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Method for generating traffic sign recognition model

A traffic sign recognition and model technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve the problem of poor detection effect of vehicle sensors on road traffic signs, improve detection effect and weaken background The effect of interference and strengthening feature information

Inactive Publication Date: 2021-12-24
SHANGHAI INST OF TECH
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AI Technical Summary

Problems solved by technology

Due to the influence of lighting, occlusion and the presence of a large number of similar small targets in the background, the detection effect of vehicle sensors on road traffic signs is not good, which leads to many traffic accidents during the trial operation of unmanned vehicles

Method used

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  • Method for generating traffic sign recognition model
  • Method for generating traffic sign recognition model
  • Method for generating traffic sign recognition model

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Embodiment Construction

[0027] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0028] Such as Figure 1~3 As shown, the present invention provides a method for generating a traffic sign recognition model, comprising:

[0029] Step S1, constructing a traffic sign recognition model, including: using a weighted bidirectional feature pyramid network instead of a path aggregation network, the weighted bidirectional feature pyramid network includes multiple bidirectional paths, each bidirectional path is regarded as a feature network layer, and the cross-feature The connection of the network layer and the weight adjustment of the transferred feature map;

[0030] Here, a weighted two-way feature pyramid network with more balanced accuracy and efficiency is used instead of the path aggregation network, and each tw...

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Abstract

According to the traffic sign recognition method provided by the invention, the detection effect on the road traffic sign is remarkably improved, and the requirement on real-time performance is met. A weighted bidirectional feature pyramid network with more balanced accuracy and efficiency is adopted to replace a path aggregation network, and channel features of road traffic signs are better fused. Secondly, common convolution is replaced by the cavity convolution, and the cavity convolution is combined with the space pooling pyramid, so that the receptive field is further expanded. Meanwhile, the detection scale is increased to four types, and the small target detection effect is improved; and random cutting is added in a data enhancement technology, so that the model learns more detail features. Finally, digital image operation technique is used to increase the number of instances for low precision categories.

Description

technical field [0001] The invention relates to a generation method of a traffic sign recognition model. Background technique [0002] Unmanned driving technology has attracted widespread attention from the society because of its advantages such as safety and efficiency. Due to the influence of lighting, occlusion, and the presence of a large number of similar small targets in the background, the detection effect of on-board sensors on road traffic signs is not good, which led to many traffic accidents during the trial operation of driverless cars. Contents of the invention [0003] The purpose of the present invention is to provide a method for generating a traffic sign recognition model. [0004] In order to solve the above problems, the present invention provides a method for generating a traffic sign recognition model, comprising: [0005] Building a traffic sign recognition model, including: adopting a weighted two-way feature pyramid network instead of a path aggre...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/2431G06F18/253
Inventor 李文举张干储王慧沙利业
Owner SHANGHAI INST OF TECH
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