Trunk texture recognition method based on four-channel convolutional neural network

A convolutional neural network and recognition method technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as difficult to accurately identify, high requirements for camera equipment, and difficult to identify tree species, so as to improve performance, enhance The effect of robustness and superior performance

Inactive Publication Date: 2020-01-14
ZHEJIANG UNIV OF TECH
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Problems solved by technology

For example, it is difficult to identify tree species when the flowers and leaves are gone in winter, and the textur

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  • Trunk texture recognition method based on four-channel convolutional neural network
  • Trunk texture recognition method based on four-channel convolutional neural network
  • Trunk texture recognition method based on four-channel convolutional neural network

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Embodiment Construction

[0049] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0050] refer to Figure 1 ~ Figure 4 , a tree trunk texture recognition method based on a four-channel convolutional neural network, comprising the following steps:

[0051] Step 1: Collect tree trunk image data

[0052] The trunk images come from different positions of the trunk. The number of pictures of each kind of trunk reaches 500, and approximately 50 trees are selected for each kind of tree. Each tree has to be photographed before, after, left, right, and at different heights to ensure the same position Only one photo is taken, about 10 photos are taken for each tree;

[0053] Step 2: Grayscale the original image

[0054] The image grayscale adopts the weighted average method, and the three components are weighted and averaged with different weights. Since the human eye is most sensitive to green and the least ...

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Abstract

A trunk texture recognition method based on a four-channel convolutional neural network comprises the following steps: S1, collecting a trunk image data set to ensure the integrity of an image; s2, preprocessing the image; and S3, training a multi-classifier by using the four-channel convolutional neural network. According to the invention, richer image feature information can be obtained, features with higher discrimination can be learned automatically, and the classification precision and robustness of the classifier obtained by training are higher.

Description

technical field [0001] The invention belongs to a tree trunk texture recognition method based on a four-channel convolutional neural network, and relates to a convolutional neural network, digital image processing and image classification methods. Background technique [0002] In recent years, with the development of China, people's calls for ecological protection have become higher and higher, and the protection of plant diversity has become more and more important. How to obtain tree information through simple pictures, such as species and growth habits, is one of the important methods to simplify tree species identification, and it is of great significance for protecting plant diversity and strengthening agricultural and forestry information management. [0003] At present, tree identification and classification are mostly done manually. However, there are so many types of trees that it is impossible for any botanist to remember the names of all tree species. For people w...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/047G06N3/045G06F18/241G06F18/2415
Inventor 宣琦刘文成翔云
Owner ZHEJIANG UNIV OF TECH
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