CN111709421A

CN111709421A - Bird identification method and device, computer equipment and storage medium

Info

Publication number: CN111709421A
Application number: CN202010557975.XA
Authority: CN
Inventors: 廖金辉; 吴亦歌; 贺鹏; 李德民; 肖娟; 徐亮
Original assignee: Shanghai Minghang Technology Development Co ltd; Shenzhen Sunwin Intelligent Co Ltd
Current assignee: Shanghai Minghang Technology Development Co ltd; Shenzhen Sunwin Intelligent Co Ltd
Priority date: 2020-06-18
Filing date: 2020-06-18
Publication date: 2020-09-25
Anticipated expiration: 2040-06-18
Also published as: CN111709421B

Abstract

The invention relates to a bird identification method, a bird identification device, computer equipment and a storage medium, wherein the bird identification method comprises the steps of obtaining an image shot by a panoramic network camera to obtain a panoramic image; inputting the panoramic image into a bird detection model for bird detection to obtain a detection result; judging whether the detection result is the result of bird existence; if the detection result is that birds exist, acquiring a bird detail image from the dome camera, and segmenting the bird detail image by adopting the detection result to obtain a detail image; inputting the detail image into a bird recognition model for bird recognition to obtain a recognition result; and storing the recognition result and the detail image into a database. The bird automatic identification device disclosed by the invention can realize automatic identification of birds, improves the identification efficiency and accuracy, has no monitoring blind area and is low in cost.

Description

Bird identification method and device, computer equipment and storage medium

Technical Field

The present invention relates to bird identification methods, and more particularly, to a bird identification method, apparatus, computer device, and storage medium.

Background

Birds are important indicators for biodiversity monitoring and ecological environment impact evaluation. The current situation of bird resources can be known through investigation and monitoring of bird species, the characteristics such as composition, quantity and diversity of bird species can be concluded, and the characteristics can be utilized to directly reflect the environmental quality of habitat, the health degree of ecosystem, biodiversity condition, the interference degree of human activities on ecosystem, the influence degree of land utilization and landscape change on ecosystem and the like, so that birds need to be identified and supervised to ensure that birds and human beings can harmoniously locate.

The existing bird identification method is to adopt a single camera or a ball grabbing linkage mode to shoot corresponding pictures and then adopt an artificial classification identification method to identify birds, but the mode needs to consume a large amount of manpower, material resources and financial resources, in addition, a monitoring blind area exists, the method cannot shoot the pictures of all birds in the whole monitoring area, if the whole monitoring area needs to be completely covered, a large number of fixed visual angle monitoring cameras need to be arranged, and the cost is high.

Therefore, it is necessary to design a new method to realize automatic bird identification, improve identification efficiency and accuracy, and have no monitoring blind area and low cost.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provides a bird identification method, a bird identification device, computer equipment and a storage medium.

In order to achieve the purpose, the invention adopts the following technical scheme: a method of bird identification comprising:

acquiring an image shot by a panoramic network camera to obtain a panoramic image;

inputting the panoramic image into a bird detection model for bird detection to obtain a detection result;

judging whether the detection result is the result of bird existence;

if the detection result is that birds exist, acquiring a bird detail image from the dome camera, and segmenting the bird detail image by adopting the detection result to obtain a detail image;

inputting the detail image into a bird recognition model for bird recognition to obtain a recognition result;

storing the recognition result and the detail image into a database;

the bird detection model is obtained by training a deep learning neural network by using image data of a plurality of bird position labels as first sample data;

the bird recognition model is obtained by training a neural network by using image data of a plurality of bird individual labels as second sample data.

The further technical scheme is as follows: bird detection model is obtained through the image data of a plurality of bird position labels as first sample data training deep learning neural network, and includes:

acquiring an image of the bird;

marking a bird position label on the bird image to obtain a first sample data set;

and training the deep learning neural network by adopting the first sample data set to obtain a bird detection model.

The further technical scheme is as follows: adopt first sample data set training deep learning neural network to obtain birds detection model, include:

dividing the first sample data set into a first training set and a first testing set;

setting parameters for training the YOLOV4 algorithm;

inputting a first training set into a YOLOV4 algorithm for network model training to obtain a first initial model;

testing the first initial model by adopting a first test set to obtain a first test result;

judging whether the first test result meets the requirement or not;

if the first test result does not meet the requirement, executing the parameter for setting the YOLOV4 algorithm training;

and if the first test result meets the requirement, taking the first initial model as a bird detection model.

The further technical scheme is as follows: the bird recognition model is obtained by training a neural network by using image data of a plurality of bird individual labels as second sample data, and comprises the following steps:

acquiring a detail image of a bird;

marking the bird identification labels on the detail images of the birds to obtain a second sample data set;

and training the neural network by adopting the second sample data set to obtain the bird recognition model.

The further technical scheme is as follows: the training of the neural network by adopting the second sample data set to obtain the bird recognition model comprises the following steps:

dividing a second sample data set into a second training set and a second test set;

setting parameters for training of a resnet50 algorithm;

inputting the second training set into a resnet50 algorithm for network model training to obtain a second initial model;

testing the second initial model by adopting a second test set to obtain a second test result;

judging whether the second test result meets the requirement or not;

if the second test result does not meet the requirement, executing the parameter for setting the training of the resnet50 algorithm;

and if the second test result meets the requirement, taking the second initial model as a bird identification model.

The further technical scheme is as follows: after the storing the recognition result and the detail image into the database, the method further comprises:

analyzing the database, making a corresponding bird ecological environment treatment scheme to obtain a treatment plan, and feeding back the treatment plan to a terminal.

The present invention also provides a bird identification device, comprising:

a panoramic image acquisition unit for acquiring an image shot by the panoramic network camera to obtain a panoramic image;

the bird detection unit is used for inputting the panoramic image into a bird detection model for bird detection to obtain a detection result;

the detection judging unit is used for judging whether the detection result is the result of bird existence;

the segmentation unit is used for acquiring a bird detail image from the dome camera if the detection result is a result that birds exist, and segmenting the bird detail image by adopting the detection result to obtain a detail image;

the identification unit is used for inputting the detail image into a bird identification model for bird identification to obtain an identification result;

and the storage unit is used for storing the identification result and the detail image into a database.

The further technical scheme is as follows: further comprising:

the first building unit is used for training the deep learning neural network by using the image data of the plurality of bird position labels as first sample data so as to obtain a bird detection model.

The invention also provides computer equipment which comprises a memory and a processor, wherein the memory is stored with a computer program, and the processor realizes the method when executing the computer program.

The invention also provides a storage medium storing a computer program which, when executed by a processor, is operable to carry out the method as described above.

Compared with the prior art, the invention has the beneficial effects that: according to the invention, through acquiring the image shot by the panoramic network camera, the panoramic network camera can shoot the monitored area without dead angles, so that no monitoring blind area exists, the multipoint installation construction wiring of an application site is reduced, the cost is low, the panoramic image shot by the panoramic network camera is input into the bird detection model for detection, under the condition that birds exist, the linkage ball machine shoots a detail image, the detail image is segmented and then input into the bird identification model for identification, corresponding contents are stored into the database according to the identification result, the automatic identification of the birds is realized, the identification efficiency and the accuracy are improved, no monitoring blind area exists, and the cost is low.

The invention is further described below with reference to the accompanying drawings and specific embodiments.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic view of an application scenario of the bird identification method according to the embodiment of the present invention;

FIG. 2 is a schematic flow chart of a bird identification method provided by an embodiment of the present invention;

FIG. 3 is a schematic view of a sub-flow of a bird identification method according to an embodiment of the present invention;

FIG. 4 is a schematic view of a sub-flow of a bird identification method provided by an embodiment of the present invention;

FIG. 5 is a schematic view of a sub-flow of a bird identification method provided by an embodiment of the present invention;

FIG. 6 is a schematic view of a sub-flow of a bird identification method provided by an embodiment of the present invention;

FIG. 7 is a schematic flow chart of a bird identification method according to another embodiment of the present invention;

FIG. 8 is a schematic block diagram of a bird identification device provided by an embodiment of the present invention;

FIG. 9 is a schematic block diagram of a bird identification device provided in another embodiment of the present invention;

FIG. 10 is a schematic block diagram of a computer device provided by an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

Referring to fig. 1 and fig. 2, fig. 1 is a schematic view of an application scenario of the bird identification method according to the embodiment of the present invention. Fig. 2 is a schematic flow chart of a bird identification method provided by an embodiment of the present invention. The bird identification method is applied to a server. This server and panorama network camera, the ball machine, the terminal carries out data interaction, after acquireing panoramic image through panorama network camera, carry out the detection that birds exist by the server, when birds exist, the testing result is the coordinate of the rectangle frame at output birds place, also be exactly the positional information that birds place, afterwards, the server is again followed ball machine and is located the detail image that acquires birds, after cutting apart detail image according to positional information, only including the regional image of only birds detail, discern in inputing birds identification model with it, do not keep in the database in order to obtain birds, supply the terminal to call to look up.

Fig. 2 is a schematic flow chart of a bird identification method provided by an embodiment of the present invention. As shown in fig. 2, the method includes the following steps S110 to S160.

And S110, acquiring an image shot by the panoramic network camera to obtain a panoramic image.

In the present embodiment, the panoramic image is an image taken from a panoramic network camera after the panoramic network camera is arranged in the monitored area.

The panoramic network camera is used for covering the whole range to be monitored, a monitoring blind area does not exist, 360-degree dead-angle-free range monitoring is carried out on the application field through the panoramic network camera, the panoramic network camera can fully cover the whole monitoring area, the detection and the type identification of birds are realized, the key realizes the detection without the blind area, the multi-point installation construction wiring of the application field is reduced, and the hardware cost of a plurality of cameras is reduced.

And S120, inputting the panoramic image into a bird detection model for bird detection to obtain a detection result.

In this embodiment, the detection result indicates whether there is the category that birds exist, and when it is the category that birds exist that detects, the detection result also includes the rectangle frame that this birds were located, that is to say the position that birds were located.

The bird detection model is obtained by training a deep learning neural network by using image data of a plurality of bird position labels as first sample data.

In an embodiment, referring to fig. 3, the bird detection model is obtained by training a deep learning neural network using image data of a plurality of bird position tags as first sample data, and includes steps S121 to S123.

And S121, acquiring bird images.

In the present embodiment, bird images refer to images collected from a network, including images that do not contain birds or contain birds, and images collected in an actual environment by assuming a panoramic network camera.

And S122, marking the bird position label on the bird image to obtain a first sample data set.

In this embodiment, the first sample dataset refers to data with bird position labels that can be used to train a deep learning neural network.

Bird images are collected and manually shot on the network, the bird images under various environmental backgrounds are collected as much as possible, and the bird images are marked and stored as xml data format files. The marked bird images are randomly divided into a first training set and a first testing set according to the ratio of 9:1, and of course, different ratios can be set according to actual requirements to divide a first sample data set.

Specifically, a labeling tool, namely, labelImg, is adopted to label coordinates of bird images, if the bird images exist, a bird rectangular frame is labeled, the best bird image is just framed, and the labeling is a bird label and a position information label, and of course, if the bird images do not exist, the labeling is a blank label, so that the deep learning neural network is trained.

And S123, training the deep learning neural network by adopting the first sample data set to obtain the bird detection model.

In the present embodiment, the bird detection model refers to a model that is trained and can be used to directly perform bird detection on an input image to obtain information on whether or not a bird is present and where the bird is located when the bird is present.

In one embodiment, referring to fig. 4, the step S123 may include steps S1231 to S1236.

And S1231, dividing the first sample data set into a first training set and a first testing set.

In the present embodiment, the first training set is image data used for training the YOLOV4 algorithm; the first test set is image data used to test the trained YOLOV4 algorithm.

S1232, setting parameters for algorithm training of YOLOV 4;

and S1233, inputting the first training set into a YOLOV4 algorithm for network model training to obtain a first initial model.

In this embodiment, the first initial model is a model obtained by training using the YOLOV4 algorithm in the deep learning neural network, training using the stochastic gradient descent algorithm, stopping training when the loss function value decreases to be stable, that is, when the loss value of the loss function tends to be stable, and storing the training.

And (3) taking the open-source pre-training data model as an initialized parameter, stopping training when the loss function value is reduced to a stable region and is at a point less than 1.0, and outputting a first initial model.

S1234, testing the first initial model by using the first test set to obtain a first test result.

In this embodiment, the first test result refers to a result obtained by testing the first initial model with the first test set.

S1235, judging whether the first test result meets the requirement;

if the first test result does not meet the requirement, executing the step S1232;

and S1236, if the first test result meets the requirement, using the first initial model as a bird detection model.

Specifically, the test result of the test set on the initial model is judged by using an mAP (mean Average Precision) index, and if the mAP is less than 0.95, the training parameter setting is modified or the data set is added for retraining until the requirement that the mAP is more than 0.95 is met. The mAP index is the average accuracy over one class, and then the average accuracy over all classes.

And (3) training by using a deep learning neural network, training by adopting a random gradient descent algorithm, testing and evaluating the first initial model when the loss function value is reduced to be stable, and selecting the optimal model, thereby forming the bird detection model.

S130, judging whether the detection result is the result of bird existence;

if the detection result shows that no birds exist, step S110 is executed.

And S140, if the detection result is that the birds exist, acquiring a bird detail image from the dome camera, and segmenting the bird detail image by adopting the detection result to obtain a detail image.

In the present embodiment, the bird detail image is an image of a bird from a dome camera.

According to the detected coordinate position, the ball machine is linked to acquire a bird image with more clear bird detail information, and the segmented detail image is output.

The panoramic network camera detects that the bird-linked dome camera outputs images with clear local details, and powerful guarantee is provided for bird identification.

And S150, inputting the detail image into a bird recognition model for bird recognition to obtain a recognition result.

In the present embodiment, the recognition result refers to the category to which birds in the detail image belong.

And the bird identification model is obtained by training a neural network by using image data of a plurality of bird identification labels as second sample data.

In an embodiment, referring to fig. 5, the bird recognition model is obtained by training a neural network using image data of a plurality of bird identification tags as second sample data, and includes steps S151 to S153.

And S151, acquiring a detail image of the bird.

In this embodiment, the detailed images of birds refer to images of different kinds of birds acquired by using a network collected image and a network open-source image library.

And S152, marking the bird identification labels on the detail images of the birds to obtain a second sample data set.

In this embodiment, the second sample data set refers to data with a bird identity tag and can be used for training a neural network.

The data labels for bird species identification are labeled with numbers, and if the number of birds is N, the data labels are respectively labeled as 0, 1, 2, 3, …, and N-1.

And S153, training the neural network by adopting the second sample data set to obtain the bird recognition model.

In the present embodiment, the bird recognition model refers to a model that is trained and can be used for bird category recognition directly on an input image.

In an embodiment, referring to fig. 6, the step S153 may include steps S1531 to S1536.

S1531, divide the second sample data set into a second training set and a second test set.

In the present embodiment, the first training set is image data for training the resnet50 algorithm; the first test set is image data used to test the trained resnet50 algorithm.

S1532, setting parameters for training the resnet50 algorithm;

and S1533, inputting the second training set into a resnet50 algorithm for network model training to obtain a second initial model.

In this embodiment, the second initial model is trained by using the resnet50 algorithm in the deep learning neural network, and when the loss function value decreases to tend to be stable, that is, when the loss value of the loss function tends to be stable, the training is stopped, and the trained model is saved.

Specifically, a resnet50 algorithm is adopted to conduct bird species recognition network training, an open-source pre-training model is also used as a real-eating practice parameter training of a training model, when the network model parameters tend to be stable, the network model is output with loss errors less than 1.0, and the training is stopped.

And S1534, testing the second initial model by using the second test set to obtain a second test result.

In this embodiment, the second test result refers to a result obtained by testing the second initial model with the second test set.

S1535, judging whether the second test result meets the requirement;

if the second test result does not meet the requirement, executing the step S1532;

s1536, if the second test result meets the requirement, the second initial model is used as a bird identification model.

S160, storing the recognition result and the detail image into a database;

specifically, a database is established for the recognition result and the detail image, and the image and the result obtained by subsequent recognition are both stored in the database.

If the birds exist in the database, the birds are compiled into a bird database, if the birds do not belong to the known bird species, the bird image is saved, and a new bird sample is provided for further expanding a bird recognition system

According to the bird identification method, the images shot by the panoramic network camera are obtained, the panoramic network camera can shoot the monitored area without dead angles, monitoring blind areas do not exist, multipoint installation construction wiring of an application site is reduced, the cost is low, the panoramic image shot by the panoramic network camera is input into the bird detection model for detection, under the condition that birds exist, the linkage ball machine shoots detailed images, the detailed images are segmented and then input into the bird identification model for identification, corresponding contents are stored into the database according to identification results, automatic bird identification is achieved, the identification efficiency and accuracy are improved, monitoring blind areas do not exist, and the cost is low.

Fig. 7 is a schematic flow chart of a bird identification method according to another embodiment of the present invention. As shown in fig. 7, the bird recognition method of the present embodiment includes steps S210 to S270. Steps S210 to S260 are similar to steps S110 to S160 in the above embodiments, and are not described herein again. The added step S270 in the present embodiment is explained in detail below.

S270, analyzing the database, making a corresponding bird ecological environment treatment scheme to obtain a treatment plan, and feeding back the treatment plan to a terminal.

In this embodiment, the management plan refers to a related bird ecological environment management scheme established according to the bird recognition result of a specific monitoring area such as an airport.

Through the analysis of database, formulate corresponding birds ecological environment treatment plan to send this plan to airport personnel, carry out by airport personnel, can furthest's reduction to the injury of birds, also can reach fine bird repellent effect.

Fig. 8 is a schematic block diagram of a bird identification device 300 according to an embodiment of the present invention. As shown in fig. 8, the present invention also provides a bird recognition device 300 corresponding to the above bird recognition method. The bird recognition device 300 includes a unit for performing the bird recognition method described above, and the device may be configured in a server. Specifically, referring to fig. 8, the bird recognition device 300 includes a panoramic image acquisition unit 301, a bird detection unit 302, a detection determination unit 303, a division unit 304, a recognition unit 305, and a storage unit 306.

A panoramic image acquisition unit 301 configured to acquire an image captured by a panoramic network camera to obtain a panoramic image; a bird detection unit 302, configured to input the panoramic image into a bird detection model for bird detection to obtain a detection result; a detection judgment unit 303, configured to judge whether the detection result is a result of presence of birds; a dividing unit 304, configured to obtain a bird detail image from the dome camera if the detection result is a result that birds exist, and divide the bird detail image by using the detection result to obtain a detail image; a recognition unit 305 for inputting the detail image into a bird recognition model for bird recognition to obtain a recognition result; a saving unit 306, configured to save the recognition result and the detail image into the database.

In one embodiment, bird identification device 300 further includes a first construction unit.

In an embodiment, the first building unit includes a bird image obtaining subunit, a position tag labeling subunit, and a first training subunit.

The bird image acquisition subunit is used for acquiring bird images; the position label labeling subunit is used for labeling the bird position labels on the bird images to obtain a first sample data set; and the first training subunit is used for training the deep learning neural network by adopting the first sample data set so as to obtain the bird detection model.

In an embodiment, the first training subunit includes a first dividing module, a first setting module, a first model obtaining module, a first testing module, and a first determining module.

The first dividing module is used for dividing the first sample data set into a first training set and a first testing set; the first setting module is used for setting parameters for training the YOLOV4 algorithm; the first model obtaining module is used for inputting a first training set into a Yolov4 algorithm to train a network model so as to obtain a first initial model; the first testing module is used for testing the first initial model by adopting a first testing set to obtain a first testing result; the first judgment module is used for judging whether the first test result meets the requirement or not; if the first test result does not meet the requirement, executing the parameter for setting the YOLOV4 algorithm training; and if the first test result meets the requirement, taking the first initial model as a bird detection model.

In one embodiment, bird identification device 300 further includes a second construction unit.

And the second construction unit is used for training the neural network by using the image data of the bird individual labels as second sample data so as to obtain the bird recognition model.

In an embodiment, the second constructing unit includes a detail image obtaining subunit, a category label labeling subunit, and a second training subunit.

The detail image acquisition subunit is used for acquiring a detail image of the bird; the category label labeling subunit is used for labeling the category labels of the birds on the detail images of the birds to obtain a second sample data set; and the second training subunit is used for training the neural network by adopting a second sample data set to obtain the bird recognition model.

In an embodiment, the second training subunit includes a second dividing module, a second setting module, a second model obtaining module, a second testing module, and a second determining module.

The second dividing module is used for dividing the second sample data set into a second training set and a second testing set; the second setting module is used for setting parameters for training the resnet50 algorithm; the second model acquisition module is used for inputting a second training set into a resnet50 algorithm for network model training to obtain a second initial model; the second testing module is used for testing the second initial model by adopting a second testing set to obtain a second testing result; the second judgment module is used for judging whether the second test result meets the requirement or not; if the second test result does not meet the requirement, executing the parameter for setting the training of the resnet50 algorithm; and if the second test result meets the requirement, taking the second initial model as a bird identification model.

Fig. 9 is a schematic block diagram of a bird identification device 300 according to another embodiment of the present invention. As shown in fig. 9, the bird recognition device 300 of the present embodiment is the above-described embodiment with the addition of the formulation unit 307.

And the making unit 307 is used for analyzing the database, making a corresponding bird ecological environment treatment scheme to obtain a treatment plan, and feeding back the treatment plan to the terminal.

It should be noted that, as can be clearly understood by those skilled in the art, the detailed implementation process of bird recognition device 300 and each unit may refer to the corresponding description in the foregoing method embodiments, and for convenience and brevity of description, no further description is provided herein.

Bird identification device 300 described above may be implemented in the form of a computer program that may be run on a computer device as shown in fig. 10.

Referring to fig. 10, fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 is a server, wherein the server may be an independent server or a server cluster composed of a plurality of servers.

Referring to fig. 10, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.

The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer programs 5032 include program instructions that, when executed, cause the processor 502 to perform a bird identification method.

The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.

The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 may be caused to execute a bird identification method.

The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 10 is a block diagram of only a portion of the configuration relevant to the present teachings and is not intended to limit the computing device 500 to which the present teachings may be applied, and that a particular computing device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

Wherein the processor 502 is configured to run the computer program 5032 stored in the memory to implement the following steps:

acquiring an image shot by a panoramic network camera to obtain a panoramic image; inputting the panoramic image into a bird detection model for bird detection to obtain a detection result; judging whether the detection result is the result of bird existence; if the detection result is that birds exist, acquiring a bird detail image from the dome camera, and segmenting the bird detail image by adopting the detection result to obtain a detail image; inputting the detail image into a bird recognition model for bird recognition to obtain a recognition result; and storing the recognition result and the detail image into a database.

The bird detection model is obtained by training a deep learning neural network by using image data of a plurality of bird position labels as first sample data; the bird recognition model is obtained by training a neural network by using image data of a plurality of bird individual labels as second sample data.

In an embodiment, when the bird detection model is implemented by the processor 502, the following steps are specifically implemented when the step of training the deep learning neural network by using the image data of the bird position tags as the first sample data is implemented:

acquiring an image of the bird; marking a bird position label on the bird image to obtain a first sample data set; and training the deep learning neural network by adopting the first sample data set to obtain a bird detection model.

In an embodiment, when the step of training the deep learning neural network with the first sample data set to obtain the bird detection model is implemented by the processor 502, the following steps are specifically implemented:

dividing the first sample data set into a first training set and a first testing set; setting parameters for training the YOLOV4 algorithm; inputting a first training set into a YOLOV4 algorithm for network model training to obtain a first initial model; testing the first initial model by adopting a first test set to obtain a first test result; judging whether the first test result meets the requirement or not; if the first test result does not meet the requirement, executing the parameter for setting the YOLOV4 algorithm training; and if the first test result meets the requirement, taking the first initial model as a bird detection model.

In an embodiment, when the processor 502 implements the step that the bird recognition model is obtained by training the neural network by using the image data of the plurality of bird identification tags as the second sample data, the following steps are implemented:

acquiring a detail image of a bird; marking the bird identification labels on the detail images of the birds to obtain a second sample data set; and training the neural network by adopting the second sample data set to obtain the bird recognition model.

In an embodiment, when the step of training the neural network by using the second sample data set to obtain the bird recognition model is implemented by the processor 502, the following steps are specifically implemented:

dividing a second sample data set into a second training set and a second test set; setting parameters for training of a resnet50 algorithm; inputting the second training set into a resnet50 algorithm for network model training to obtain a second initial model; testing the second initial model by adopting a second test set to obtain a second test result; judging whether the second test result meets the requirement or not; if the second test result does not meet the requirement, executing the parameter for setting the training of the resnet50 algorithm; and if the second test result meets the requirement, taking the second initial model as a bird identification model.

In one embodiment, after the step of saving the recognition result and the detail image in the database, the processor 502 further performs the following steps:

It should be understood that, in the embodiment of the present Application, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It will be understood by those skilled in the art that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing associated hardware. The computer program includes program instructions, and the computer program may be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.

Accordingly, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program, when executed by a processor, causes the processor to perform the steps of:

In an embodiment, when the processor executes the computer program to implement the step that the bird detection model is obtained by training the deep learning neural network by using image data of a plurality of bird position labels as first sample data, the processor specifically implements the following steps:

In an embodiment, the processor, when executing the computer program to implement the step of training the deep learning neural network with the first sample data set to obtain the bird detection model, specifically implements the following steps:

In an embodiment, when the processor executes the computer program to implement the step that the bird identification model is obtained by training a neural network by using image data of a plurality of bird identity tags as second sample data, the following steps are specifically implemented:

In an embodiment, when the processor executes the computer program to implement the step of training the neural network by using the second sample data set to obtain the bird recognition model, the following steps are specifically implemented:

In one embodiment, after the step of saving the recognition result and the detail image into the database is realized by the processor executing the computer program, the following steps are further realized:

The storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, which can store various computer readable storage media.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.

The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be merged, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.

While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method of bird identification, comprising:

judging whether the detection result is the result of bird existence;

storing the recognition result and the detail image into a database;

2. The bird recognition method of claim 1, wherein the bird detection model is obtained by training a deep learning neural network with image data of a plurality of bird position tags as first sample data, and comprises:

acquiring an image of the bird;

3. The bird identification method of claim 2, wherein training a deep learning neural network with a first sample data set to obtain a bird detection model comprises:

setting parameters for training the YOLOV4 algorithm;

judging whether the first test result meets the requirement or not;

4. The bird recognition method of claim 1, wherein the bird recognition model is obtained by training a neural network by using image data of a plurality of bird identification tags as second sample data, and comprises:

acquiring a detail image of a bird;

5. The bird recognition method of claim 4, wherein training a neural network with a second set of sample data to obtain a bird recognition model comprises:

setting parameters for training of a resnet50 algorithm;

judging whether the second test result meets the requirement or not;

6. The bird identification method of claim 1, further comprising, after saving the identification result and the detail image in a database:

7. Bird recognition device, characterized by, includes:

8. The bird identification device of claim 7, further comprising:

9. A computer device, characterized in that the computer device comprises a memory, on which a computer program is stored, and a processor, which when executing the computer program implements the method according to any of claims 1 to 6.

10. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.