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A fruit image classification method and device based on a neural network and transfer learning

A transfer learning and neural network technology, which is applied in the field of fruit image classification methods and devices, can solve problems such as inability to meet the needs of classification, and achieve the effects of reducing time costs, high reliability, and improving recognition and classification efficiency.

Inactive Publication Date: 2019-01-25
SOUTH CHINA AGRI UNIV
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Problems solved by technology

At the same time, due to the explosive growth of massive unlabeled RGB images on the Internet, the BP algorithm can no longer meet the needs of massive unlabeled RGB image classification.

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  • A fruit image classification method and device based on a neural network and transfer learning
  • A fruit image classification method and device based on a neural network and transfer learning
  • A fruit image classification method and device based on a neural network and transfer learning

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Embodiment Construction

[0057]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.

[0058] The purpose of the present invention is to solve the problem of classifying input images of any size, and to overcome the adverse effects caused by the influence of image definition, brightness, contrast and other aspects in the existing RGB image classification method. The invention proposes a fruit image classification method based on neural network and transfer learning, and can classify images based on deep learning.

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Abstract

The invention relates to a fruit image classification method and device based on a neural network and transfer learning. The method comprises the following steps: preprocessing the image to be classified, preprocessing the image, and then combining the SMOTE algorithm for data enhancement; Carrying out BN batch normalization operation on pixel points of the data enhanced image to make the pixel points conform to a normal distribution; The pixel points of the normal distribution are inputted into a fruit image classification model based on neural network and transfer learning, and the classification result of the image is outputted. This method can be used to classify the input images of arbitrary size. The method relies on the classification model of fruit image based on neural network andtransfer learning, which improves the efficiency of recognition and classification, reduces a lot of time cost and has high reliability.

Description

technical field [0001] The invention relates to the field of deep learning, in particular to a method and device for classifying fruit images based on neural network and transfer learning. Background technique [0002] At present, convolutional neural networks are widely used in image analysis application scenarios to complete specific tasks, such as classification, detection, recognition, etc., by training models on a given data set. Deep learning is a new field in image analysis research, and its purpose is to simulate the human brain for autonomous learning. Deep learning is a data-driven model that can simulate the visual mechanism of the human brain to automatically learn the abstract features of each level of data, so as to better reflect the essential characteristics of the data. With the continuous increase of the image database and the increasing complexity, the extraction of RGB image features by a single machine is far from meeting the demand, and the use of data...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/24
Inventor 王卫星黄仲强姜晟赖俊桂韩清春
Owner SOUTH CHINA AGRI UNIV
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