Intelligent sea horse identifying and catching system and method

Abstract

The invention discloses an intelligent sea horse identifying and catching system and method. The system comprises a catching container and a sea horse information identifying subsystem carried on the catching container. The fishing container is a cube or a cuboid; half of the side surface of the fishing container is open; the hippocampus information identification subsystem comprises a camera and a controller; the camera is used for acquiring an image of the cultured hippocampus; and the controller is used for analyzing the images of the cultured hippocampus to obtain the number of the cultured hippocampus and the corresponding price. Through the system, the accuracy and efficiency of hippocampus quantity statistics can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent aquaculture, and more specifically, to a seahorse intelligent recognition and fishing system and method. Background Art

[0002] In recent years, the seahorse aquaculture industry has been gradually developing from traditional aquaculture towards industrialization and technology. However, the seahorse aquaculture industry still faces problems such as low digitalization level, few intelligent facilities, and low labor productivity. Especially in the fishing and harvesting stage, the quantity data of seahorses is an important basic information for farmers. However, for traditional manual seahorse fishing, due to the small size of seahorses and inconvenient quantity statistics, a large amount of manpower and material resources are often spent during the statistics process, and the efficiency is relatively low.

[0003] The development of artificial intelligence technology in the field of aquaculture has opened up new ideas for solving the above-mentioned problems.

[0004] Therefore, how to improve the accuracy and statistical efficiency of seahorse quantity statistics based on artificial intelligence is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] In view of the above problems, the present invention provides a seahorse intelligent recognition and fishing system and method to at least solve some of the technical problems mentioned in the above background art.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] On the one hand, an embodiment of the present invention provides a seahorse intelligent recognition and fishing system, including a fishing container, and a seahorse information recognition subsystem mounted on the fishing container;

[0008] The fishing container is a cube or a cuboid; and half of the side of the fishing container is open;

[0009] The seahorse information recognition subsystem includes: a camera and a controller;

[0010] The camera is used to acquire images of cultured seahorses;

[0011] The controller is used to analyze the images of cultured seahorses to obtain the quantity and corresponding prices of cultured seahorses.

[0012] Further, the container wall of the fishing container is pure green.

[0013] Further, the number of the cameras is multiple, and they are evenly installed on the front, top, and side of the container wall respectively for acquiring images of cultured seahorses from the front view, top view, and side view.

[0014] Further, the controller is located above the fishing container; and a seahorse quantity recognition model, a seahorse length recognition model, and a price module are embedded in the controller;

[0015] The seahorse quantity recognition model and the seahorse length recognition model are respectively used to recognize the acquired seahorse breeding images to obtain the corresponding seahorse breeding quantity information and seahorse breeding length information;

[0016] The price module is used to estimate the corresponding seahorse breeding price according to the seahorse breeding quantity information and the seahorse breeding length information.

[0017] Further, the seahorse information recognition subsystem further includes a memory;

[0018] The memory is connected to the controller and is used to store the recognized seahorse breeding quantity information, seahorse breeding length information, and the corresponding seahorse breeding price.

[0019] Further, the seahorse information recognition subsystem further includes a display;

[0020] The display is located above the controller and is connected to the controller, and is used to display the recognized seahorse breeding quantity information, seahorse breeding length information, and the corresponding seahorse breeding price.

[0021] Further, the seahorse information recognition subsystem further includes a battery pack;

[0022] The battery pack is connected to the controller and is used to provide energy for the seahorse information recognition subsystem.

[0023] Further, the training process of the seahorse quantity recognition model includes:

[0024] Obtain a large number of images containing seahorses, and set the number of seahorses in each image as a label to obtain a seahorse quantity data set;

[0025] Use the seahorse quantity data set to train the YOLOv11 model to obtain a trained seahorse quantity recognition model.

[0026] Further, the training process of the seahorse length recognition model includes:

[0027] Obtain a large number of images containing seahorses, and set the length of seahorses in each image as a label to obtain a seahorse length data set;

[0028] Use the seahorse length data set to train the YOLOv11 model to obtain a trained seahorse quantity recognition model.

[0029] On the other hand, the present invention provides a hippocampus intelligent recognition and fishing method, which applies the above-mentioned system. The method specifically includes:

[0030] Collect and analyze the images of cultured hippocampi through the hippocampus information recognition subsystem to obtain the number of cultured hippocampi and the corresponding prices;

[0031] According to the number of cultured hippocampi and the corresponding prices, fish the cultured hippocampi through the fishing container.

[0032] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a hippocampus intelligent recognition and fishing system and method, which has the following beneficial effects:

[0033] Based on the powerful feature extraction and expression ability of artificial intelligence technology, the present invention extracts deep visual features to obtain a more accurate number of cultured hippocampi, significantly improves the fishing efficiency, reduces the cost, and helps the development of the hippocampus intelligent breeding industry.

[0034] The present invention trains a hippocampus number recognition model and a hippocampus length recognition model based on the YOLOv11 model, providing higher accuracy, faster processing speed, better adaptability and flexibility for object number recognition and length recognition, making it superior to traditional image processing and object recognition technologies in many practical application scenarios. Obtain more accurate quantity information

[0035] Next, through the drawings and embodiments, the technical solutions of the present invention will be further described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0037] Figure 1 It is a three-view drawing of the hippocampus intelligent recognition and fishing system provided by the embodiment of the present invention.

[0038] Figure 2 It is a schematic diagram of the camera provided by the embodiment of the present invention.

[0039] Figure 3 It is a schematic diagram of the controller provided by the embodiment of the present invention.

[0040] Figure 4 It is a schematic diagram of the display provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] Embodiment 1:

[0043] An embodiment of the present invention discloses a hippocampus intelligent recognition and fishing system, including a fishing container and a hippocampus information recognition subsystem mounted on the fishing container; wherein:

[0044] The fishing container is a cube or a cuboid; and half of the side of the fishing container is open;

[0045] The hippocampus information recognition subsystem includes a camera and a controller; the camera is used to obtain images of cultured hippocampi; the controller is used to analyze the images of cultured hippocampi to obtain the number of cultured hippocampi and the corresponding prices.

[0046] The three views of the hippocampus intelligent recognition and fishing system can be seen in Figure 1 as shown, where Figure 1 (1), (2), and (3) in the figure respectively represent the top view, side view, and front view. Next, the above components will be described in detail.

[0047] 1. Fishing container

[0048] In the embodiment of the present invention, there is at least 1 fishing container, and at least 1 hippocampus information recognition subsystem is mounted on each fishing container;

[0049] The fishing container is a cube or a cuboid, and half of the side of the fishing container is open; based on this, when the fishing container is placed underwater, water and hippocampi will enter the container. Since half of the side of the fishing container is open, when the fishing container is lifted, part of the water will flow out, leaving only the water and hippocampi in the lower half of the container.

[0050] The container wall of the fishing container is pure green, which is convenient for fishing cultured hippocampi.

[0051] 2. Hippocampus information recognition subsystem

[0052] The hippocampus information recognition subsystem includes a camera; see Figure 2As shown, there are multiple cameras, which are evenly installed on the front, top, and side of the container wall, and are respectively used to obtain the images of cultured seahorses from the front view, top view, and side view; the cameras verify the quantity and length information of the seahorses in the container in three directions, presenting the seahorse status more intuitively. For example, the number of cameras is set to 3, which are respectively installed on the front, top, and side of the container wall; the cameras are connected to the controller and are used to send the obtained images of cultured seahorses to the controller.

[0053] The seahorse information recognition subsystem includes a controller; see Figure 3 As shown, the controller is located above the fishing container; and a seahorse quantity recognition model, a seahorse length recognition model, and a price module are embedded in the controller; where:

[0054] The seahorse quantity recognition model and the seahorse length recognition model are respectively used to recognize the obtained images of cultured seahorses to obtain the corresponding quantity information and length information of the cultured seahorses;

[0055] The price module is used to estimate the corresponding price of the cultured seahorses according to the quantity information and length information of the cultured seahorses.

[0056] The training process of the above seahorse quantity recognition model includes: obtaining a large number of images containing seahorses, and setting the number of seahorses in each image as a label to obtain a seahorse quantity data set; using the seahorse quantity data set to train the YOLOv11 model to obtain a trained seahorse quantity recognition model.

[0057] The training process of the above seahorse length recognition model includes: obtaining a large number of images containing seahorses, and setting the length of seahorses in each image as a label to obtain a seahorse length data set; using the seahorse length data set to train the YOLOv11 model to obtain a trained seahorse quantity recognition model.

[0058] The seahorse information recognition subsystem includes a memory; the memory is connected to the controller and is used to store the recognized quantity information, length information of the cultured seahorses, and the corresponding price of the cultured seahorses.

[0059] The seahorse information recognition subsystem includes a display; see Figure 4 As shown, the display is an LCD display screen, which is located above the controller and is connected to the controller, and is used to display the recognized quantity information, length information of the cultured seahorses, and the corresponding price of the cultured seahorses. In the specific use process, a start detection button can be set on the display to control the start of the entire seahorse information recognition subsystem. When the seahorse information recognition subsystem starts to run, the display screen will display the quantity information, length information of the cultured seahorses, and the corresponding price of the cultured seahorses.

[0060] The seahorse information recognition subsystem includes a battery pack; the battery pack is connected to the controller and is used to supply energy to the seahorse information recognition subsystem.

[0061] Embodiment 2:

[0062] The embodiment of the present invention provides a seahorse intelligent recognition and fishing method, which applies the seahorse intelligent recognition and fishing system provided in Embodiment 1 above. The method specifically includes: collecting and analyzing the images of cultured seahorses through the seahorse information recognition subsystem to obtain the number of cultured seahorses and the corresponding prices; and fishing the cultured seahorses through the fishing container according to the number of cultured seahorses and the corresponding prices.

[0063] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "front", "side", "upper surface", etc. is the orientation or positional relationship based on the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0064] In the present invention, unless otherwise clearly specified and defined, terms such as "connection" and "installation" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0065] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the methods disclosed in the embodiments, since they correspond to the systems disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0066] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A seahorse intelligent identification and fishing system, characterized in that: It includes a fishing container and a seahorse information identification subsystem carried on the fishing container; The fishing container is a cube or a cuboid; and half of the side of the fishing container is open; The hippocampus information recognition subsystem includes: a camera and a controller; The camera is used to obtain images of cultured seahorses; The controller is used to analyze the cultured seahorse image to obtain the number of cultured seahorses and the corresponding price.

2. A seahorse intelligent identification and fishing system according to claim 1, characterized in that: The container wall of the fishing container is pure green.

3. A seahorse intelligent identification and fishing system according to claim 2, characterized in that: There are multiple cameras, which are evenly installed on the front, top and side of the container wall, and are used to obtain images of cultured seahorses from the front, top and side perspectives, respectively.

4. The seahorse intelligent identification and fishing system according to claim 1 is characterized in that: The controller is located above the fishing container; and a seahorse quantity identification model, a seahorse length identification model and a price module are embedded in the controller; The seahorse quantity recognition model and the seahorse length recognition model are used to respectively recognize the acquired cultured seahorse images to obtain corresponding cultured seahorse quantity information and cultured seahorse length information; The price module is used to estimate the corresponding price of farmed seahorses based on the farmed seahorse quantity information and farmed seahorse length information.

5. A seahorse intelligent identification and fishing system according to claim 4, characterized in that: The hippocampal information recognition subsystem also includes a memory; The memory is connected to the controller and is used to store the identified farmed seahorse quantity information, farmed seahorse length information and corresponding farmed seahorse prices.

6. A seahorse intelligent identification and fishing system according to claim 4, characterized in that: The hippocampal information recognition subsystem also includes a display; The display is located above the controller and connected to the controller, and is used to display the identified farmed seahorse quantity information, farmed seahorse length information and the corresponding farmed seahorse price.

7. The seahorse intelligent identification and fishing system according to claim 1, characterized in that: The hippocampus information recognition subsystem also includes a battery pack; The battery pack is connected to the controller and is used to provide energy to the hippocampal information recognition subsystem.

8. The seahorse intelligent identification and fishing system according to claim 4, characterized in that: The training process of the hippocampal quantity recognition model includes: Obtain a large number of images containing hippocampi, and set the number of hippocampi in each image as a label to obtain a hippocampus number dataset; The hippocampus number dataset is used to train the YOLOv11 model to obtain a trained hippocampus number recognition model.

9. The seahorse intelligent identification and fishing system according to claim 4, characterized in that: The training process of the hippocampal length recognition model includes: Obtain a large number of images containing hippocampi, and set the length of the hippocampus in each image as a label to obtain a hippocampus length dataset; The hippocampus length dataset is used to train the YOLOv11 model to obtain a trained hippocampus quantity recognition model.

10. A seahorse intelligent identification and fishing method, characterized in that: Using the system described in any one of claims 1 to 9, the method specifically comprises: The seahorse information recognition subsystem collects and analyzes images of farmed seahorses to obtain the number of farmed seahorses and the corresponding price; The farmed seahorses are caught using the fishing container according to the number of the farmed seahorses and the corresponding price.

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