Research on Traffic Sign Recognition Technology Based on Learning Vector Quantitative Neural Network
Because the traditional traffic sign recognition technology needs a lot of data for training and requires high input data,which leads to a large error in recognition results.Therefore,a traffic sign recognition technology based on learning vector quantization neural network is proposed.The preprocessed traffic sign images are taken as the data set to be recognized,and the learning vector quantization neural network model is constructed.After inputting the images to be recognized for learning and training,the recognition results of traffic sign categories are output to realize the recognition of traffic sign.The experimental results show that the recognition rate of traffic signs under the designed technology is as high as 98.77％,which proves the effectiveness and correctness of the technology.
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