An image classification method combining darknet and capsulenet models

A classification method and model technology, applied in the field of image processing, can solve problems such as data imbalance and poor classification effect, and achieve the effect of improving learning ability, improving classification accuracy, and improving poor classification effect
CN111914904BActive Publication Date: 2022-07-01TAIYUAN UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
TAIYUAN UNIV OF TECH
Publication Date
2022-07-01

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Abstract

The invention discloses an image classification method integrating DarkNet and CapsuleNet models, which belongs to the technical field of image processing and solves the problem of poor classification effect due to unbalanced data. The technical scheme includes the following steps: constructing a DarkNet-Capsule network fusion classification model, realizing Define the loss function of the fusion classification model, input the image to be classified in the fusion classification model, use DarkNet for forward training, and extract the feature map of the target image; further process the feature map of the target image, and complete the error back propagation update through loss. parameters of the entire network; after multiple rounds of iterative learning, the fusion classification model is used to complete the image classification; in the field of image classification, the invention can further improve the classification accuracy when the data is unbalanced, and also lays a solid foundation for the research of machine vision. a more solid foundation.
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Description

technical field

[0001] The invention belongs to the technical field of image processing, in particular to an image classification method integrating DarkNet and CapsuleNet models. Background technique

[0002] Image classification is an important technology in the field of image processing. In recent years, with the development of deep learning, image classification technology has achieved tremendous development.

[0003] The DarkNet model is an improved image feature extraction model based on the residual concept in the YOLO detection framework. It not only has the property of the residual network to avoid network degradation, but also reduces the amount of parameters of the model. However, as the amount of training data decreases, the generalization performance of the model will deteriorate, resulting in a sharp drop in classification accuracy.

[0004] The CapsuleNet model proposes that convolutional neural networks use convolution kernels to extract image features, whic...

Claims

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