Deep-learning-based high-precision traffic sign detection method and system

A traffic sign, deep learning technology, applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve the problem of low accuracy of traffic signs, improve the accuracy and detection accuracy, improve the accuracy, and increase the proportion Effect
CN106909886AActive Publication Date: 2017-06-30CHINA UNIV OF PETROLEUM (EAST CHINA)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA UNIV OF PETROLEUM (EAST CHINA)
Publication Date
2017-06-30

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Abstract

The invention discloses a deep-learning-based high-precision traffic sign detection method and system. On the basis of combination of the deep learning technology and the high-precision traffic sign detection technology, training is carried out on an SSD network and a convolution neural network; a traffic sign characteristic after overlapped cutting based on a proportion in a video stream is extracted by using the trained SSD network; according to the traffic sign characteristic extracted by the SSD network, the characteristic of the traffic sign characteristic is extracted by using the trained convolution neural network; and the extracted characteristic of the traffic sign characteristic matches characteristics of positive and negative type traffic signs in a traffic sign image detection database and the positive type traffic signs are kept, thereby obtaining a high-precision traffic sign matching screening result. Therefore, the accuracy rate of the high-precision traffic sign detection can be improved effectively.
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Description

technical field

[0001] The invention belongs to the field of artificial intelligence, and in particular relates to a high-precision traffic sign detection method and system based on deep learning. Background technique

[0002] Deep learning is currently the highest level of machine learning development. As a method of deep learning, convolutional neural network has good results in object recognition, image processing and other fields. For feature extraction, the convolutional neural network has the advantage of automatically learning image features, reducing manual intervention and extracting high-quality features, thus laying a solid foundation for improving the accuracy of image matching.

[0003] Since the deep learning method does not do enough targeted detail processing in the image preprocessing module, if the image is too large and the proportion of the target object is too small, the expected high-precision detection results may not be achieved. In order to improve t...

Claims

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