Traffic sign classification method based on deep neural network

Abstract

The invention discloses a traffic sign classification method based on a deep neural network. The traffic sign classification method comprises the following steps: A, detecting a read-in video based on a moving object detection method of a light stream method, and when a moving object is detected, extracting a region of interest; B, utilizing blocks with fixed sizes to carry out blocked processing on the extracted region of interest; C, carrying out zooming processing on pictures after the blocked processing, and converting into pictures with the same size; D, inputting the converted pictures, and utilizing a convolutional neural network for classification. According to the method, the region of interest is extracted for the pictures after motion detection and then the blocked processing is carried out, and after the obtained pictures are converted into the pictures with the same size, processing is carried out by utilizing the convolutional neural network, so that the problems caused by an artificially assumed class conditional density function are avoided, the testing speed is greatly quickened, and the precision is greatly improved. The traffic sign classification method based on the deep neural network disclosed by the invention can be widely applied to the traffic field.

Description

technical field [0001] The invention relates to the traffic field, in particular to a traffic sign classification method based on a deep neural network. Background technique [0002] With the progress of urbanization and the popularization of automobiles, the number of motor vehicles has increased significantly, traffic congestion has intensified, traffic accidents have occurred frequently, and the problems of road traffic safety and transportation efficiency have become increasingly prominent. The driver support system based on computer vision is one of the important measures to solve the problems of traffic safety and transportation efficiency. It is gradually applied in the intelligent transportation system. Its research is generally carried out in three aspects: road recognition, collision recognition, and traffic sign recognition. . The research on road recognition and collision recognition is earlier, and many good results have been achieved; however, there is less re...

AI Technical Summary

Problems solved by technology

[0010] 5. A multi-feature layered traffic sign recognition method (CN103390167A) proposed by Sun Rui and Wang Jizhen of Chery Automobile Co., Ltd. This method solves the problem of low accuracy in traffic sign recognition through color-based detection methods , The problem of poor real-time performance

The method based on the edge contour is the most basic method. At present, there are many mature edge extraction methods to choose from, and then analyze on the extracted edge, but the disadvantage of the above method is that the accuracy and detection speed of traffic sign classification are difficult. get both

Images