Traffic sign detection and recognition method based on convolutional neural network

A convolutional neural network, traffic sign technology, applied in neural learning methods, biological neural network models, character and pattern recognition, etc., can solve problems such as manual extraction, achieve good results, reduce a lot of time, and meet real-time effects.

Inactive Publication Date: 2017-09-01
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

At the same time, the traditional method for the recognition of traffic signs needs to manuall

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  • Traffic sign detection and recognition method based on convolutional neural network
  • Traffic sign detection and recognition method based on convolutional neural network
  • Traffic sign detection and recognition method based on convolutional neural network

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Embodiment Construction

[0036] The technical solutions in the embodiments of the present invention will be described clearly and in detail below in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0037] The technical scheme that the present invention solves the problems of the technologies described above is:

[0038] attached figure 1 It is a system structure block diagram of the present invention. It proceeds in the following steps:

[0039] Step 1. Use histogram equalization to perform histogram equalization on the R, G, and B channels of the input image respectively

[0040] Step 2, convert the RGB image preprocessed in step 1 into an HSV color model, then extract the target color information, and carry out preliminary segmentation in conjunction with 8 connected regions to obtain a region of interest (Region Of Interest, ROI)

[0041] Step 201, RGB image is converted into HS...

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Abstract

The invention discloses a traffic sign detection and recognition method based on a convolutional neural network, which belongs to the field of digital image processing and machine learning. The method comprises steps: firstly, an RGB image after pre-processing is converted to HSV color space, and a region of interest is obtained through threshold setting; and then, a two-classification convolutional neural network for distinguishing a traffic sign and a non-traffic sign is designed to judge whether the region of interest is a traffic sign. After the position of a traffic sign is obtained, the traffic sign recognition method based on the convolutional neural network is used, parameters such as the layer number and the characteristic pattern number of the convolutional neural network are adjusted, parameters in the network are learnt through a large amount of training samples, and classes of traffic signs at different positions are further recognized. An experiment shows that the method has good adaptability to deformation, partial occlusion and tilt and the like of the traffic sign, and good performance is presented in aspects of recognition effects and recognition efficiency.

Description

technical field [0001] The invention belongs to the field of digital image processing and machine learning, in particular to a traffic sign recognition method based on a convolutional neural network. Background technique [0002] As early as the early 1980s, some countries began to pay attention to traffic sign recognition, mainly using methods such as template matching, edge detection, and neural networks. The images of traffic sign detection and recognition come from vehicle camera equipment. Although traffic signs generally have obvious color and shape characteristics, due to the complex and changeable outdoor natural conditions, the collected images are easily affected by many unfavorable factors, such as weather Factors such as influence, background interference and object occlusion will directly affect the results of traffic sign detection and recognition. For the detection stage of traffic signs, the main difficulty is that the appearance of traffic signs changes dra...

Claims

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Application Information

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IPC IPC(8): G06K9/32G06K9/34G06K9/46G06K9/62G06N3/08
CPCG06N3/084G06V20/63G06V10/26G06V10/507G06V10/56G06F18/2415
Inventor 栾晓刘玲慧陈俊恒
Owner CHONGQING UNIV OF POSTS & TELECOMM
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