CN114863478A - Livestock weight identification method and device, storage medium and terminal equipment - Google Patents

Livestock weight identification method and device, storage medium and terminal equipment Download PDF

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CN114863478A
CN114863478A CN202210445427.7A CN202210445427A CN114863478A CN 114863478 A CN114863478 A CN 114863478A CN 202210445427 A CN202210445427 A CN 202210445427A CN 114863478 A CN114863478 A CN 114863478A
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image
weight
target
livestock
reference object
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邓文忠
刁远明
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Shenzhen Zhongrong Digital Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01K—ANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
    • A01K29/00—Other apparatus for animal husbandry
    • A01K29/005—Monitoring or measuring activity, e.g. detecting heat or mating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/32—Normalisation of the pattern dimensions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/12—Acquisition of 3D measurements of objects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/70—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in livestock or poultry

Abstract

The embodiment of the application provides a livestock weight identification method, a livestock weight identification device, a storage medium and terminal equipment, wherein the method comprises the following steps: acquiring a first image, wherein the first image comprises a target to be detected and a reference object bound with the target to be detected; determining the scaling of the target to be measured relative to the preset size based on the position coordinates of the reference object in the first image; adjusting the size of the first image according to the scaling to obtain a second image; and inputting the second image into the weight recognition model to perform weight recognition operation, so as to obtain weight data of the target to be detected in the second image. According to the embodiment of the application, the scaling of the reference object is calculated, the first image is scaled according to the obtained scaling, after the second image with the unified standard proportion is obtained, the weight parameter identification is carried out on the second image by utilizing the weight identification model trained in advance, the frequent direct contact of workers with livestock can be avoided, and the acquisition process of the weight parameter is simple and high in accuracy.

Description

Livestock weight identification method and device, storage medium and terminal equipment
Technical Field
The present application relates to the field of electronic communications technologies, and in particular, to a method, an apparatus, a storage medium, and a terminal device for identifying a weight of a livestock.
Background
In order to obtain the weight parameters of the livestock, the traditional method is to directly measure the livestock by means of a weight scale. The obtained weight parameters can be applied to various scenes, for example, livestock bred by farmers die after the insurance of livestock breeding insurance is put into operation, and if the conditions of claim settlement are met, the compensation can be obtained according to the weight. The livestock insurance usually takes the weight of the livestock as the basis for claim settlement, and different weight intervals are paid according to different quota proportions. Because traditional survey flow is for the scene is taken a picture, register ear mark number to use weighing equipment manual measurement livestock weight parameter, perhaps combine to measure livestock length with the tape measure and speculate weight, in order to obtain accurate reimbursement numerical value, it is loaded down with trivial details to use weighing equipment weight measurement process, especially the manual work carries out weight parameter measurement in-process and is inevitable with the carcass direct contact, in case the livestock carries the virus and will infect for the staff, is unfavorable for the health and safety who ensures the staff.
Disclosure of Invention
The embodiment of the application provides a livestock weight identification method, a livestock weight identification device, a storage medium and terminal equipment.
An embodiment of the application provides a livestock weight recognition method on the one hand, including:
acquiring a first image, wherein the first image comprises a target to be detected and a reference object bound with the target to be detected;
determining the scaling of the target to be measured relative to a preset size based on the position coordinates of the reference object in the first image;
adjusting the size of the first image according to the scaling to obtain a second image;
and inputting the second image into a pre-trained weight recognition model for weight recognition operation to obtain weight data of the target to be detected in the second image.
In the method for identifying the weight of the livestock according to the embodiment of the application, before the acquiring the first image, the method further includes:
acquiring an initial image;
inputting the initial image into a pre-trained target recognition model to perform target recognition operation, so as to obtain a preprocessed image comprising at least one preset mark frame, wherein the preset mark frame is used for indicating the target to be detected or a reference object;
and carrying out image segmentation on the preprocessed image to obtain at least one first image, wherein only one preset mark frame of the same category is arranged in each first image.
In the method for identifying the weight of the livestock according to the embodiment of the application, the preset mark frame includes a first mark frame for indicating the target to be detected and a second mark frame for indicating the reference object, and after the image segmentation processing is performed on the preprocessed image, the method further includes:
judging whether an intermediate image obtained by image segmentation processing of the preprocessed image simultaneously contains the first mark frame and the second mark frame;
and taking an intermediate image which simultaneously comprises the first mark frame and the second mark frame as the first image.
In the method for identifying the weight of the livestock according to the embodiment of the application, after the step of determining whether the intermediate image obtained by the image segmentation processing of the preprocessed image contains the first mark frame and the second mark frame at the same time, the method further includes:
and when the first mark frame and the second mark frame do not exist in the intermediate image at the same time, generating abnormity reminding information, wherein the abnormity reminding information is used for indicating that the target to be detected and a reference object are abnormally bound.
In the method for identifying the weight of the livestock according to the embodiment of the application, the determining the scaling of the target to be measured relative to a preset size based on the position coordinates of the reference object in the first image includes:
acquiring vertex coordinates of a preset mark frame corresponding to the reference object in the first image;
calculating a size parameter of the reference object in the first image according to the vertex coordinates;
and calculating the scaling according to the size parameter and the preset size of the reference object.
In the method for identifying the weight of the livestock according to the embodiment of the application, before the second image is input to a pre-trained weight identification model for weight identification operation, the method further includes:
acquiring color component values of all channels of each pixel point in the second image;
mirror filling is performed on the color component values of all channels, respectively, so that the size of the second image is mxn.
In the method for identifying the weight of the livestock according to the embodiment of the application, before the second image is input to a pre-trained weight identification model for weight identification operation, the method further includes:
acquiring a training sample of a weight recognition model to be trained, wherein the training sample comprises image data provided with a label, and the label is used for indicating a weight parameter of the image data;
performing feature extraction on image data in the training sample through the weight recognition model to be trained to obtain an image feature vector corresponding to the image data;
identifying weight parameters of image data in the training sample based on the image feature vectors through the weight identification model to be trained to obtain an identification result of the image data;
and adjusting parameters of the weight recognition model to be trained based on the recognition result and the label of the image data to obtain the pre-trained weight recognition model.
Correspondingly, another aspect of the embodiments of the present application further provides a livestock weight recognition device, including:
the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a first image, and the first image comprises a target to be detected and a reference object bound with the target to be detected;
the determining module is used for determining the scaling of the target to be measured relative to a preset size based on the position coordinates of the reference object in the first image;
the adjusting module is used for adjusting the size of the first image according to the scaling to obtain a second image;
and the recognition module is used for inputting the second image to a pre-trained weight recognition model for weight recognition operation to obtain weight data of the target to be detected in the second image.
Accordingly, the embodiment of the present application also provides a storage medium storing a plurality of instructions, which are suitable for being loaded by a processor to execute the livestock weight identification method.
Accordingly, the embodiment of the present application also provides a terminal device in another aspect, which includes a processor and a memory, where the memory stores a plurality of instructions, and the processor loads the instructions to execute the livestock weight identification method as described above.
The embodiment of the application provides a livestock weight identification method, a livestock weight identification device, a storage medium and terminal equipment, wherein the method comprises the steps of obtaining a first image, wherein the first image comprises a target to be detected and a reference object bound with the target to be detected; determining the scaling of the target to be measured relative to a preset size based on the position coordinates of the reference object in the first image; adjusting the size of the first image according to the scaling to obtain a second image; and inputting the second image into a pre-trained weight recognition model for weight recognition operation to obtain weight data of the target to be detected in the second image. The embodiment of the application utilizes the reference object with the fixed size, calculates the size of the reference object in the shot first image, calculates the scaling ratio, scales the first image according to the obtained scaling ratio, obtains the second image with the unified standard ratio, and then utilizes the weight recognition model trained in advance to recognize the weight parameters of the second image, so that frequent direct contact between a worker and livestock can be avoided, and the acquisition process of the weight parameters is simple and high in accuracy.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic flow chart of a livestock weight identification method provided in an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a livestock weight recognition device provided in the embodiment of the application.
Fig. 3 is another schematic structural diagram of the livestock weight recognition device provided by the embodiment of the application.
Fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without inventive step, are within the scope of the present application.
The embodiment of the application provides a livestock weight identification method, and the livestock weight identification method can be applied to terminal equipment. The terminal equipment can be equipment such as a smart phone and a computer.
In order to obtain the weight parameters of the livestock, the traditional method is to directly measure the livestock by means of a weight scale. The obtained weight parameters can be applied to various scenes, for example, livestock bred by farmers die after the insurance of livestock breeding insurance is put into operation, and if the conditions of claim settlement are met, the compensation can be obtained according to the weight. The livestock insurance usually takes the weight of the livestock as a basis for settlement, and different weight intervals are paid according to different premium proportions. Because traditional survey flow is for the scene is taken a picture, register ear mark number to use weighing equipment manual measurement livestock weight parameter, perhaps combine to measure livestock length with the tape measure and speculate weight, in order to obtain accurate reimbursement numerical value, it is loaded down with trivial details to use weighing equipment weight measurement process, especially the manual work carries out weight parameter measurement in-process and is inevitable with the carcass direct contact, in case the livestock carries the virus and will infect for the staff, is unfavorable for the health and safety who ensures the staff.
In order to solve the technical problem, the embodiment of the application provides a livestock weight identification method. By utilizing the livestock weight identification method provided by the embodiment of the application, the scaling of the reference object is calculated, the first image is scaled according to the obtained scaling, after the second image with the unified standard scale is obtained, the weight parameter identification is carried out on the second image by utilizing the pre-trained weight identification model, the frequent direct contact between the working personnel and the livestock can be avoided, and the acquisition process of the weight parameter is simple and the accuracy is high.
Referring to fig. 1, fig. 1 is a schematic flow chart of a livestock weight identification method according to an embodiment of the present application. The livestock weight identification method is applied to terminal equipment, and the method can comprise the following steps:
step 101, obtaining a first image, wherein the first image comprises a target to be detected and a reference object bound with the target to be detected.
It should be noted that the target to be measured refers to livestock. By using the method, the weight parameters of the livestock can be quickly acquired without adopting a traditional acquisition mode, such as directly measuring the weight parameters of the livestock by using a weight scale. The obtained weight parameters can be applied to various scenes, for example, livestock bred by farmers die after the insurance of livestock breeding insurance is put into operation, and if the conditions of claim settlement are met, the compensation can be obtained according to the weight. The livestock insurance usually takes the weight of the livestock as the basis for claim settlement, and different weight intervals are paid according to different quota proportions. Because traditional survey flow is for the scene is shot, register the ear mark number to use weighing-appliance manual measurement livestock weight parameter, perhaps combine to measure the livestock length with the tape measure and speculate weight, in order to obtain accurate claim money numerical value, it is loaded down with trivial details to use weighing-appliance weight measurement process, especially the manual work carries out in the weight parameter measurement process unavoidably with carcass direct contact, in case the livestock carries the virus and will infect for the staff, is unfavorable for the health and safety of guarantee staff.
Taking the above scenario as an example, in order to reduce direct contact of the worker with the livestock during the measurement process, the purpose of automatically identifying the weight parameters of the livestock is also achieved by acquiring images of the livestock on site and based on a pre-trained neural network model at present. However, in an actual application scenario, when shooting and evidence obtaining are performed on livestock, workers are usually required to hold the camera to shoot the livestock, so that the shot images are affected by the shooting distance, the camera focal length and other influence factors, the pixel size of the same livestock in the images cannot be guaranteed to be the same, and the original images cannot be directly used for training the neural network model. Therefore, in order to establish a standard for the image, the individual size difference between different livestock can be embodied through pixels, the scheme utilizes a reference object with a fixed size, the size of the reference object in the shot image is firstly calculated, then a scaling ratio is obtained, the shot image is scaled according to the obtained scaling ratio, after the image with the uniform standard ratio is obtained, the weight parameter identification is carried out on the image with the same standard size through a pre-trained neural network model.
Before the staff obtains the livestock weight parameter, need keep flat on dead livestock with the reference thing, the reference thing can be the object of fixed size, generally selects the shape rule like circular or rectangle, and the colour is bright single and has the object of great colour difference with the livestock skin color, makes things convenient for follow-up to detect the reference thing like this. In the scheme, 1 yuan coins are preferably used as reference objects. So as not to fall off and be able to be captured by an image capturing device (e.g., a camera) as an optimal position.
After the reference object is placed, the worker starts the image shooting equipment to obtain the image data of the livestock, namely the first image. It should be noted that the image shooting device should ensure that the livestock can be shot vertically downwards from the top of the livestock, and the whole body of the livestock is ensured to be within the shooting range during shooting.
In some embodiments, prior to said acquiring the first image, the method further comprises:
acquiring an initial image;
inputting the initial image into a pre-trained target recognition model to perform target recognition operation, so as to obtain a preprocessed image comprising at least one preset mark frame, wherein the preset mark frame is used for indicating the target to be detected or a reference object;
and carrying out image segmentation on the preprocessed image to obtain at least one first image, wherein only one preset mark frame of the same category is arranged in each first image.
In this embodiment, the target recognition model may be obtained by training based on a model such as CNN, and a target recognition model with the capability of recognizing a target type is obtained by training any of the above listed models. The initial image refers to an unprocessed image shot by an image shooting device, and a preprocessed image comprising at least one preset mark frame can be obtained after the initial image is subjected to target recognition operation through a target recognition model. It should be explained that the preset marking frame is automatically generated based on a third-party marking tool, after the target identification model is identified, the target to be detected and the reference object are selected by the third-party marking tool frame on the preprocessed image, and targets of different types can be represented by marking frames with different colors or shapes so as to be distinguished.
After the preprocessed image is obtained, since the preprocessed image may include more than one target to be detected and there may be multiple targets to be detected, in order to facilitate subsequent detection of the reference object and reduce errors in the detection result due to the multiple targets to be detected existing in the same image, in this embodiment, an image segmentation technology is used to perform image segmentation on the preprocessed image to obtain at least one first image, where only one preset mark frame of the same category is in each first image.
In some embodiments, the preset mark frame includes a first mark frame for indicating the target to be measured and a second mark frame for indicating the reference object, and after the image segmentation processing is performed on the preprocessed image, the method further includes:
judging whether an intermediate image obtained by image segmentation processing of the preprocessed image simultaneously contains the first mark frame and the second mark frame;
and taking an intermediate image which simultaneously comprises the first mark frame and the second mark frame as the first image.
In this embodiment, since the present solution can be implemented by mainly relying on the detection analysis of the target to be detected and the reference object, in order to ensure that the subsequent detection of the target to be detected is performed successfully, it is necessary to determine that the acquired first image includes both the target to be detected and the reference object.
Specifically, the intermediate image including the first mark frame and the second mark frame at the same time is used as the first image by judging whether the intermediate image obtained by the image segmentation processing of the preprocessed image includes the first mark frame and the second mark frame at the same time.
When the first mark frame and the second mark frame are not simultaneously present in the intermediate image, it is indicated that the reference object may fall off from the object to be detected, and if the reference object is absent in the first image, the subsequent acquisition of the weight parameter of the object to be detected is directly influenced. At this time, abnormity reminding information needs to be generated and sent to the working personnel in time, and the abnormity of the binding between the target to be detected and the reference object is indicated.
And 102, determining the scaling of the target to be measured relative to a preset size based on the position coordinates of the reference object in the first image.
In this embodiment, the vertex coordinates of the preset mark frame corresponding to the reference object in the first image are obtained, the size parameter of the reference object in the first image is calculated according to the vertex coordinates, and the scaling ratio is calculated according to the size parameter and the preset size of the reference object. It should be noted that the preset size refers to a predetermined value, and may be a size parameter smaller than the reference object in the first image, or may be the real size of the reference object.
Specifically, first, coordinates (x) of a preset mark frame (here, a rectangular frame is taken as an example) corresponding to the reference object in the first image are obtained 1 ,y 1 )、(x 2 ,y 2 ) Wherein (x) 1 ,y 1 ) (x) a coordinate point representing the upper left corner of the preset mark box 2 ,y 2 ) A coordinate point representing the lower right corner of the preset mark frame, and the calculated diameter d ═ x 2 -x 1 +y 2 -y 1 ) And/2, calculating the image scaling ratio r to be 10/d.
In a preferred embodiment, before the inputting the second image to a weight recognition model trained in advance for a weight recognition operation, the method further includes:
acquiring color component values of all channels of each pixel point in the second image;
mirror filling is performed on the color component values of all channels, respectively, so that the size of the second image is mxn.
In this embodiment, the frame of the zoomed second image is filled with a mirror image, and the filled pixel value is (0,0,0), so that the size of the second image is mxn (for example, 448 × 448), and the width of the filling on the left and right sides of the second image is the same, and in the same way, the target to be detected is integrally present at the middle position of the second image, which is convenient for the subsequent identification and detection of the target to be detected during the weight parameter identification operation.
And 103, adjusting the size of the first image according to the scaling to obtain a second image.
In this embodiment, the first image is resized according to the scaling ratio, and a second image having a scaled size that matches a predetermined size is obtained.
In a preferred embodiment, the first image may be further intercepted according to the coordinates of the preset mark frame corresponding to the target to be detected, and then the first image is scaled based on the scaling ratio to obtain the second image.
And 104, inputting the second image into a pre-trained weight recognition model for weight recognition operation to obtain weight data of the target to be detected in the second image.
It should be noted that the weight recognition model can be obtained based on model training based on CNN and the like.
CNN, a structure modeled by local information such as CNN, fully uses spatial locality and translation etc. due to its powerful visual representation learning ability. ResNet, the outstanding among CNN models, solves the degradation problem of the deep network, successfully trains a very deep neural network model by introducing a residual module, and achieves very excellent performance on ImageNet at that time.
The common model of ResNet is ResNet50 and ResNet101, and 50 and 101 respectively represent the number of convolutional layers in the model, where ResNet50 is used. The network structure of ResNet50 is composed of a convolutional layer and a pooling layer from head to tail, and a plurality of bottleeck structures with different convolutional kernel sizes are arranged in the middle, and each bottleeck structure is composed of a convolutional layer, a BN layer and an activation function. Regression training was performed on ResNet50, with the output being the weight parameter. The mean square error loss function MSE (mean Squared error) is adopted as the loss function, and the calculation mode of the MSE is as follows
Figure BDA0003615412060000111
Where m is the number of samples, y i Is the weight data corresponding to the image,
Figure BDA0003615412060000112
and the output value of the last full connection layer of the neural network. And performing iterative training on all sample data in batches, and stopping training when the MSE error is smaller than a threshold value, wherein the model at the moment is the final weight recognition model.
The training process of the weight recognition model comprises the following steps:
acquiring a training sample of a weight recognition model to be trained, wherein the training sample comprises image data provided with a label, and the label is used for indicating a weight parameter of the image data;
performing feature extraction on image data in the training sample through the weight recognition model to be trained to obtain an image feature vector corresponding to the image data;
identifying weight parameters of image data in the training sample based on the image feature vectors through the weight identification model to be trained to obtain an identification result of the image data;
and adjusting parameters of the weight recognition model to be trained based on the recognition result and the label of the image data to obtain the pre-trained weight recognition model.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
In particular implementation, the present application is not limited by the execution sequence of the described steps, and some steps may be performed in other sequences or simultaneously without conflict.
According to the method for identifying the weight of the livestock, which is provided by the embodiment of the application, a plurality of text data of the target to be detected in the preset time period are obtained; carrying out state discrimination operation on the target data through a pre-trained weight recognition model to obtain the existence state of the target to be detected; and judging whether to send out reminding information according to the deposit and death state. The embodiment of the application utilizes the reference object with the fixed size, calculates the size of the reference object in the shot first image, calculates the scaling ratio, scales the first image according to the obtained scaling ratio, obtains the second image with the unified standard ratio, and then utilizes the weight recognition model trained in advance to recognize the weight parameters of the second image, so that frequent direct contact between a worker and livestock can be avoided, and the acquisition process of the weight parameters is simple and high in accuracy.
The embodiment of the application also provides a livestock weight recognition device, and the livestock weight recognition device can be integrated in the terminal equipment. The terminal equipment can be a television, a smart phone, a tablet computer and other equipment.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a livestock weight recognition device according to an embodiment of the present application. The livestock weight recognition device 30 may comprise:
the first acquiring module 31 is configured to acquire a first image, where the first image includes a target to be detected and a reference object bound to the target to be detected;
the determining module 32 is configured to determine, based on the position coordinates of the reference object in the first image, a scaling of the target to be measured with respect to a preset size;
an adjusting module 33, configured to adjust the size of the first image according to the scaling ratio to obtain a second image;
and the recognition module 34 is configured to input the second image to a pre-trained weight recognition model for weight recognition operation, so as to obtain weight data of the target to be detected in the second image.
In some embodiments, the apparatus further comprises a second acquisition module for acquiring an initial image; inputting the initial image into a pre-trained target recognition model to perform target recognition operation, so as to obtain a preprocessed image comprising at least one preset mark frame, wherein the preset mark frame is used for indicating the target to be detected or a reference object; and carrying out image segmentation on the preprocessed image to obtain at least one first image, wherein only one preset mark frame of the same category is arranged in each first image.
In some embodiments, the apparatus further includes a first determining module, configured to determine whether an intermediate image obtained by image segmentation processing of the preprocessed image includes the first marker frame and the second marker frame at the same time; and taking an intermediate image which simultaneously comprises the first mark frame and the second mark frame as the first image.
In some embodiments, the apparatus further includes a second determining module, configured to generate an abnormality prompting message when it is determined that the first mark frame and the second mark frame do not exist in the intermediate image at the same time, where the abnormality prompting message is used to indicate that the target to be detected and a reference object are abnormally bound.
In some embodiments, the determining module 32 is configured to obtain vertex coordinates of a preset mark frame corresponding to the reference object in the first image; calculating a size parameter of the reference object in the first image according to the vertex coordinates; and calculating the scaling according to the size parameter and the preset size of the reference object.
In some embodiments, the apparatus further comprises a filling module, configured to obtain color component values of all channels of each pixel point in the second image; mirror filling is performed on the color component values of all channels, respectively, so that the size of the second image is mxn.
In some embodiments, the apparatus further includes a pre-training module, configured to obtain a training sample of the weight recognition model to be trained, where the training sample includes image data provided with a label, and the label is used to indicate a weight parameter of the image data; performing feature extraction on image data in the training sample through the weight recognition model to be trained to obtain an image feature vector corresponding to the image data; identifying weight parameters of image data in the training sample based on the image feature vectors through the weight identification model to be trained to obtain an identification result of the image data; and adjusting parameters of the weight recognition model to be trained based on the recognition result and the label of the image data to obtain the pre-trained weight recognition model.
In specific implementation, the modules may be implemented as independent entities, or may be combined arbitrarily and implemented as one or several entities.
As can be seen from the above, in the livestock weight recognition device 30 provided in the embodiment of the present application, the first obtaining module 31 obtains the first image, where the first image includes the target to be detected and the reference object bound to the target to be detected; the determining module 32 determines a scaling of the target to be measured relative to a preset size based on the position coordinates of the reference object in the first image; the adjusting module 33 adjusts the size of the first image according to the scaling ratio to obtain a second image; the recognition module 34 inputs the second image into a pre-trained weight recognition model for weight recognition operation, so as to obtain weight data of the target to be detected in the second image.
Referring to fig. 3, fig. 3 is another schematic structural diagram of the livestock weight recognition device according to the embodiment of the present application, the livestock weight recognition device 30 includes a memory 120, one or more processors 180, and one or more applications, wherein the one or more applications are stored in the memory 120 and configured to be executed by the processor 180; the processor 180 may include a first acquisition module 31, a determination module 32, an adjustment module 33, and an identification module 34. For example, the structures and connection relationships of the above components may be as follows:
the memory 120 may be used to store applications and data. The memory 120 stores applications containing executable code. The application programs may constitute various functional modules. The processor 180 executes various functional applications and data processing by running the application programs stored in the memory 120. Further, the memory 120 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 120 may also include a memory controller to provide the processor 180 with access to the memory 120.
The processor 180 is a control center of the device, connects various parts of the entire terminal using various interfaces and lines, performs various functions of the device and processes data by running or executing an application program stored in the memory 120 and calling data stored in the memory 120, thereby monitoring the entire device. Optionally, processor 180 may include one or more processing cores; preferably, the processor 180 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, an application program, and the like.
Specifically, in this embodiment, the processor 180 loads the executable code corresponding to the process of one or more application programs into the memory 120 according to the following instructions, and the processor 180 runs the application programs stored in the memory 120, thereby implementing various functions:
the first acquiring module 31 is configured to acquire a first image, where the first image includes a target to be detected and a reference object bound to the target to be detected;
the determining module 32 is configured to determine, based on the position coordinates of the reference object in the first image, a scaling of the target to be measured with respect to a preset size;
an adjusting module 33, configured to adjust the size of the first image according to the scaling ratio to obtain a second image;
and the recognition module 34 is configured to input the second image to a pre-trained weight recognition model for weight recognition operation, so as to obtain weight data of the target to be detected in the second image.
In some embodiments, the apparatus further comprises a second acquisition module for acquiring an initial image; inputting the initial image into a pre-trained target recognition model to perform target recognition operation, so as to obtain a preprocessed image comprising at least one preset mark frame, wherein the preset mark frame is used for indicating the target to be detected or a reference object; and carrying out image segmentation on the preprocessed image to obtain at least one first image, wherein only one preset mark frame of the same category is arranged in each first image.
In some embodiments, the apparatus further includes a first determining module, configured to determine whether an intermediate image obtained by image segmentation processing of the preprocessed image includes the first marker frame and the second marker frame at the same time; and taking an intermediate image which simultaneously comprises the first mark frame and the second mark frame as the first image.
In some embodiments, the apparatus further includes a second determining module, configured to generate an abnormality prompting message when it is determined that the first mark frame and the second mark frame do not exist in the intermediate image at the same time, where the abnormality prompting message is used to indicate that the target to be detected and a reference object are abnormally bound.
In some embodiments, the determining module 32 is configured to obtain vertex coordinates of a preset mark frame corresponding to the reference object in the first image; calculating a size parameter of the reference object in the first image according to the vertex coordinates; and calculating the scaling according to the size parameter and the preset size of the reference object.
In some embodiments, the apparatus further comprises a filling module, configured to obtain color component values of all channels of each pixel point in the second image; mirror filling is performed on the color component values of all channels, respectively, so that the size of the second image is mxn.
In some embodiments, the apparatus further includes a pre-training module, configured to obtain a training sample of the weight recognition model to be trained, where the training sample includes image data provided with a label, and the label is used to indicate a weight parameter of the image data; performing feature extraction on image data in the training sample through the weight recognition model to be trained to obtain an image feature vector corresponding to the image data; identifying weight parameters of image data in the training sample based on the image feature vectors through the weight identification model to be trained to obtain an identification result of the image data; and adjusting parameters of the weight recognition model to be trained based on the recognition result and the label of the image data to obtain the pre-trained weight recognition model.
The embodiment of the application also provides the terminal equipment. The terminal equipment can be equipment such as a smart phone, a computer and a tablet computer.
Referring to fig. 4, fig. 4 shows a schematic structural diagram of a terminal device provided in the embodiment of the present application, where the terminal device may be used to implement the livestock weight identification method provided in the foregoing embodiment. The terminal device 1200 may be a television, a smart phone, or a tablet computer.
As shown in fig. 4, the terminal device 1200 may include an RF (Radio Frequency) circuit 110, a memory 120 including one or more computer-readable storage media (only one shown in the figure), an input unit 130, a display unit 140, a sensor 150, an audio circuit 160, a transmission module 170, a processor 180 including one or more processing cores (only one shown in the figure), and a power supply 190. Those skilled in the art will appreciate that the terminal device 1200 configuration shown in fig. 4 does not constitute a limitation of terminal device 1200, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components. Wherein:
the RF circuit 110 is used for receiving and transmitting electromagnetic waves, and performs interconversion between the electromagnetic waves and electrical signals, so as to communicate with a communication network or other devices. The RF circuitry 110 may include various existing circuit elements for performing these functions, such as an antenna, a radio frequency transceiver, a digital signal processor, an encryption/decryption chip, a Subscriber Identity Module (SIM) card, memory, and so forth. The RF circuitry 110 may communicate with various networks such as the internet, an intranet, a wireless network, or with other devices over a wireless network.
The memory 120 can be used for storing software programs and modules, such as program instructions/modules corresponding to the livestock weight identification method in the above embodiment, and the processor 180 executes various functional applications and data processing by running the software programs and modules stored in the memory 120, can automatically select a vibration reminding mode according to the current scene where the terminal device is located to identify the livestock weight, can ensure that scenes such as a conference and the like are not disturbed, can ensure that a user can sense an incoming call, and improves the intelligence of the terminal device. Memory 120 may include high speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 120 may further include memory located remotely from the processor 180, which may be connected to the terminal device 1200 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input unit 130 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, the input unit 130 may include a touch-sensitive surface 131 as well as other input devices 132. The touch-sensitive surface 131, also referred to as a touch display screen or a touch pad, may collect touch operations by a user on or near the touch-sensitive surface 131 (e.g., operations by a user on or near the touch-sensitive surface 131 using a finger, a stylus, or any other suitable object or attachment), and drive the corresponding connection device according to a predetermined program. Alternatively, the touch sensitive surface 131 may comprise two parts, a touch detection means and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 180, and can receive and execute commands sent by the processor 180. Additionally, the touch-sensitive surface 131 may be implemented using various types of resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch-sensitive surface 131, the input unit 130 may also include other input devices 132. In particular, other input devices 132 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.
The display unit 140 may be used to display information input by or provided to a user and various graphic user interfaces of the terminal apparatus 1200, which may be configured by graphics, text, icons, video, and any combination thereof. The Display unit 140 may include a Display panel 141, and optionally, the Display panel 141 may be configured in the form of an LCD (Liquid Crystal Display), an OLED (Organic Light-Emitting Diode), or the like. Further, the touch-sensitive surface 131 may cover the display panel 141, and when a touch operation is detected on or near the touch-sensitive surface 131, the touch operation is transmitted to the processor 180 to determine the type of the touch event, and then the processor 180 provides a corresponding visual output on the display panel 141 according to the type of the touch event. Although in FIG. 4, touch-sensitive surface 131 and display panel 141 are shown as two separate components to implement input and output functions, in some embodiments, touch-sensitive surface 131 may be integrated with display panel 141 to implement input and output functions.
The terminal device 1200 may also include at least one sensor 150, such as light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel 141 according to the brightness of ambient light, and a proximity sensor that may turn off the display panel 141 and/or the backlight when the terminal device 1200 is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when the mobile phone is stationary, and can be used for applications of recognizing the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which may be further configured in the terminal device 1200, detailed descriptions thereof are omitted.
The audio circuitry 160, speaker 161, microphone 162 may provide an audio interface between the user and the terminal device 1200. The audio circuit 160 may transmit the electrical signal converted from the received audio data to the speaker 161, and convert the electrical signal into a sound signal for output by the speaker 161; on the other hand, the microphone 162 converts the collected sound signal into an electric signal, converts the electric signal into audio data after being received by the audio circuit 160, and then outputs the audio data to the processor 180 for processing, and then to the RF circuit 110 to be transmitted to, for example, another terminal, or outputs the audio data to the memory 120 for further processing. The audio circuitry 160 may also include an earbud jack to provide communication of peripheral headphones with the terminal device 1200.
The terminal device 1200, which may assist the user in sending and receiving e-mails, browsing web pages, accessing streaming media, etc., through the transmission module 170 (e.g., Wi-Fi module), provides the user with wireless broadband internet access. Although fig. 4 shows the transmission module 170, it is understood that it does not belong to the essential constitution of the terminal device 1200, and may be omitted entirely as needed within the scope not changing the essence of the invention.
The processor 180 is a control center of the terminal device 1200, connects various parts of the entire mobile phone by using various interfaces and lines, and performs various functions of the terminal device 1200 and processes data by running or executing software programs and/or modules stored in the memory 120 and calling data stored in the memory 120, thereby performing overall monitoring of the mobile phone. Optionally, processor 180 may include one or more processing cores; in some embodiments, the processor 180 may integrate an application processor, which primarily handles operating systems, user interfaces, applications, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 180.
Terminal device 1200 also includes a power supply 190 for powering the various components, which in some embodiments may be logically coupled to processor 180 via a power management system to manage power discharge and power consumption via the power management system. The power supply 190 may also include any component including one or more of a dc or ac power source, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
Although not shown, the terminal device 1200 may further include a camera (e.g., a front camera, a rear camera), a bluetooth module, and the like, which are not described in detail herein. Specifically, in this embodiment, the display unit 140 of the terminal device 1200 is a touch screen display, and the terminal device 1200 further includes a memory 120, and one or more programs, wherein the one or more programs are stored in the memory 120, and the one or more programs configured to be executed by the one or more processors 180 include instructions for:
the image acquisition method comprises the steps that a first acquisition instruction is used for acquiring a first image, wherein the first image comprises a target to be detected and a reference object bound with the target to be detected;
determining instructions, configured to determine, based on the position coordinates of the reference object in the first image, a scaling of the target to be measured with respect to a preset size;
an adjustment instruction, configured to adjust a size of the first image according to the scaling ratio to obtain a second image;
and the identification instruction is used for inputting the second image to a pre-trained weight identification model to perform weight identification operation, so as to obtain weight data of the target to be detected in the second image.
In some embodiments, the program further comprises second acquisition instructions for acquiring an initial image; inputting the initial image into a pre-trained target recognition model to perform target recognition operation, so as to obtain a preprocessed image comprising at least one preset mark frame, wherein the preset mark frame is used for indicating the target to be detected or a reference object; and carrying out image segmentation on the preprocessed image to obtain at least one first image, wherein only one preset mark frame of the same category is arranged in each first image.
In some embodiments, the program further includes a first determining instruction, configured to determine whether an intermediate image obtained by image segmentation processing of the preprocessed image includes the first marker frame and the second marker frame at the same time; and taking an intermediate image which simultaneously comprises the first mark frame and the second mark frame as the first image.
In some embodiments, the program further includes a second determination instruction, configured to generate abnormality prompting information when it is determined that the first mark frame and the second mark frame do not exist in the intermediate image at the same time, where the abnormality prompting information is used to indicate that the target to be detected and a reference object are abnormally bound.
In some embodiments, the determining instruction is configured to obtain vertex coordinates of a preset mark frame corresponding to the reference object in the first image; calculating a size parameter of the reference object in the first image according to the vertex coordinates; and calculating the scaling according to the size parameter and the preset size of the reference object.
In some embodiments, the program further includes a fill instruction for obtaining color component values of all channels of each pixel point in the second image; mirror filling is performed on the color component values of all channels, respectively, so that the size of the second image is mxn.
In some embodiments, the program further includes pre-training instructions for obtaining a training sample of a weight recognition model to be trained, the training sample including image data provided with a label, the label being used to indicate a weight parameter of the image data; performing feature extraction on image data in the training sample through the weight recognition model to be trained to obtain an image feature vector corresponding to the image data; identifying weight parameters of image data in the training sample based on the image feature vectors through the weight identification model to be trained to obtain an identification result of the image data; and adjusting parameters of the weight recognition model to be trained based on the recognition result and the label of the image data to obtain the pre-trained weight recognition model.
The embodiment of the application also provides the terminal equipment. The terminal equipment can be equipment such as a smart phone and a computer.
As can be seen from the above, an embodiment of the present application provides a terminal device 1200, where the terminal device 1200 executes the following steps:
embodiments of the present application further provide a storage medium, in which a computer program is stored, and when the computer program runs on a computer, the computer executes the livestock weight identification method according to any of the above embodiments.
It should be noted that, for the livestock weight identification method described in the present application, it can be understood by those skilled in the art that all or part of the processes for implementing the livestock weight identification method described in the embodiments of the present application may be implemented by controlling the relevant hardware through a computer program, where the computer program may be stored in a computer readable storage medium, such as a memory of the terminal device, and executed by at least one processor in the terminal device, and the processes of the embodiments of the livestock weight identification method may be included in the execution process. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.
For the livestock weight recognition device of the embodiment of the application, each functional module may be integrated in one processing chip, or each module may exist alone physically, or two or more modules are integrated in one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium, such as a read-only memory, a magnetic or optical disk, or the like.
The livestock weight identification method, the livestock weight identification device, the storage medium and the terminal device provided by the embodiment of the application are described in detail above. The principle and the implementation of the present application are explained herein by applying specific examples, and the above description of the embodiments is only used to help understand the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims (10)

1. A livestock weight identification method is characterized by comprising the following steps:
acquiring a first image, wherein the first image comprises a target to be detected and a reference object bound with the target to be detected;
determining the scaling of the target to be measured relative to a preset size based on the position coordinates of the reference object in the first image;
adjusting the size of the first image according to the scaling to obtain a second image;
and inputting the second image into a pre-trained weight recognition model for weight recognition operation to obtain weight data of the target to be detected in the second image.
2. The livestock weight identification method of claim 1, wherein prior to said acquiring a first image, said method further comprises:
acquiring an initial image;
inputting the initial image into a pre-trained target recognition model to perform target recognition operation, so as to obtain a preprocessed image comprising at least one preset mark frame, wherein the preset mark frame is used for indicating the target to be detected or a reference object;
and performing image segmentation on the preprocessed image to obtain at least one first image, wherein only one preset marking frame of the same category is in each first image.
3. The livestock weight recognition method of claim 2, wherein said preset mark frame comprises a first mark frame for indicating said target to be measured and a second mark frame for indicating said reference object, and after said image segmentation processing on said preprocessed image, said method further comprises:
judging whether an intermediate image obtained by image segmentation processing of the preprocessed image simultaneously contains the first mark frame and the second mark frame;
and taking an intermediate image which simultaneously comprises the first mark frame and the second mark frame as the first image.
4. The livestock weight recognition method of claim 3, wherein after said determining whether said first marker frame and said second marker frame are both included in said intermediate image obtained by image segmentation processing of said preprocessed image, said method further comprises:
and when the first mark frame and the second mark frame do not exist in the intermediate image at the same time, generating abnormal reminding information, wherein the abnormal reminding information is used for indicating that the target to be detected and the reference object are abnormally bound.
5. The livestock weight recognition method of claim 2, wherein said determining a scaling of said target to be measured with respect to a preset size based on position coordinates of said reference object in said first image comprises:
acquiring vertex coordinates of a preset mark frame corresponding to the reference object in the first image;
calculating a size parameter of the reference object in the first image according to the vertex coordinates;
and calculating the scaling according to the size parameter and the preset size of the reference object.
6. The livestock weight recognition method of claim 2, wherein prior to said inputting said second image into a pre-trained weight recognition model for weight recognition operations, said method further comprises:
acquiring color component values of all channels of each pixel point in the second image;
mirror filling is performed on the color component values of all channels, respectively, so that the size of the second image is mxn.
7. The livestock weight recognition method of claim 1, wherein prior to said inputting said second image into a pre-trained weight recognition model for weight recognition operations, said method further comprises:
acquiring a training sample of a weight recognition model to be trained, wherein the training sample comprises image data provided with a label, and the label is used for indicating a weight parameter of the image data;
performing feature extraction on image data in the training sample through the weight recognition model to be trained to obtain an image feature vector corresponding to the image data;
identifying weight parameters of image data in the training sample based on the image feature vectors through the weight identification model to be trained to obtain an identification result of the image data;
and adjusting parameters of the weight recognition model to be trained based on the recognition result and the label of the image data to obtain the pre-trained weight recognition model.
8. A livestock weight recognition device, characterized by comprising:
the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a first image, and the first image comprises a target to be detected and a reference object bound with the target to be detected;
the determining module is used for determining the scaling of the target to be measured relative to a preset size based on the position coordinates of the reference object in the first image;
the adjusting module is used for adjusting the size of the first image according to the scaling to obtain a second image;
and the recognition module is used for inputting the second image into a pre-trained weight recognition model to perform weight recognition operation so as to obtain weight data of the target to be detected in the second image.
9. A computer readable storage medium storing instructions adapted to be loaded by a processor to perform the method of animal weight identification according to any one of claims 1 to 7.
10. A terminal device, characterized by comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor loads the instructions to execute the livestock weight recognition method according to any one of claims 1 to 7.
CN202210445427.7A 2022-04-26 2022-04-26 Livestock weight identification method and device, storage medium and terminal equipment Pending CN114863478A (en)

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