Traffic sign classification method based on deep neural network

A deep neural network, traffic sign technology, applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve the problems of poor real-time performance, low accuracy, and difficulty in taking into account the accuracy and detection speed of traffic sign classification. Achieve the effect of speeding up the test speed and improving the accuracy

Inactive Publication Date: 2015-04-15
GUANGZHOU INST OF ADVANCED TECH CHINESE ACAD OF SCI
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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 m

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  • Traffic sign classification method based on deep neural network
  • Traffic sign classification method based on deep neural network
  • Traffic sign classification method based on deep neural network

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Embodiment Construction

[0035] The specific embodiment of the present invention will be further described below in conjunction with accompanying drawing:

[0036] refer to figure 1 , a traffic sign classification method based on a deep neural network, including the following steps:

[0037] A. The moving target detection method based on the optical flow method detects the read-in video, and when a moving object is detected, the region of interest is extracted;

[0038] B. The extracted region of interest is divided into blocks using blocks of a fixed size;

[0039] C. Perform scaling processing on the image after block processing, and convert it into an image of the same size;

[0040] D. Use the converted image as input and use a convolutional neural network for classification.

[0041]Convolutional Neural Networks (CNN, Convolutional Neural Networks) is a type of artificial neural network and has become a research hotspot in the fields of speech analysis and image recognition. Its weight sharin...

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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...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46
CPCG06V20/582G06F18/2111
Inventor 贺庆冷斌官冠胡欢蒋东国
Owner GUANGZHOU INST OF ADVANCED TECH CHINESE ACAD OF SCI
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