A wild animal monitoring and analyzing system and method based on a deep convolutional neural network

A wild animal and deep convolution technology, applied in the field of wild animal wireless monitoring and analysis system, can solve the problems of high labor intensity, difficulty in analyzing the current status and dynamic changes of wild animal resources, and long time-consuming manual analysis. Improve work efficiency, overcome rainy weather and poor image recognition rate at night, reduce delay and packet loss

Inactive Publication Date: 2019-05-31
BEIJING FORESTRY UNIVERSITY
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Problems solved by technology

[0005] In view of this, the purpose of the present invention is to propose a wild animal analysis monitoring analysis system and method based on a deep convolutional neural network, which solves the problem of high labor intensity, long time-consum

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  • A wild animal monitoring and analyzing system and method based on a deep convolutional neural network
  • A wild animal monitoring and analyzing system and method based on a deep convolutional neural network
  • A wild animal monitoring and analyzing system and method based on a deep convolutional neural network

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[0033] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0034] Aiming at the problems that the wireless monitoring and analysis system for wild animals consumes manpower and time, and the accuracy of the analysis results is low, the first aspect of the embodiment of the present invention provides a wireless monitoring and analysis system for wild animals based on a deep convolutional neural network, which mainly includes the following parts, the relationship between the parts is as figure 1 Shown:

[0035] The mobile image acquisition module includes several terminal nodes, which are respectively set in the monitoring area to collect image data of moving objects;

[0036] Optionally, the network structure of the terminal nodes is set as a mesh network, which has the character...

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Abstract

The invention discloses a wild animal wireless monitoring analysis system and method based on a deep convolutional neural network. Image data of a moving object is acquired and a database is established; image calibration and preprocessing are performed; the deep convolutional neural network is trained in combination with an Adam optimization algorithm; wild animals are detected and identified; the appearance time of wild animals is prolonged; statistical analysis is carried out on places and a model is established; the problem of high labor intensity in the traditional method is solved; Artificial analysis time consumption is long, it is difficult to analyze and obtain the current situation of wild animal resources and the accurate situation of dynamic changes due to a large amount of redundant information and false identification information is solved, researchers can remotely obtain the individual characteristics and population distribution situation of wild animals in a monitored area in real time, and the working efficiency and the accuracy of analysis results are improved.

Description

technical field [0001] The invention relates to the field of protection and monitoring of the ecological environment and animals and plants, in particular to a wireless monitoring and analysis system and method for wild animals based on a deep convolutional neural network. Background technique [0002] As an indispensable part of the ecological chain, wild animals play an important role in biodiversity. The protection of wild animals not only plays an important role in maintaining the balance and stability of the ecosystem, but also plays an important role in the survival and development of human beings. significance. Scientific and effective wildlife monitoring is the primary prerequisite for the protection of wildlife resources, which requires real-time acquisition of wild animal picture information and geographic location information during wildlife monitoring, and through these information to obtain the species information, Quantitative information and habitat condition...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06N3/04
Inventor 张军国冯文钊杜科刘文定
Owner BEIJING FORESTRY UNIVERSITY
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