Vehicle ranging method based on deep neural network

A technology of deep neural network and ranging method, which is applied in the direction of neural learning method, biological neural network model, measuring distance, etc. It can solve the problems of cumbersome installation of monocular camera and insufficient ranging accuracy, so as to improve the accuracy rate and training time , Solve the problem that the distance measurement accuracy is not high enough, and solve the cumbersome effect

Inactive Publication Date: 2019-07-30
KUNSHAN BRANCH INST OF MICROELECTRONICS OF CHINESE ACADEMY OF SCI
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Problems solved by technology

[0007] In view of the above-mentioned problems, the present invention mainly solves the existing problem that the distance measurement accuracy is not high enough due to the cumbersome installation of the monocular camera

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  • Vehicle ranging method based on deep neural network
  • Vehicle ranging method based on deep neural network
  • Vehicle ranging method based on deep neural network

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Embodiment Construction

[0040] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0041] Such as figure 1 As shown, the present invention provides a kind of vehicle ranging method based on deep neural network, comprising:

[0042] S1. Collect an image of the target vehicle, and extract the image coordinates of the target vehicle;

[0043] S2. Establish a prediction network, load a training sample containing the image coordinates of the target vehicle, and train the sample through a deep neural network model to ...

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Abstract

The invention discloses a vehicle ranging method based on deep neural network. The vehicle ranging method comprises the following steps: S1, acquiring target vehicle images and extracting image coordinates of the target vehicles; S2, building predication network, loading training sample including the image coordinates of the target vehicles, and training the samples through a deep neural network model to calculate network model parameters of the predication network; and S3, using the images of the target vehicles as input of the trained predication network, and predicating a target vehicle distance through a forward propagation algorithm. The vehicle ranging method disclosed by the invention has the advantages that the ground height and pitch data of a camera are not needed to be known inadvance, so that the total identification correction ratio is increased, and the training time is lengthened; a ranging geometrical model is not needed to be built in advance, so that the problem of lower fitting degree of manual modeling is solved, the trouble caused by conventional geometrical ranging is solved, and the problem of low ranging accuracy obtained by firstly modeling and subsequently predicting is solved.

Description

technical field [0001] The invention relates to the field of the Internet of Vehicles, in particular to a vehicle ranging method based on a deep neural network. Background technique [0002] Maintaining the automation, comfort and safety of driving is the goal that smart cars have been pursuing. In order to ensure driving safety, it is very important to develop automobile anti-collision technology. Countries all over the world have invested a lot of manpower, material and financial resources in the research and development of automobile anti-collision technology. The core of this technology is vehicle ranging technology. [0003] The car must rely on the sensor to measure the distance of the vehicle in front, and quickly feed back to the car, in order to avoid the driver's fatigue, negligence, A traffic accident caused by misjudgment. According to Mercedes-Benz's research on various traffic accidents, it is shown that if the driver can realize the danger of an accident o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C3/00G01C11/04G06N3/04G06N3/08
CPCG01C3/00G01C11/04G06N3/08G06N3/045
Inventor 郭佳奇李庆梁艳菊常嘉义
Owner KUNSHAN BRANCH INST OF MICROELECTRONICS OF CHINESE ACADEMY OF SCI
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