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Vehicle attitude estimation method, terminal equipment and storage medium

A vehicle attitude and vehicle technology, applied in the field of vehicle detection, can solve the problems of not being able to recognize the specific attitude of the vehicle, not providing the vehicle attitude, etc., to achieve the effects of good generalization, reduced consumption, and strong application

Active Publication Date: 2021-11-02
XIAMEN MEIYA PICO INFORMATION
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, current vehicle detection systems still have limitations, such as only detecting and recognizing vehicles, but not identifying specific poses of vehicles
Currently the most widely used Pascal visual object classes (VOC) and Cityscapes databases do not provide annotation data for vehicle poses

Method used

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  • Vehicle attitude estimation method, terminal equipment and storage medium
  • Vehicle attitude estimation method, terminal equipment and storage medium

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0026] Embodiments of the present invention provide a vehicle attitude estimation method, such as figure 1 As shown, the method includes the following steps:

[0027] S1: Collect an image containing a vehicle, and label the pose corresponding to the vehicle in the image and the bounding box of the vehicle target, and form the training set with the labeled images.

[0028] S2: Build a vehicle attitude estimation model based on the YOLOv2 network, and train the vehicle attitude estimation model through the training set.

[0029] Since images at different resolutions pass through the model, the amount of information in the output feature map is different, so in order to obtain a larger and detailed feature map, in this embodiment, the resolution of the input layer in the YOLOv2 network is set to 768×384 and 1024×512, corresponding to construct two vehicle pose estimation models.

[0030] The vehicle pose estimation model extracts multi-dimensional feature maps from input images...

Embodiment 2

[0049] The present invention also provides a vehicle attitude estimation terminal device, including a memory, a processor, and a computer program stored in the memory and operable on the processor, and the present invention is realized when the processor executes the computer program Steps in the above method embodiment of Embodiment 1.

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Abstract

The invention relates to a vehicle attitude estimation method, terminal equipment and a storage medium, and the method comprises the steps: S1, collecting an image containing a vehicle, marking the attitude corresponding to the vehicle in the image and a bounding box of a vehicle target, and enabling the marked image to form a training set; S2, constructing a vehicle attitude estimation model based on a YOLOv2 network, and training the vehicle attitude estimation model through the training set; and S3, estimating the vehicle attitude and the vehicle target through the trained vehicle attitude estimation model. The invention can be fused with a detection task of an intelligent traffic system into a backbone network, has good generalization, does not need to additionally design a network structure responsible for vehicle attitude estimation, can realize the vehicle attitude estimation only by modifying the input and output of a detector, has strong application in a real scene, and reduces the consumption of hardware facilities.

Description

technical field [0001] The invention relates to the field of vehicle detection, in particular to a vehicle attitude estimation method, terminal equipment and a storage medium. Background technique [0002] With the development of the current intelligent transportation system, the number of vehicles has increased sharply. License plate recognition and vehicle type detection have become the main components of the intelligent transportation system. The application of the mark detection algorithm based on the monocular camera in the vehicle detector has made great progress. However, current vehicle detection systems still have limitations, such as they can only detect and recognize vehicles, but cannot identify specific poses of vehicles. Currently the most widely used Pascal visual object classes (VOC) and Cityscapes databases do not provide annotation data for vehicle poses. The attitude information of the vehicle itself can help to obtain the specific orientation of the vehi...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/214
Inventor 陈德意吴婷婷赵建强高志鹏张辉极杜新胜李国庆
Owner XIAMEN MEIYA PICO INFORMATION
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