A traffic sign deep learning mode identification method

A traffic sign and pattern recognition technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as low accuracy and redundant parameters, improve accuracy, eliminate overfitting, and simplify structural parameters. Effect

Inactive Publication Date: 2018-10-26
YANSHAN UNIV
View PDF5 Cites 69 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] To sum up, there is no effective solution to the problems of low accurac...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A traffic sign deep learning mode identification method
  • A traffic sign deep learning mode identification method
  • A traffic sign deep learning mode identification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0066] The present invention will be further described below in conjunction with specific examples and accompanying drawings.

[0067] Feature representation refers to the activation value of an image in a certain layer of CNN, and the size of feature representation should be slowly reduced in CNN. High-dimensional features are easier to process, and training on high-dimensional features is faster and easier to converge. Spatial aggregation is performed on low-dimensional embedding space, and the loss is not very large. The explanation for this is that there is a strong correlation between adjacent neurons, and the information is redundant.

[0068] Balanced network depth and width. If the width and depth are appropriate, the network can have a relatively balanced computing budget when applied to distributed systems.

[0069] figure 1 For a flow chart of the present invention, it comprises the following steps:

[0070] Step 1, input traffic sign images as test samples and...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a traffic sign deep learning mode identification method. The method includes the following steps: pretreatment is performed on a test sample and, a training sample of a trafficsign image, a residual deep learning network based on the multiscale characteristic weight operation fusion of a convolution nerve network is designed, the characteristic is automatically extracted by the network in order to eliminate the artificial trace mainly through training, and identification is performed on the traffic sign test sample by using a deep color characteristic trained classifier. The image characteristic weight operation combining with the multiscale convolution fusion network is applied to the traffic mode identification technology, the training efficiency of the network is substantially improved, and the problem that the accuracy and the real-time performance are not ideal, the network structure is complex, the training time is long, the stability and the robustness are bad encountered in the traffic sign identification method can be effectively solved. The image recognition accuracy of the trained network in 43 kinds of traffic signs reaches 97%.

Description

technical field [0001] The invention relates to the field of traffic sign recognition, in particular to a method for deep network model image pattern recognition. Background technique [0002] During the driving process of the car, the pattern recognition of the traffic sign image is an important part of the intelligent traffic control system. In this link, the recognition accuracy of different traffic command states plays a vital role in the subsequent driving control effect of the car. Different types of traffic data in the road scene environment are collected by camera for identification, and the type of current traffic instruction status is judged, which is fed back to the control mechanism of the car, and the safe driving of the car is guaranteed by controlling the operation of the executive body, and finally the smart car meets the requirements. The production standard of unmanned driving, so we should seek and study the pattern recognition method of high-precision tra...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V20/582G06V10/56G06F18/214G06F18/24
Inventor 张秀玲张逞逞周凯旋
Owner YANSHAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap