Garden nursery stock intelligent detection and counting method based on UAV and convolutional neural network

A convolutional neural network and intelligent detection technology, applied in the field of intelligent monitoring of garden plants, can solve the problems of high detection cost, low feasibility, time-consuming and labor-consuming, etc., achieve high detection rate, low false detection rate, and save manpower The effect of material and financial resources

Pending Publication Date: 2020-11-03
湖南省建筑科学研究院有限责任公司
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  • Application Information

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Problems solved by technology

There are many studies on deep learning algorithms and UAV applications, but there are few reports on large-scale intelligent detection and counting of garden seedlings combined with UAVs and convolutional neural networks
[0004] The existing garden seedling detection and counting methods are mainly traditional manual field surveys, which adopt the method of counting while marking to avoid repetition and omission of manual counting. Although this method has high accuracy of counting results, it is time-consuming, labor-intensive and inefficient. Low
[0005] Through the above analysis, the problems and defects of the prior art are: (1) the traditional survey of garden seedlings adopts the method of manual on-the-spot investigation, which

Method used

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  • Garden nursery stock intelligent detection and counting method based on UAV and convolutional neural network
  • Garden nursery stock intelligent detection and counting method based on UAV and convolutional neural network
  • Garden nursery stock intelligent detection and counting method based on UAV and convolutional neural network

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Embodiment 1

[0099] The invention discloses a method for intelligent detection and counting of garden seedlings based on UAV and convolutional neural network, which belongs to the field of intelligent monitoring of garden plants. The method is suitable for multiple intelligent detection of garden seedlings in a wide range of different periods. The specific steps are:

[0100] 1. Research area

[0101]The present invention takes garden seedlings under natural light conditions as the research object, and the shooting location is located in Zhentou Town, Liuyang City, Yuhua District, Changsha City, Hunan Province. In order to improve the stability of drone shooting, try to ensure sufficient light, no wind or light wind.

[0102] 2. Data collection

[0103] The unmanned aerial vehicle used in the present invention is Mavic 2Pro, and instrument inspection includes hardware inspection, software inspection and signal inspection; Flight parameters include height 100 meters and speed 5m / s; Gather ...

Embodiment 2

[0134] 1), data analysis

[0135] In this embodiment, 90 pictures are randomly intercepted as training samples, and 10 pictures are taken as test samples. Firstly, perform operations such as image screening, image labeling, and data amplification on the intercepted sample images. After preprocessing, the size of the picture is 256×200×3, and the picture format is JPG; the picture labeling targets are divided into three categories, which are marked as A, B, and C; finally, data amplification is performed, that is, image translation, image rotation and Image scaling. Since the images of the selected three types of targets are similar in shape, different in color, and the sample features caused by brightness adjustment are the same, which leads to an increase in false detection rate, a decrease in the accuracy of detection and recognition, or even failure to detect the result, import the marked json file into the training set and test set. In this embodiment, the training batc...

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Abstract

The invention belongs to the technical field of garden plant intelligent monitoring, and discloses a garden nursery stock intelligent detection counting method based on UAV and a convolutional neuralnetwork, and the method comprises the steps: collecting garden seedling image data through an unmanned plane under the conditions of sufficient illumination, no wind or breeze; screening the acquiredimage data according to the picture definition, and deleting an unclear image or an image with too dark light; cutting the image data, and unifying image color representation at the same time; preprocessing the image data; utlizing a Mask RCNN deep learning framework based on image segmentation to construct a convolutional neural network oriented to garden nursery stock intelligent detection and counting; and carrying out garden nursery stock intelligent detection and counting by utilizing the constructed convolutional neural network for garden nursery stock intelligent detection and counting.According to the invention, unmanned aerial vehicle remote sensing and the convolutional neural network are combined to carry out intelligent detection and counting of garden nursery stocks, and theblank of domestic intelligent detection and counting of garden nursery stocks can be filled.

Description

technical field [0001] The invention belongs to the technical field of intelligent monitoring of garden plants, in particular to an intelligent detection and counting method for garden seedlings based on a UAV and a convolutional neural network. Background technique [0002] Landscaping plants are rich in tree species and are the main components of urban greening systems. With the development of urbanization, the area of ​​garden seedling bases in my country has been expanding year by year. However, there is no accurate estimate of the distribution and number of garden seedlings. Due to the lack of sufficient attention, the intelligent detection of garden seedlings has been neglected for a long time, and there are few reports on the intelligent detection and counting of garden seedlings at home and abroad. In recent years, the maturity of drone technology and deep learning algorithms has provided conditions for the detection and counting of garden seedlings. Therefore, it i...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06M11/00G06N3/04G06N3/08
CPCG06N3/08G06M11/00G06V20/10G06N3/045G06F18/214
Inventor 张国珍钟雅婷刘丽娜吴鹏飞文古罗鹏
Owner 湖南省建筑科学研究院有限责任公司
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