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A traffic sign detection method based on improved yolof model

A detection method and signage technology, applied in neural learning methods, biological neural network models, character and pattern recognition, etc., can solve the problems of traffic sign algorithm missed detection and false detection, so as to improve detection speed and solve missed detection and false detection problems, and the effect of reducing collection costs

Active Publication Date: 2022-05-31
BEIJING UNION UNIVERSITY
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0005] However, traffic sign detection in unmanned driving environments in complex scenes will be disturbed by factors such as lighting changes, bad weather, and other graphics similar to traffic signs. The above traffic sign algorithms will all have the problem of missed detection and false detection. It is urgent to provide a method sufficient to solve the above problems

Method used

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  • A traffic sign detection method based on improved yolof model
  • A traffic sign detection method based on improved yolof model
  • A traffic sign detection method based on improved yolof model

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Experimental program
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Embodiment Construction

[0062]

[0065]

[0068] t

[0069] t

[0070] Wherein, σ is the sigmoid activation function, is the real number domain space of dimension is, is the dimension

[0072]

[0078] (4) Use 4 consecutive hole residual units to cope with different target sizes.

[0080] The network module uses a cross-entropy loss function:

[0081]

[0082] where α and γ are balance factors.

[0083] After many iterations, when the loss value tends to be stable, it is saved as a training model.

[0087] (1) Begin. Input the pictures in the dataset;

[0092] (6) Output the detection result.

[0097] The basic principles, main features and advantages of the present invention have been shown and described above. Technicians in the industry should

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Abstract

The invention discloses a traffic sign detection method based on an improved YOLOF model, comprising: augmenting collected traffic sign samples, inputting the augmented data set into an improved YOLOF network model for training; The improved YOLOF network model is used for detection, and the detection is ended if the detection results are qualified. The sample set is augmented by various augmentation methods, which reduces the collection cost and obtains a large number of samples, and improves the robustness of the model and the detection performance of traffic signs in complex unmanned scenarios. The improved YOLOF model detects traffic signs, which not only solves the problem of missed detection and false detection of traffic signs in complex scenes, but also improves the detection speed of traffic signs in unmanned environments.

Description

A Traffic Sign Detection Method Based on Improved YOLOF Model technical field The present invention relates to the technical field of automatic driving control, particularly relate to a kind of traffic based on improved YOLOF model Sign detection method. Background technique [0002] Object detection is one of the most important tasks in the field of computer vision, and is usually applied in the field of autonomous driving. automatic As a direction of future technological development, driving has become a research hotspot in recent years. Traffic sign detection is the leading edge of autonomous driving An important part of the domain perception module, it can automatically identify and label traffic signs, and transmit the results to the autonomous driving decision-making model. block to ensure that the vehicle can drive safely in accordance with the traffic rules. [0003] Before the advent of deep neural networks, traffic sign detection usually used feature extrac...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/58G06V10/42G06V10/774G06V10/82G06K9/62G06T3/60G06T5/00G06N3/04G06N3/08
CPCG06N3/084G06T3/60G06T5/007G06N3/047G06N3/048G06N3/045G06F18/2414G06F18/214
Inventor 鲍泓徐歆恺梁天骄吴祉璇潘卫国徐成
Owner BEIJING UNION UNIVERSITY
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