CN111523612B - Deep-sea fish image classification and identification method - Google Patents
Deep-sea fish image classification and identification method Download PDFInfo
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Abstract
The invention discloses a deep-sea fish image classification and identification method which is characterized by comprising the following steps: step one, establishing a fish state multi-source information database according to the fin position information quantity, the fish size information quantity, the fish tail shape information quantity and the body color information quantity of deep-sea fishes, and dividing the state similarity of the fishes into 0-1; step two, establishing a three-layer neural network based on a BP algorithm; and step three, setting parameters of all items on the basis of the neural network tool, and training and verifying the neural network. The invention respectively makes four types of information scoring rules for fish category state evaluation, combines the scoring of the four types of information, adopts a neural network as an evaluation algorithm to obtain a comprehensive score which is used as a basis for fish classification, integrates various data and parameters, comprehensively and accurately evaluates the category of deep-sea fish, provides a scientific basis for deep-sea fish classification, can effectively develop and utilize deep-sea fish resources, and achieves the strategic significance of long-term development.
Description
Technical Field
The invention relates to the technical field of power grids, in particular to a deep-sea fish image classification and identification method.
Background
China is one of the world oceans, has a sea area of more than 300 million square kilometers, breeds extremely rich biological resources in the wide sea area of China, and according to statistics, the marine biological resources of China are as high as 20278 species, wherein about three thousand species of fishes account for about 20% of the fish species of the world, but the current marine resource development and utilization degree of China is low, and the overall level of marine economic development is not high;
the fish identification is the first step of marine fish resource detection and also an important basis for developing and utilizing marine resources, but the deep-sea fishes are different in shape and size, so that the task complexity is much higher compared with other identification tasks, and different varieties of the same fish usually have similar appearance, size and texture color, so that the identification difficulty of the fish is further increased, and therefore, deep-sea high-tech research, especially the deep-sea fish related technical research, has great strategic significance for effective development and utilization of marine biological resources and long-term development in China.
Disclosure of Invention
The invention aims to provide a deep-sea fish image classification and identification method to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a deep-sea fish image classification and identification method comprises the following steps:
step one, establishing a fish state multi-source information database according to the fin position information quantity, the fish size information quantity, the fish tail shape information quantity and the body color information quantity of deep-sea fishes, and dividing the state similarity of the fishes into 0-1;
establishing a three-layer neural network based on a BP algorithm, wherein the three-layer neural network comprises an input layer, a hidden layer and an output layer; wherein the input layer: receiving external signals and data, and taking indexes of fin position information quantity, fish size information quantity, fish tail shape information quantity and body color information quantity as nodes of an input layer;
an output layer: a BP network model in an ANN network is adopted, qualitative output is converted into quantitative output through a BP network neural model, and a set output result is comprehensively evaluated;
hidden layer: constructing a network according to a neuron number algorithm, and establishing a simple and efficient model;
setting parameters of various items on the basis of a neural network tool, and training and verifying the neural network;
step four, establishing an image acquisition module; the system is used for acquiring image data of deep-sea fishes and uploading the shot data image data to a multi-source information database for further processing;
introducing data of fin position information quantity, fish size information quantity, fish tail shape information quantity and body color information quantity into the trained neural network to obtain relevant numerical values of P1, P2, P3 and P4;
sixthly, calculating according to the obtained numerical values of P1, P2, P3 and P4 to obtain specific values of each state quantity of the fish evaluation at the time;
step seven, inputting specific values of the state quantities of the state evaluation, and outputting a comprehensive evaluation result P;
and step eight, inputting the obtained result P into a multi-source information database to perform classification and identification on the deep-sea fishes.
Preferably, the state similarity can be divided into five states: degree I similarity is divided into 0-0.2 degree I similarity, degree II similarity is divided into 0.2-0.4 degree II similarity, degree III similarity is divided into 0.4-0.6 degree III similarity, degree V similarity is divided into 0.6-0.8 degree V similarity, and degree VI similarity is divided into 0.8-1 degree VI similarity.
Preferably, in step three, the parameters are correction rate, training times and training target error.
Preferably, in the fourth step, the image needs to be preprocessed, and after the acquired fish image is preprocessed, the image reading, the size adjustment and the data conversion are performed.
Preferably, in step four, the image acquisition module includes: the device comprises an underwater camera equipment, an instrument interface, an underwater connection module, a data caching and forwarding module and a decomposition framing preprocessing module.
Preferably, in step eight, the multi-source information database obtains the result and displays the result, for example: fish A is 0.5, fish B is 0.63, fish C is 0.72, and fish D is 0.82.
The invention provides a deep-sea fish image classification and identification method, which has the beneficial effects that: by adopting a multi-index scoring method, a standard of comprehensive score classification is formulated by giving a standard of an evaluation system of multi-index scoring of fishes, four information scoring rules of fish category state evaluation are formulated respectively according to the characteristics of four information of deep-sea fishes, a neural network is adopted as an evaluation algorithm by combining the scoring of the four information to obtain comprehensive scores, the comprehensive scores are used as a basis of fish classification, various data and parameters are integrated, the categories of the deep-sea fishes are comprehensively and accurately evaluated, scientific bases are provided for deep-sea fish classification, deep-sea fish resources can be effectively developed and utilized, and the strategic significance of long-term development is achieved.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The invention provides a technical scheme that: a deep-sea fish image classification and identification method comprises the following steps: step one, establishing a fish state multi-source information database according to the fin position information quantity, the fish size information quantity, the fish tail shape information quantity and the body color information quantity of deep-sea fishes, dividing the state similarity of the fishes into 0-1, and dividing the state similarity into five states: 0-0.2 is similar for degree I, 0.2-0.4 is similar for degree II, 0.4-0.6 is similar for degree III, 0.6-0.8 is similar for degree V, and 0.8-1 is similar for degree VI; establishing a three-layer neural network based on a BP algorithm, wherein the three-layer neural network comprises an input layer, a hidden layer and an output layer; wherein the input layer: receiving external signals and data, and taking indexes of fin position information amount, fish size information amount, fish tail shape information amount and body color information amount as nodes of an input layer; an output layer: a BP network model in an ANN network is adopted, qualitative output is converted into quantitative output through a BP network neural model, and a set output result is comprehensively evaluated; hidden layer: constructing a network according to a neuron number algorithm, and establishing a simple and efficient model; setting parameters of each item on the basis of a neural network tool, and training and verifying the neural network, wherein the parameters are correction rate, training times and training target error; step four, establishing an image acquisition module; the image acquisition module is used for acquiring deep-sea fish image data, uploading the shot data image data to a multi-source information database for further processing, preprocessing the image, reading the image, adjusting the size and converting the data after preprocessing the collected fish image, and comprises: the system comprises an underwater camera device, an instrument interface, an underwater connection module, a data caching and forwarding module and a decomposition framing preprocessing module; introducing data of fin position information quantity, fish size information quantity, fish tail shape information quantity and body color information quantity into the trained neural network to obtain relevant numerical values of P1, P2, P3 and P4; sixthly, calculating according to the obtained numerical values of P1, P2, P3 and P4 to obtain specific values of the state quantities evaluated by the fishes at this time; step seven, inputting specific values of the state quantities of the state evaluation, and outputting a comprehensive evaluation result P; step eight, inputting the obtained result P into a multi-source information database for classification and identification of the deep-sea fishes, and displaying the result in the multi-source information database, wherein the steps are as follows: fish A is 0.5, fish B is 0.63, fish C is 0.72, and fish D is 0.82.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A deep-sea fish image classification and identification method is characterized by comprising the following steps:
step one, establishing a fish state multi-source information database according to the fin position information quantity, the fish size information quantity, the fish tail shape information quantity and the body color information quantity of deep-sea fishes, and dividing the state similarity of the fishes into 0-1;
establishing a three-layer neural network based on a BP algorithm, wherein the three-layer neural network comprises an input layer, a hidden layer and an output layer; wherein
An input layer: receiving external signals and data, and taking indexes of fin position information amount, fish size information amount, fish tail shape information amount and body color information amount as nodes of an input layer;
an output layer: a BP network model in an ANN network is adopted, qualitative output is converted into quantitative output through a BP network neural model, and a set output result is comprehensively evaluated;
hidden layer: constructing a network according to a neuron number algorithm, and establishing a simple and efficient model;
setting parameters of various items on the basis of a neural network tool, and training and verifying the neural network;
step four, establishing an image acquisition module; the system is used for acquiring image data of deep-sea fishes and uploading the shot data image data to a multi-source information database for further processing;
introducing data of fin position information quantity, fish size information quantity, fish tail shape information quantity and body color information quantity into the trained neural network to obtain relevant numerical values of P1, P2, P3 and P4;
sixthly, calculating according to the obtained numerical values of P1, P2, P3 and P4 to obtain specific values of each state quantity of the fish evaluation at the time;
step seven, inputting specific values of the state quantities of the state evaluation, and outputting a comprehensive evaluation result P;
and step eight, inputting the obtained result P into a multi-source information database to perform classification and identification on the deep-sea fishes.
2. The deep-sea fish image classification and identification method according to claim 1, characterized in that: in the first step, the state similarity can be divided into five states: degree I similarity is divided into 0-0.2 degree I similarity, degree II similarity is divided into 0.2-0.4 degree II similarity, degree III similarity is divided into 0.4-0.6 degree III similarity, degree V similarity is divided into 0.6-0.8 degree V similarity, and degree VI similarity is divided into 0.8-1 degree VI similarity.
3. The deep-sea fish image classification and identification method according to claim 1, characterized in that: in the third step, the parameters are correction rate, training times and training target error.
4. The deep-sea fish image classification and identification method according to claim 1, characterized in that: and in the fourth step, the image needs to be preprocessed, and after the acquired fish image is preprocessed, the image is read, the size is adjusted and the data is converted.
5. The deep-sea fish image classification and identification method according to claim 1, characterized in that: in step four, the image acquisition module comprises: the device comprises an underwater camera equipment, an instrument interface, an underwater connection module, a data caching and forwarding module and a decomposition framing preprocessing module.
6. The deep-sea fish image classification and identification method according to claim 1, characterized in that: in the eighth step, the multi-source information database obtains the result and displays the result, for example: fish A is 0.5, fish B is 0.63, fish C is 0.72, and fish D is 0.82.
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| CN113269078B (en) * | 2021-05-20 | 2022-04-19 | 中国水利水电科学研究院 | Unmanned aerial vehicle-based river fish resource amount statistical method and system |
| CN113758577B (en) * | 2021-09-09 | 2022-04-22 | 易科捷(武汉)生态科技有限公司成都分公司 | Accurate type fish identification technology system based on big data |
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