Traffic Sign Recognition Algorithm Model Based on Machine Learning

At present, the development of our country is getting better and better, the vehicles running on the road are also increasing, so the traffic problems are becoming more and more obvious. This kind of problem will also set up the development of the modern city. At this time, the intelligent transportation technology has also developed, and the above problems are gradually treated by new methods. It has become one of the hot topics in the field of an intelligent transportation system to use the advantages of machine learning technology to deal with traffic congestion and improve the traffic efficiency of the road network. It has high theoretical and practical significance to detect road traffic signs in the actual scene. A method based on directional gradient histogram features combined with a support vector machine classifier is proposed. Each type of traffic sign has its own characteristics. By classifying its appearance and color, many recognition methods are produced, and the target area is retained by a unique method, thus the feature can be extracted and identified. Make the paving. The main work is to obtain a training sample, and then add the direction gradient histogram of the sample library into the SVM for training, to get a one to many classifiers to be tuned continuously, it can realize the rapid and accurate judgment of multiple traffic signs.