Image Classification Model and Its Classification Method Based on Hybrid Depth Separable Dilated Convolution

A classification model and deep technology, applied in the field of image processing, can solve the problems of losing the detailed information of features, reducing the classification accuracy, difficult to optimize, etc., to achieve the effect of supplementing the context information, improving the classification accuracy, and improving the expression ability.
CN112258431BActive Publication Date: 2021-07-20成都东方天呈智能科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
成都东方天呈智能科技有限公司
Publication Date
2021-07-20

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Abstract

The invention discloses an image classification model based on hybrid depth-separable dilation convolution, the construction process includes: from front to back, a depth-separable dilation convolution layer, a feature connection layer, a convolution layer, a batch normalization layer and a corrected linear unit The layer is packaged into a mixed depth separable dilated convolution module; from front to back, the convolution layer, batch normalization layer, corrected linear unit layer, mixed depth separable dilated convolution module, maximum pooling layer, flattening layer, The random deactivation layer and the fully connected layer encapsulate the backbone network of the deep neural network; the parameter weights of the backbone network are randomly initialized, and the number of iterations and the momentum parameters of the batch normalization layer are preset; the network model is optimized by the stochastic gradient descent method Parameters, iterative calculations are repeated until the loss value converges, and the optimal network model is obtained. Through the above solution, the present invention has the advantages of simple structure, less calculation workload, and accurate classification.
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Description

technical field

[0001] The invention relates to the technical field of image processing, in particular to an image classification model and a classification method based on hybrid depth separable dilated convolution. Background technique

[0002] Images can objectively display natural things and are an important resource for people to understand the world, and technicians can obtain useful information and develop related algorithms by analyzing images. Image classification belongs to the direction of computer vision and is widely used in medical, food safety and other fields.

[0003] At present, the main idea of ​​the image classification algorithm in the prior art is to assign corresponding labels to the image sets that need to be classified. For a computer, an image is a pixel matrix, which uses relevant algorithm technology to extract effective information in the pixel matrix. This is different from the way people perceive images. Traditional image classification algor...

Claims

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