A system and method for self-adaptive annual observation of biological activity in reef areas
A biological activity and self-adaptive technology, applied in the field of image data processing and data recognition, can solve problems such as the influence of underwater biological habits, achieve strategic observation, reduce image pixel information, and improve computing efficiency
Active Publication Date: 2022-02-11
山东省海洋资源与环境研究院
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[0004] The present invention provides a system and method for self-adaptive annual observation of the law of biological activities in the reef area to solve the problem that the existing underwater observation network needs the assistance of high-intensity lights and easily has a great impact on the habits of underwater organisms, so as to realize the The location of underwater organisms can be identified and observed with less impact on underwater organisms
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[0072] Below in conjunction with accompanying drawing and embodiment the technical scheme of the present application is elaborated:
[0073] This application proposes a system and method for self-adaptive annual observation of the law of biological activities in reef areas. Aiming at the problem that the existing underwater observation network needs the assistance of high-intensity light, which is likely to have a great impact on the habits of underwater organisms, a starlight-based The observation system of the first-class camera, the image data processing method that does not require the clarity of underwater images, to identify underwater organisms and avoid the impact of observation on biological habits.
[0074] refer to figure 1 , a system for self-adaptive annual observation of biological activity in the reef area includes an observation network 1 and a platform terminal 2 .
[0075] The observation network 1 includes: a camera 10, a temperature sensor 20, a salinity s...
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The present invention is a method for self-adaptive annual observation of the law of biological activities in reef areas, comprising the following steps: intermittently collecting various types of data; processing and storing various types of data; and simultaneously using a multi-layer feature recognition network to perform Recognition processing, identifying biological species; packaging the recognition processing results and time information, and storing them; at the same time, packaging the recognition processing results and time information and displaying them on the platform; managing the recognition processing results. Since the multi-layer feature recognition network is used to identify and process the image, the computing efficiency can be improved, and the technical solution of the present invention can realize the recognition of low-quality images without using strong light irradiation, and will not affect the habits of underwater organisms, and The use of interruption management strategy can reduce the observation power consumption and observation cost.
Description
technical field [0001] The application relates to the fields of image data processing and data identification, and specifically relates to a system and method for adaptive annual observation of biological activity rules in reef areas. Background technique [0002] Coral reefs provide a natural habitat and protective barrier for many plants and animals. The growth status, species richness and activity rules of the organisms in the reef area directly reflect the health of the coral reef ecosystem and the degree of biodiversity in the water area. [0003] When the existing methods of observing biological activities in reef areas use underwater monitoring equipment for video collection, the light intensity used is very strong, which has a great impact on biological habits, and the observation network is continuous observation, which consumes a lot of power and costs a lot. high cost. Moreover, due to low video resolution, extremely complex underwater environment, high biodiver...
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IPC IPC(8): G06V10/764G06V10/774G06V10/80G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24G06F18/253G06F18/214
Inventor 王斐张明亮韩慧宗汪健平刘京熙于广磊崔国平姜海滨王腾腾
Owner 山东省海洋资源与环境研究院