Ecological biological recognition method based on Light-Head R-CNN algorithm
A biometric and ecological technology, applied in the field of biometrics, can solve the problems of low biometric image recognition speed and recognition accuracy, and achieve the effect of improving speed and accuracy, and improving speed and accuracy.
Pending Publication Date: 2022-07-01
澜途集思生态科技集团有限公司
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[0003] However, the number of aquatic organisms is large, and there are many biological images collected. The existing biometric recognition methods are not high in recognition speed and recognition accuracy for many biological images.
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[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments.
[0026] refer to Figure 1-2 , an ecological biometric identification method based on the Light-Head R-CNN algorithm, including the following steps:
[0027] S1 collects ecological biological features, collects and categorizes the collected ecological features, and establishes a distributed ecological feature database;
[0028] S2 initiates an ecological biometric identification request, and collects biological image data in the ecological environment according to the request;
[0029] S3 analyzes and processes the collected biological image data, and stores the analyzed and processed biological image data;
[0030] S4 performs target dete...
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The invention discloses an ecological biological recognition method based on a Light-HeadR-CNN algorithm, and the method comprises the following steps: collecting ecological biological characteristics, carrying out the collection and classification of the collected ecological characteristics, and building a distributed ecological characteristic database; initiating an ecological biological recognition request, and collecting biological image data in the ecological environment according to the request; analyzing and processing the collected biological image data, and storing the analyzed and processed biological image data; performing target detection on the acquired biological image data through a Light-HeadR-CNN (Convolutional Neural Network) algorithm; and comparing and identifying the obtained biological image data needing to be identified with the feature data in the distributed ecological feature database. By setting the Light-HeadR-CNN algorithm, the speed and accuracy of target detection can be improved, and the speed and accuracy of ecological biological recognition are effectively improved.
Description
technical field [0001] The invention relates to the technical field of biometric identification, in particular to an ecological biometric identification method based on the Light-Head R-CNN algorithm. Background technique [0002] Aquatic biological communities have an intricate relationship with the water environment and play an important role in water quality changes. Different types of aquatic organisms have different adaptability to water pollution, and some types are only suitable for living in clean water, which are called clear water organisms (or oligopollutants). Some aquatic organisms can live in sewage and are called sewage organisms. The survival of aquatic organisms marks the degree of water quality change, so organisms become indicators of water pollution. Through the investigation of aquatic organisms, the status of water pollution can be evaluated. Many aquatic organisms are very sensitive to water poisons, and they can also be tested by aquatic organism tox...
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IPC IPC(8): G06V10/764G06V10/20G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24
Inventor 杨志峰沈永明张远蔡宴朋
Owner 澜途集思生态科技集团有限公司