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Image processing method based on binary quantization model

A quantitative model and image processing technology, applied in the field of image processing, can solve the problems of small storage memory of UAVs and inability to store full-precision models, etc., to achieve the effect of accelerating parameter convergence speed, small model size, and less memory occupation

Active Publication Date: 2021-07-23
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0003] In view of the above-mentioned deficiencies in the prior art, an image processing method based on a binary quantization model provided by the present invention solves the problem that the storage memory of the drone is too small to store the existing full-precision model

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  • Image processing method based on binary quantization model

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[0066] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0067] Such as figure 1 As shown, an image processing method based on a binarized quantization model includes the following steps:

[0068] S1. Preprocessing the image set to obtain the initial input data of each image;

[0069] Step S1 includes the following sub-steps:

[0070] S11. Perform normalization processing on each image in the image set to obtain normalized image pixel values;

[0071] The method for normalizin...

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Abstract

The invention discloses an image processing method based on a binarization quantization model, which belongs to the technical field of image processing, and comprises the following steps: S1, preprocessing an image set to obtain initial input data of each image; S2, constructing a binarization quantification model; S3, adopting the initial input data of each image to train the binaryzation quantification model to obtain a trained binaryzation quantification model; and S4, inputting the initial input data of one image into the trained binaryzation quantification model to obtain the boundary and the attribute of an object in the image, and completing the processing of the image; the problem that an existing full-precision model cannot be stored due to the fact that the storage memory of the unmanned aerial vehicle is very small is solved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image processing method based on a binarized quantization model. Background technique [0002] Convolutional Neural Networks (CNN) are widely used in the field of drones, especially in image processing. At present, researchers have proposed CNN algorithms for aerial images, such as image classification and target tracking tasks. However, there are some difficulties in applying the full-precision CNN model to the UAV field. The full-precision CNN model requires a lot of storage space and computing resources. Currently, the UAVs on the market have very small storage memory, and it is impossible to transplant the trained full-precision model. Moreover, the computing components carried by the UAV consume a lot of energy. , according to the existing battery capacity, the battery life of the drone will drop sharply. Contents of the invention [0003] In view of the abo...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06K9/62
CPCG06N3/084G06N3/045G06F18/214
Inventor 刘启和但毅周世杰张准董婉祾王钰涵严张豹
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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