Traffic sign recognition method based on asymmetric convolution neural network

A convolutional neural network and traffic sign recognition technology, applied in the field of traffic sign recognition based on asymmetric convolutional neural network, can solve the problems of low recognition accuracy and slow recognition speed.

Inactive Publication Date: 2015-08-19
DALIAN UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0012] The technical problem to be solved in the present invention is the problem that the recognition accuracy is not high and the recognition speed is slow when the convolutional neural network is used for traffic sign recognition, and a traffic sign recognition method based on an asymmetrical convolutional neural network is proposed

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  • Traffic sign recognition method based on asymmetric convolution neural network
  • Traffic sign recognition method based on asymmetric convolution neural network
  • Traffic sign recognition method based on asymmetric convolution neural network

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Embodiment Construction

[0058] 1, determine training set, what the present invention selects is the training set in GTSRB (Germany traffic sign recognition benchmark, German traffic sign recognition benchmark), comprises training picture 39,209 pieces, test picture 12630 pieces.

[0059] 2. Preprocess the pictures in the training set, take out the target area (traffic sign area) in the original image, convert the color image into a grayscale image, and scale it to 48×48, and then pass the target area through histeq image contrast Enhanced processing, the original training set is obtained, and the test set is processed in the same way. In order to make the trained model more robust, the original training set is rotated [-10°, 10°] and scaled [0.9, 1.1], and then added to the original data set to form a new training set. In the new Randomly select samples equivalent to the number of test sets from the dataset to form the validation set, and the remaining samples form the final training set.

[0060] 3...

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Abstract

The invention, which belongs to the field of intelligent traffic sign recognition technology, relates to a traffic sign recognition method based on an asymmetric convolution neural network. With the method, problems of slow recognition speed and poor robustness during traffic sign recognition can be solved. According to the method, two convolution neural networks with different structures are used for carrying out feature mapping and extraction concurrently; the features are combined; and a full connection layer and a classifier are used for completing the whole classification process. The two convolution neural networks with different structures employ a random pooling operation and a maxout unit respectively, thereby guaranteeing diversity of the image features, improving the recognition precision, and accelerating the network operation speed. According to the invention, the structure of the traditional convolution neural network is modified and the two convolution neural networks with different structures are used for replacing the traditional convolution neural network structure. Therefore, the image feature diversity is guaranteed; the recognition precision is improved; and the network operation speed is accelerated.

Description

technical field [0001] The invention belongs to the technical field of intelligent traffic sign recognition, and relates to a traffic sign recognition method based on an asymmetric convolutional neural network, which is used to solve the problems of slow recognition speed and weak robustness in the traffic sign recognition problem. Background technique [0002] In recent years, the intelligent transportation system has been greatly developed. In 2010, Audi's unmanned automatic car traveled 12.42 kilometers and arrived at the top of Pikes Peak in the Rocky Mountains, marking the gradual maturity of the unmanned vehicle technology in the intelligent transportation system. Automatic traffic sign recognition is an important part of driverless car technology. [0003] Traffic sign recognition has direct real-world applications, such as safe driving, autonomous driving, scene understanding and signal detection, etc. The recognition of traffic signs is a restricted classification ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/582G06F18/214
Inventor 葛宏伟何鹏程孙亮谭贞刚
Owner DALIAN UNIV OF TECH
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