Based on Convolutional Neural Network

In this paper, the traffic sign recognition method based on CNN is studied by using image processing and machine vision processing technology combined with the application of in-depth learning in target classification. A traffic sign recognition method with high recognition efficiency and high efficiency is proposed. The scheme uses the method of training cascade classifiers based on HOG features to detect traffic signs. At the same time, to ensure that all traffic signs are detected, an improved CNN model is trained to determine whether there are traffic signs in the candidate area, and the final decision is made as the last level of the cascade classifier. The simulation results show that the scheme can accurately locate the location of traffic signs, greatly reduce the false detection rate, improve the accuracy rate, and lay the foundation for subsequent traffic sign recognition.