CN112541432A - Video livestock identity authentication system and method based on deep learning - Google Patents
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Abstract
The invention discloses a video livestock identity authentication system and method based on deep learning, and relates to the technical field of deep learning. The system comprises a livestock identity establishing module, a video processing module, a livestock detecting module, a livestock tracking module, a livestock identity identifying module and a visual recording module; the method comprises the following steps: s1, acquiring video pictures by livestock; s2, manual marking; s3, training a detection model; s4, training a livestock identity recognition model; s5, feature storage and image data storage; s6, using the identity authentication model to extract the characteristics and compare the characteristics with the livestock with the established library; s7, identifying the livestock identity by using a tracking module; and S8, effectively identifying unknown livestock. The invention can be compatible with livestock in various activity states, has high identification accuracy, can be shot by a mobile phone conveniently and quickly, can be used for feeding management of farmers and insurance company insurance claim settlement and driving protection, and realizes efficient and accurate management and control.
Description
Technical Field
The invention belongs to the technical field of deep learning, and particularly relates to a video livestock identity authentication system based on deep learning and a video livestock identity authentication method based on deep learning.
Background
For the traditional livestock industry, effective management of livestock and control of the dynamics of livestock are of great importance, and with the national emphasis on the livestock industry, the breeding insurance is successively released in a plurality of areas of China so as to reduce the loss of farmers. As a policy insurance benefiting three farmers, the livestock insurance can effectively reduce the disaster risks caused by various diseases or natural disasters and enhance the capability of farmers in resisting the natural disasters and coping with the risks. In order to better serve farmers and guarantee the benefits of insurance companies, the identification work of the insurable livestock needs to be done.
The livestock insurance uses cattle, horses, mules, donkeys, pigs and other livestock as insurance targets. Livestock insurance is one of the agricultural insurance, and entails the loss of livestock due to death from disease or other accidents (wind, rain, water, fire, etc.). The traditional insurance claim settlement evidence collection mainly combines two modes of ear tag number and photo comparison. The ear tag number is usually taken as a unique identification mark for confirming the identity of the livestock, after the livestock is in danger, an indemnifier firstly confirms whether the livestock is a security mark according to the ear tag number, and then compares live livestock photos with dead livestock photos through naked eyes, but the ear tag number is added manually, so that the problems of falling off and manual change in the feeding process can occur, and the photos are easily misjudged by comparing the photos through the naked eyes.
With the introduction and popularization of network and computer technologies, deep learning drives the rapid development and landing of artificial intelligence, and in recent years, a method for carrying out livestock identity authentication by adopting deep learning is not rarely tried, but livestock is photographed and identified in most cases.
Traditional aquaculture manages the livestock or the insurance trade certifies the livestock of insuring, generally speaking for every livestock customization an ear tag that symbolizes the livestock identity, the ear tag drops easily or can be changed by the people, and the binding process causes certain injury to the animal, also propose the mode that adopts the degree of depth learning method to carry out livestock identity authentication, but carry out data acquisition in the breeding process and be difficult to gather the data that accord with the image quality requirement, the acquisition process is more difficult, it is bigger to the authentication stage examination.
The invention provides a video livestock identity authentication system and method based on deep learning aiming at the defects of the traditional livestock identity identification.
Disclosure of Invention
The invention provides a video livestock identity authentication system and method based on deep learning, which solve the problems.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention discloses a video livestock identity authentication system based on deep learning, which comprises a livestock identity establishing module, a video processing module, a livestock detecting module, a livestock tracking module, a livestock identity recognition module and a visual recording module, wherein the livestock identity establishing module is used for establishing a video file;
the livestock identity creation module: the system is used for creating identities for livestock needing to be built and storing livestock image data in a warehouse;
the video processing module: the livestock video processing module is used for processing shot livestock video pictures and sending the processed video pictures to the livestock detection module;
the livestock detection module: the system is used for detecting and identifying livestock individuals and livestock faces in the video pictures sent by the video processing module, and livestock ear tags can be detected if the original livestock is bound with the ear tags;
the livestock tracking module: the livestock detection device is used for judging the livestock with the same head in the continuous frame frames according to the livestock detection result of the continuous frame video frames;
the livestock identity recognition module: the system is used for identifying the identity of each tracked livestock, and the identity is completed in the livestock data acquisition and warehousing stage;
the visual recording module: the livestock image display method is used for displaying the number of the livestock identified in the shot video, displaying the identity number of each head of livestock identified in the video picture, the image information for identifying the head of livestock in the video and the image information of the livestock during early warehousing, and allowing a viewer to verify the identification accuracy.
A video livestock identity authentication method based on deep learning comprises the following steps:
s1, acquiring video pictures of livestock to be subjected to identity recognition or library building in a breeding plant, and acquiring livestock data in different activity scenes and different time periods;
s2, carrying out manual marking on the collected video information, marking livestock individuals and livestock faces in the video pictures, and marking ear tags if the ear tags exist, wherein the marking process is to mark positions and types, and the types comprise the livestock individuals, the livestock faces and the livestock ear tags;
s3, training the detection model of the manually marked data by adopting a yolov4 target detection model;
s4, cutting all livestock face pictures in the data after the artificial marking to obtain multiple livestock face picture data of each livestock in different states, and training an livestock identity recognition model by adopting a deep learning method;
s5, performing feature warehousing and image data warehousing on the livestock needing to be warehoused by using the trained detection model and the trained identity recognition model;
s6, when shooting a video for identity recognition, firstly, detecting livestock individuals and livestock faces by using a detection model, tracking the same livestock by using iou tracking on the detected livestock individuals with continuous frames, extracting features of the detected livestock faces by using an identity authentication model, comparing the extracted features with the livestock with a built library, calculating the distance between vectors, and determining the identity of the livestock;
s7, tracking the same livestock by using the tracking module, recognizing the livestock identity by using the continuous frames, taking the continuous frames to recognize the identity with the most identity in the process of recognizing the identity of the same livestock, improving the recognition accuracy again on the recognized relatively accurate result, having high fault tolerance and being capable of recognizing each activity state of the livestock;
s8, in the process of video shooting identification, if livestock individuals which are not warehoused appear, unknown livestock can be effectively identified through a threshold value set by feature matching, and if new livestock need to be warehoused, the livestock can be warehoused in one key.
Further, the video processing module is used for performing segmentation processing on the video image and segmenting the video into image data according to the number of frames.
Further, the step S2 is specifically to perform manual labeling on a plurality of image data obtained by segmenting the acquired video image.
Further: the step S3 performs data cleaning on all video frames before training, including very fuzzy discarding, discarding of livestock individuals in the video frames, and performs data enhancement on the marked image data, wherein the data enhancement includes but is not limited to image brightness change, clipping, rotation, and noising.
Further, in the step S5, the livestock face data after being manually marked is deducted to train a deep learning classification model, the livestock identity recognition model is trained by using resnet50, including building a neural network model, and by using the idea of face recognition, a loss function is independently modified, center _ loss is used to reduce the distance between different images of each livestock, and amsoftmax is used to effectively distinguish different livestock.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, a deep learning method is adopted to detect and identify shot livestock video pictures in a farm to realize the functions of video livestock individual counting and identity authentication, the identification result is transmitted to a visual management interface, visual verification of identification identity can be carried out, model training is not required to be carried out again for newly appeared livestock individuals, identity authentication can be carried out on the newly appeared individuals only by characteristic warehousing, video shooting of livestock is carried out, livestock under various activity states can be compatible, the identification accuracy is high, mobile phone shooting can be convenient and rapid, and the method can be used for raising management of farmers and application of protection claims and driving protection of insurance companies, and efficient and accurate management and control are realized.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a deep learning based video livestock identity authentication system of the present invention;
FIG. 2 is a step diagram of a deep learning-based video livestock identity authentication method according to the present invention;
fig. 3 is a schematic diagram of a video livestock identity authentication method based on deep learning according to the 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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The invention aims to provide a video livestock identity authentication system and method based on deep learning, which can realize high-efficiency and accurate counting in livestock breeding management and can help the insurance industry to authenticate the identity of an insuring livestock to avoid cheating insurance. According to the invention, livestock in a shot video picture is accurately detected and identified, so that the high efficiency, accuracy, intellectualization and visualization of supervision in the breeding and insurance industry are ensured.
Referring to fig. 1, the video livestock identity authentication system based on deep learning of the present invention includes a livestock identity creation module, a video processing module, a livestock detection module, a livestock tracking module, a livestock identity recognition module, and a visual recording module;
a livestock identity creation module: the system is used for creating identities for livestock needing to be built and storing livestock image data in a warehouse;
the video processing module: the livestock video processing module is used for processing shot livestock video pictures and sending the processed video pictures to the livestock detection module;
livestock detection module: the system is used for detecting and identifying livestock individuals and livestock faces in the video pictures sent by the video processing module, and livestock ear tags can be detected if the original livestock is bound with the ear tags;
a livestock tracking module: the livestock detection device is used for judging the livestock with the same head in the continuous frame frames according to the livestock detection result of the continuous frame video frames;
livestock identity identification module: the system is used for identifying the identity of each tracked livestock, and the identity is completed in the livestock data acquisition and warehousing stage;
a visual recording module: the livestock image display method is used for displaying the number of the livestock identified in the shot video, displaying the identity number of each head of livestock identified in the video picture, the image information for identifying the head of livestock in the video and the image information of the livestock during early warehousing, and allowing a viewer to verify the identification accuracy.
As shown in fig. 2, a video livestock identity authentication method based on deep learning includes the following steps:
s1, acquiring video pictures of livestock to be subjected to identity recognition or library building in a breeding plant, and acquiring livestock data in different activity scenes and different time periods;
s2, carrying out manual marking on the collected video information, marking livestock individuals and livestock faces in the video pictures, and marking ear tags if the ear tags exist, wherein the marking process is to mark positions and types, and the types comprise the livestock individuals, the livestock faces and the livestock ear tags;
s3, training the detection model of the manually marked data by adopting a yolov4 target detection model;
s4, cutting all livestock face pictures in the data after the artificial marking to obtain multiple livestock face picture data of each livestock in different states, and training an livestock identity recognition model by adopting a deep learning method;
s5, performing feature warehousing and image data warehousing on the livestock needing to be warehoused by using the trained detection model and the trained identity recognition model;
s6, when shooting a video for identity recognition, firstly, detecting livestock individuals and livestock faces by using a detection model, tracking the same livestock by using iou tracking on the detected livestock individuals with continuous frames, extracting features of the detected livestock faces by using an identity authentication model, comparing the extracted features with the livestock with a built library, calculating the distance between vectors, and determining the identity of the livestock;
s7, tracking the same livestock by using the tracking module, recognizing the livestock identity by using the continuous frames, taking the continuous frames to recognize the identity with the most identity in the process of recognizing the identity of the same livestock, improving the recognition accuracy again on the recognized relatively accurate result, having high fault tolerance and being capable of recognizing each activity state of the livestock;
s8, in the process of video shooting identification, if livestock individuals which are not warehoused appear, unknown livestock can be effectively identified through a threshold value set by feature matching, and if new livestock need to be warehoused, the livestock can be warehoused in one key.
The video processing module is used for carrying out segmentation processing on the video image and segmenting the video into image data according to the number of frames.
Step S2 is specifically to perform manual labeling on a plurality of image data obtained by segmenting the acquired video image.
Wherein, step S3 performs data cleaning on all video frames before training, including very fuzzy discarding, discarding of livestock individuals in the video frames, and performs data enhancement on the marked image data, wherein the data enhancement includes but is not limited to image brightness change, cropping, rotation, and noising.
Specifically, in the step S5, the livestock face data after being manually marked is deducted to perform deep learning classification model training, the livestock identity recognition model is trained by using resnet50, the training includes building a neural network model, the idea of face recognition is used for reference, a loss function is independently modified, the center _ loss is used for reducing the distance between different images of each livestock, and the amsoftmax is used for effectively distinguishing different livestock.
Has the advantages that:
according to the method, a deep learning method is adopted to detect and identify shot livestock video pictures in a farm to realize the functions of video livestock individual counting and identity authentication, the identification result is transmitted to a visual management interface, visual verification of identification identity can be carried out, model training is not required to be carried out again for newly appeared livestock individuals, identity authentication can be carried out on the newly appeared individuals only by characteristic warehousing, video shooting of livestock is carried out, livestock under various activity states can be compatible, the identification accuracy is high, mobile phone shooting can be convenient and rapid, and the method can be used for raising management of farmers and application of protection claims and driving protection of insurance companies, and efficient and accurate management and control are realized.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. A video livestock identity authentication system based on deep learning is characterized by comprising a livestock identity creating module, a video processing module, a livestock detecting module, a livestock tracking module, a livestock identity recognition module and a visual recording module;
the livestock identity creation module: the system is used for creating identities for livestock needing to be built and storing livestock image data in a warehouse;
the video processing module: the livestock video processing module is used for processing shot livestock video pictures and sending the processed video pictures to the livestock detection module;
the livestock detection module: the system is used for detecting and identifying livestock individuals and livestock faces in the video pictures sent by the video processing module, and livestock ear tags can be detected if the original livestock is bound with the ear tags;
the livestock tracking module: the livestock detection device is used for judging the livestock with the same head in the continuous frame frames according to the livestock detection result of the continuous frame video frames;
the livestock identity recognition module: the system is used for identifying the identity of each tracked livestock, and the identity is completed in the livestock data acquisition and warehousing stage;
the visual recording module: the livestock image display method is used for displaying the number of the livestock identified in the shot video, displaying the identity number of each head of livestock identified in the video picture, the image information for identifying the head of livestock in the video and the image information of the livestock during early warehousing, and allowing a viewer to verify the identification accuracy.
2. A video livestock identity authentication method based on deep learning is characterized by comprising the following steps:
s1, acquiring video pictures of livestock to be subjected to identity recognition or library building in a breeding plant, and acquiring livestock data in different activity scenes and different time periods;
s2, carrying out manual marking on the collected video information, marking livestock individuals and livestock faces in the video pictures, and marking ear tags if the ear tags exist, wherein the marking process is to mark positions and types, and the types comprise the livestock individuals, the livestock faces and the livestock ear tags;
s3, training the detection model of the manually marked data by adopting a yolov4 target detection model;
s4, cutting all livestock face pictures in the data after the artificial marking to obtain multiple livestock face picture data of each livestock in different states, and training an livestock identity recognition model by adopting a deep learning method;
s5, performing feature warehousing and image data warehousing on the livestock needing to be warehoused by using the trained detection model and the trained identity recognition model;
s6, when shooting a video for identity recognition, firstly, detecting livestock individuals and livestock faces by using a detection model, tracking the same livestock by using iou tracking on the detected livestock individuals with continuous frames, extracting features of the detected livestock faces by using an identity authentication model, comparing the extracted features with the livestock with a built library, calculating the distance between vectors, and determining the identity of the livestock;
s7, tracking the same livestock by using the tracking module, recognizing the livestock identity by using the continuous frames, taking the continuous frames to recognize the identity with the most identity in the process of recognizing the identity of the same livestock, improving the recognition accuracy again on the recognized relatively accurate result, having high fault tolerance and being capable of recognizing each activity state of the livestock;
s8, in the process of video shooting identification, if livestock individuals which are not warehoused appear, unknown livestock can be effectively identified through a threshold value set by feature matching, and if new livestock need to be warehoused, the livestock can be warehoused in one key.
3. The video livestock identity authentication method based on deep learning of claim 2, wherein the video processing module is used for performing segmentation processing on video images, and segmenting the video into image data according to the number of frames.
4. The video livestock identity authentication method based on deep learning of claim 2, wherein said step S2 is specifically to perform manual labeling on a plurality of image data obtained by segmenting the acquired video image.
5. The video livestock identity authentication method based on deep learning of claim 2, characterized in that: the step S3 performs data cleaning on all video frames before training, including very fuzzy discarding, discarding of livestock individuals in the video frames, and performs data enhancement on the marked image data, wherein the data enhancement includes but is not limited to image brightness change, clipping, rotation, and noising.
6. The video livestock identity authentication method based on deep learning of claim 2, characterized in that: in the step S5, the livestock face data after being manually marked is deducted to train a deep learning classification model, the livestock identity recognition model is trained by using resnet50, the training includes building a neural network model, the idea of face recognition is used for reference, a loss function is independently modified, the center _ loss is used for reducing the distance between different images of each livestock, and the amsoftmax is used for effectively distinguishing different livestock.
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