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Traffic sign identifying method and traffic sign identifying system based on cascading deep learning

A traffic sign recognition, deep learning technology, applied in character and pattern recognition, instruments, computer parts and other directions, can solve the problems of insufficient sample space utilization, inability to effectively explore sample supervision information, etc., to improve detection and recognition accuracy, Make up for the lack of supervision

Active Publication Date: 2016-10-12
INST OF INFORMATION ENG CAS
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AI Technical Summary

Problems solved by technology

Therefore, the simple feature extraction method cannot adapt, so a relatively complex classifier such as a neural network is used for identification. However, the existing traffic detection and marking methods based on neural networks do not make full use of the sample space and cannot effectively explore the supervision of samples. information

Method used

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  • Traffic sign identifying method and traffic sign identifying system based on cascading deep learning
  • Traffic sign identifying method and traffic sign identifying system based on cascading deep learning
  • Traffic sign identifying method and traffic sign identifying system based on cascading deep learning

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Experimental program
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Effect test

Embodiment

[0064] Based on the above method, the inventor has done relevant experimental verification. The experiment is based on the evaluation of 43 traffic signs on the German Traffic Sign Recognition Benchmark (GTSRB), a German traffic sign standard database.

[0065] First, the classic convolutional neural network LeNet-5, VGG-Net, and AlexNet were used as the basic convolutional network of the framework, and then four sets of experiments were carried out, including only positive samples, adding random negative samples on the basis of positive samples, and adding The case of negative supervised samples. The traffic sign recognition rate in the GTSRB database is shown in the following table:

[0066] basic network

[0067] From the above comparative experimental data, it can be found that the recognition rate of traffic signs can be improved by about 1.5% by adopting the present invention.

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Abstract

The invention provides a traffic sign identifying method and a traffic sign identifying system based on cascading deep learning. By introducing a cascading convolutional neural network idea, expanding target sign sample space, and adding more samples having supervision functions, identification of traffic signs is additionally provided with more apriori information, and then sample space used for training of an identification device has the higher supervision function. The traffic sign identifying method is advantageous in that by fully using the various characteristic information of the signs, the deficiency of the conventional traffic sign identification based on the neural networks is remedied, and therefore the detection rate and the identification rate of the signs are improved.

Description

technical field [0001] The present invention relates to a computer vision and machine learning technology, which belongs to the method of target detection and recognition, in particular to a traffic sign recognition method and system based on cascade deep learning, which is suitable for the detection and recognition of traffic signs in images or videos . Background technique [0002] Traffic sign recognition is one of the most important modules of the current intelligent assisted driving system. During daily driving, drivers often ignore traffic signs due to obstacles or lack of energy, violate traffic rules and even cause car accidents. Therefore, traffic sign recognition is not just A technology is a major event related to the national economy and people's livelihood. [0003] Traffic sign recognition technology is a branch of target recognition, but it is different from traditional target recognition, but for a specific field of target recognition. At present, there are...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/582G06V2201/09G06F18/214G06F18/24G06F18/25
Inventor 葛仕明解凯旋罗朝叶奇挺孙利民
Owner INST OF INFORMATION ENG CAS
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