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Traffic sign detection and recognition method based on a self-built neural network

A traffic sign and neural network technology, applied in the field of machine learning and deep learning, can solve the problems of time-consuming and labor-intensive, prone to human errors, etc., and achieve the effect of reliable recognition, fast and accurate recognition and classification

Inactive Publication Date: 2018-07-20
GUILIN UNIV OF ELECTRONIC TECH
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AI Technical Summary

Problems solved by technology

Since every road must be regularly inspected for missing or damaged signs, these tasks have been done in the past by driving a car along the road and manually recording the observed information, which is time-consuming and labor-intensive, and is prone to human errors

Method used

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  • Traffic sign detection and recognition method based on a self-built neural network
  • Traffic sign detection and recognition method based on a self-built neural network
  • Traffic sign detection and recognition method based on a self-built neural network

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Embodiment Construction

[0057] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings, but the present invention is not limited.

[0058] figure 1 , figure 2 It shows a traffic sign detection and recognition method based on a self-built neural network. According to the captured image, the color segmentation in digital image theory is used to obtain the non-real area of ​​interest of the traffic sign in the picture, and the real area of ​​interest is obtained by using the SVM classifier. The region of interest, and then put the real region of interest into the self-built convolutional neural network for identification and classification, including the following steps:

[0059] (1) The on-board system will take pictures or videos of road signs. If it is a picture, it will directly perform color conversion. If it is a video format, it will draw frames from the video and convert the RGB image to the HSI color model. HSI respectively...

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Abstract

The invention, which belongs to the technical field of machine learning and deep learning, discloses a traffic sign detection and recognition method based on a self-built neural network. According tothe method, on the basis of a shot image, a non-real area of interest of a traffic sign in the image is obtained by using color segmentation in a digital image theory; a real area of interest is obtained by using an SVM classifier; and then the real area of interest is inputted into a self-built convolutional neural network for identification and classification. Therefore, the usage state of the traffic sign can be identified and classified quickly and accurately; the real-time requirement of quick, reliable, and accurate identification is realized; and the time and effort are saved.

Description

technical field [0001] The invention relates to the technical field of machine learning and deep learning, in particular to a traffic sign detection and recognition method based on a self-built neural network. Background technique [0002] As of the end of May 2017, the number of motor vehicles in the country has reached 300 million, and the total number of motor vehicle drivers has reached 350 million. While automobiles bring convenience to people and promote the development of road traffic, frequent traffic accidents also bring great harm to today's society. There are many reasons for traffic accidents, one of which is the driver's irregular operation, such as fatigue driving, self-negligence, etc. In order to improve driving safety, help drivers regulate operations and even perform correct operations instead of drivers, Advanced Driver Assistance Systems (Advanced Driver Assistance Systems, ADAS) came into being. Automatic traffic sign detection and recognition system (A...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/582G06N3/048G06N3/045G06F18/2411G06F18/214
Inventor 黄知超李栋王斌
Owner GUILIN UNIV OF ELECTRONIC TECH
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