CN111680551A - Method and device for monitoring livestock quantity, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the application belongs to the technical field of artificial intelligence, and relates to a method for monitoring the quantity of livestock, which comprises the steps of carrying out livestock detection on acquired pictures according to a target detection AI model to obtain a detection frame; intercepting images of single livestock in the detection frame to obtain a livestock sample illustration, and processing the livestock sample illustration through a depth tracking model and a livestock classification model to obtain a tracking detection matching frame, a redundant tracking frame and a tracking compensation missed detection frame; adding the obtained number of tracking compensation missed detection frames and the number of tracking detection matching frames to obtain the actual number of the livestock as a statistical result; comparing the statistical result with the livestock quantity stored in the database; and if the statistical result is inconsistent with the livestock quantity stored in the database, judging that the livestock quantity is abnormal, and sending an abnormal alarm to the user. The application also provides a device, computer equipment and storage medium for monitoring the quantity of livestock. The livestock multi-detection rate and the omission rate are effectively reduced.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a method and a device for monitoring livestock quantity, computer equipment and a storage medium.
Background
The traditional livestock number statistics of the farm completely depends on manual work, the livestock number in each column is recorded by walking to the farm regularly by manual work, and the manual calculation mode is easy to cause calculation errors, so that the situation that the number is difficult to correspond occurs; and when the number of the livestock is found to be abnormal, the cultured personnel can hardly find the reason in time, so that the cultured livestock is lost.
Present modern plant's introduction automation equipment and intelligent software algorithm improve breed efficiency, promote the breed quality, and current most mode adopts camera and single model to combine to carry out the statistics of plant livestock figure and monitor the livestock, and the object identification that probably the mistake is non-livestock is the livestock in the model identification process, and then produces the problem of examining the rate more. In the process of carrying out model statistics on the quantity of the livestock, the condition of missing detection of the livestock is caused due to different models.
Disclosure of Invention
The embodiment of the application aims to provide a method, a device, computer equipment and a storage medium for monitoring the number of livestock, so that the conditions of multiple detection rates and missed detection rates are reduced.
In order to solve the above technical problem, an embodiment of the present application provides a method for monitoring livestock quantity, which adopts the following technical scheme:
a method of monitoring the quantity of animals comprising the steps of:
controlling a camera installed in a livestock pen to collect videos, and intercepting the videos according to frames to obtain collected pictures;
carrying out livestock detection on the collected pictures according to a pre-trained target detection AI model to obtain at least one detection frame, wherein the content in the detection frame is an image of a single livestock judged by the model;
intercepting images of single livestock in the detection frame to obtain a livestock sample illustration, and comparing all the livestock sample illustrations of the current frame with all the livestock sample illustrations of the previous frame through a pre-trained depth tracking model to obtain a tracking detection matching frame and a redundant tracking frame;
sequentially judging the content in each redundant tracking frame through a pre-trained livestock classification model, and if the content in the redundant tracking frames is judged to be real livestock, taking the redundant tracking frames as tracking compensation missed detection frames;
adding the obtained number of tracking compensation missed detection frames and the number of tracking detection matching frames to obtain the actual number of the livestock as a statistical result;
comparing the statistical result with the livestock quantity stored in a database; and
and if the statistical result is inconsistent with the livestock quantity stored in the database, judging that the livestock quantity is abnormal, and sending an abnormal alarm to the user.
Further, the depth tracking model comprises a livestock re-identification model and a livestock tracking AI model, and the step of comparing all the livestock sample illustrations of the current frame with all the livestock sample illustrations of the previous frame by the pre-trained depth tracking model to obtain a tracking detection matching frame and an unnecessary tracking frame comprises:
carrying out livestock re-identification on the livestock sample drawing through the livestock re-identification model to obtain feature vectors with the number equal to that of the detection frames;
and comparing all the feature vectors of the current frame with all the feature vectors of the previous frame through the motion matching degree and the apparent matching degree in the livestock tracking AI model to obtain a tracking detection matching frame and a redundant tracking frame of the current frame.
Further, the step of comparing all the feature vectors of the current frame with all the feature vectors of the previous frame by the motion matching degree and the apparent matching degree in the livestock tracking AI model to obtain the tracking detection matching frame and the redundant tracking frame of the current frame includes:
inputting the collected picture into the livestock tracking AI model, and outputting a tracking frame by the livestock tracking AI model;
comparing all the feature vectors of the current frame with all the feature vectors of the previous frame through the motion matching degree and the apparent matching degree in the livestock tracking AI model, and taking the detection frame with the same feature vectors of the current frame and the previous frame as a tracking detection matching frame;
and comparing all the feature vectors of the current frame with all the feature vectors in the tracking frame of the previous frame, and taking the tracking frame which is different from the feature vectors of the detection frame of the current frame in the tracking frame of the previous frame as a redundant tracking frame to obtain a tracking detection matching frame and the redundant tracking frame of the current frame.
Further, the step of obtaining the tracking detection matching frame and the redundant tracking frame of the current frame includes:
taking the detection frame with different feature vectors of the current frame and the previous frame as a redundant detection frame;
calculating whether the sum of the number of the tracking detection matching frames and the number of the redundant detection frames is consistent with the number of the current frame detection frames;
if the number of the tracking detection matching frames is consistent, the tracking detection matching frames and the redundant tracking frames are respectively used as the tracking detection matching frames and the redundant tracking frames of the current frame;
if the numbers are inconsistent, the feature vector comparison is repeatedly carried out to obtain the tracking detection matching frame, the redundant tracking frame and the redundant detection frame again for recalculation, and when the recalculated numbers are still inconsistent, an error report is sent to a designated person.
Further, the step of sequentially judging the content in each redundant tracking frame through a pre-trained livestock classification model, and if the content judgment result in the redundant tracking frame is true livestock, taking the redundant tracking frame as a tracking and remedying missed detection frame comprises the following steps:
acquiring the content in the redundant tracking frame of the acquired picture, wherein the content in the redundant tracking frame is an image of a single livestock judged by a model;
classifying the content in each redundant tracking frame in sequence through the livestock classification model;
and judging the redundant tracking frames with the classification probability larger than a preset threshold value as the positions of the real livestock, and acquiring tracking compensation missed detection frames.
Further, the step of judging the redundant tracking frames to be the positions of the real livestock by judging that the classification probability is greater than a preset threshold value comprises the following steps:
calculating classification probability through a classification probability formula, and judging the redundant tracking frames with the classification probability larger than a preset threshold value as the positions of the real livestock;
the classification probability is calculated by the formula
Wherein i is a category, e is a natural index, P is a probability, and Vi is a classification network output value of the livestock classification model corresponding to the category i.
Further, the step of controlling the camera mounted on the livestock pen to perform video acquisition includes:
and controlling a camera arranged in the livestock pen to carry out video acquisition on the livestock pen at the frequency of 1 time/second and the time length of 1 second/time.
In order to solve the technical problem, an embodiment of the present application further provides a device for monitoring the number of livestock, which adopts the following technical scheme:
an apparatus for monitoring the quantity of animals, comprising:
the acquisition module is used for controlling a camera installed in the livestock pen to acquire videos and intercepting the videos according to frames to obtain acquired pictures;
the detection module is used for carrying out livestock detection on the collected pictures according to a pre-trained target detection AI model to obtain at least one detection frame, wherein the content in the detection frame is an image of a single livestock judged by the model;
the acquisition module is used for intercepting images of single livestock in the detection frame to obtain a livestock sample illustration, and comparing all the livestock sample illustrations of the current frame with all the livestock sample illustrations of the previous frame through a pre-trained depth tracking model to obtain a tracking detection matching frame and a redundant tracking frame;
the judging module is used for sequentially judging the content in each redundant tracking frame through a pre-trained livestock classification model, and if the content in the redundant tracking frames is judged to be real livestock, the redundant tracking frames are used as tracking compensation missed detection frames;
the calculation module is used for adding the obtained number of the tracking compensation missed detection frames and the number of the tracking detection matching frames to obtain the actual number of the livestock as a statistical result;
the comparison module is used for comparing the statistical result with the livestock quantity stored in the database; and
and the warning module is used for judging that the quantity of the livestock is abnormal and sending an abnormal alarm to the user if the statistical result is inconsistent with the quantity of the livestock stored in the database.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
a computer arrangement comprising a memory in which a computer program is stored and a processor which, when executing the computer program, carries out the steps of the method of monitoring the number of animals described above.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of monitoring the quantity of animals as described above. Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
this application uses target detection AI model as the basis, introduces degree of depth tracking model and livestock classification model, makes it to obtain the video information that catches with the make full use of camera, through the livestock image that detects of contrast current frame and last frame, and then detects the tracking that will obtain and match the frame and as one of the data of livestock statistics, rather than directly carry out follow-up calculation through the quantity that detects the frame, effectively reduces and examines the rate more. The missing rate is reduced by judging the real livestock in the obtained redundant tracking frames, and the detection accuracy of the model is further improved. Simultaneously, this application can count the livestock quantity in the plant automatically, reports statistics result automatically to reach the automatic real-time early warning's purpose. When the time that livestock are lost occurs in the later stage, the livestock can be helped to find the reason and stop loss in time by finding the time point when the quantity of livestock is predicted to be reduced and playing back the video of the reduction time period.
Drawings
In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a method of monitoring livestock according to the present application;
FIG. 3 is a schematic diagram of the structure of one embodiment of an apparatus for monitoring the quantity of animals according to the present application;
FIG. 4 is a schematic block diagram of one embodiment of a computer device according to the present application.
Reference numerals: 200. a computer device; 201. a memory; 202. a processor; 203. a network interface; 300. means for monitoring the quantity of livestock; 301. an acquisition module; 302. a detection module; 303. an acquisition module; 304. a decision module; 305. a calculation module; 306. a comparison module; 307. and an alarm module.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that the method for monitoring the quantity of livestock provided by the embodiment of the present application is generally performed by a server/terminal device, and accordingly, a device for monitoring the quantity of livestock is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow chart of one embodiment of a method of monitoring livestock according to the present application is shown. The method for monitoring the number of the livestock comprises the following steps:
s1: and controlling a camera arranged in the livestock pen to collect videos, and intercepting the videos according to frames to obtain collected pictures.
In this embodiment, the monitoring cameras are deployed for each livestock ring portion to be monitored, so that the purpose of acquiring the livestock ring video is achieved, wherein each camera is a wide-angle camera to ensure that the images of the whole livestock ring can be acquired, and in the process of acquiring the video, livestock in the livestock ring is not missed, wherein the livestock can be animals such as cattle, sheep and pigs. And intercepting the video according to frames to ensure that the time interval between the acquired collected pictures is small, and avoid the situation that the pictures have larger difference due to long time interval.
Wherein, the step that the camera that control was installed in the livestock pen carries out video acquisition includes:
and controlling a camera arranged in the livestock pen to carry out video acquisition on the livestock pen at the frequency of 1 time/second and the time length of 1 second/time.
In this embodiment, the frame rate of the camera is generally 30 frames or 60 frames, and compared with a scheme of collecting data many times per second, the present application collects data once per second, and reduces the density of data transmission. The duration of the collected video is 1 second, so that the video is not too large, and the transmission speed is improved.
S2: and carrying out livestock detection on the collected pictures according to a pre-trained target detection AI model to obtain at least one detection frame, wherein the content in the detection frame is the image of the single livestock judged by the model.
In this embodiment, the target detection AI model is a general object detection model, which is used as a pre-training model, the target detection AI model is pre-trained based on a data set of livestock types, and the object detection model is a model pre-constructed by using one of the following algorithms: SSD algorithm, Fast RCNN algorithm. The three algorithms are all algorithms in the convolutional neural network technology, and specifically which algorithm is adopted to construct the object detection model can be determined according to the actual requirements of object detection. And the target detection AI model is adopted to ensure that the position of the livestock can be preliminarily detected, and the livestock in the picture is selected out by frames.
S3: and intercepting images of single livestock in the detection frame to obtain a livestock sample illustration, and comparing all the livestock sample illustrations of the current frame with all the livestock sample illustrations of the previous frame through a pre-trained depth tracking model to obtain a tracking detection matching frame and an unnecessary tracking frame.
In the embodiment, the tracking detection matching frame and the redundant tracking frame are determined by comparing the depth tracking models so as to carry out livestock judgment in the following process.
Specifically, the depth tracking model includes a livestock re-identification model and a livestock tracking AI model, and the step of comparing all the livestock sample illustrations of the current frame with all the livestock sample illustrations of the previous frame by the depth tracking model trained in advance to obtain a tracking detection matching frame and an unnecessary tracking frame includes:
carrying out livestock re-identification on the livestock sample drawing through the livestock re-identification model to obtain feature vectors with the number equal to that of the detection frames;
and comparing all the feature vectors of the current frame with all the feature vectors of the previous frame through the motion matching degree and the apparent matching degree in the livestock tracking AI model to obtain a tracking detection matching frame and a redundant tracking frame of the current frame.
In this embodiment, because the moving speed of livestock in a farm is not fast compared with the camera frame rate, the information of the motion matching degree has a good effect on detecting the number of livestock, specifically, the similarity of the motion is firstly obtained through kalman filtering, and the motion matching degree is obtained through cascade matching. And then calculating the apparent matching degree through deep neural network extraction. The matching degree of the detection frame can be obtained frame by frame through the motion matching degree and the apparent matching degree, so that the tracking detection matching frame and the redundant tracking frame of the current frame are obtained according to the matching degree. The Re-recognition model adopts a dense semantic alignment-based Re-recognition model DSA-reiD (Denseely semantic Aligned Person Re-identification), effectively solves the problem of spatial semantic misalignment widely existing in Re-recognition, and obviously improves the algorithm precision of the Re-recognition technology. Dense semantics better solves the problems of different shooting visual angles, large barrier shielding and background difference and the like in practical application.
Further, the step of comparing all the feature vectors of the current frame with all the feature vectors of the previous frame by the motion matching degree and the apparent matching degree in the livestock tracking AI model to obtain the tracking detection matching frame and the redundant tracking frame of the current frame includes:
inputting the collected picture into the livestock tracking AI model, and outputting a tracking frame by the livestock tracking AI model;
comparing all the feature vectors of the current frame with all the feature vectors of the previous frame through the motion matching degree and the apparent matching degree in the livestock tracking AI model, and taking the detection frame with the same feature vectors of the current frame and the previous frame as a tracking detection matching frame;
and comparing all the feature vectors of the current frame with all the feature vectors in the tracking frame of the previous frame, and taking the tracking frame which is different from the feature vectors of the detection frame of the current frame in the tracking frame of the previous frame as a redundant tracking frame to obtain a tracking detection matching frame and the redundant tracking frame of the current frame.
In this embodiment, the method further includes comparing all the feature vectors of the current frame with all the feature vectors in the previous frame tracking frame, and using the detection frame with the feature vectors of the current frame and the previous frame different as the redundant detection frame. The tracking detection matching frame, the redundant detection frame and the redundant detection frame are obtained by comparing the feature vectors of the upper frame and the lower frame, so that the accuracy of the matching result is ensured.
Wherein, the step of obtaining the tracking detection matching frame and the redundant tracking frame of the current frame comprises the following steps:
taking the detection frame with different feature vectors of the current frame and the previous frame as a redundant detection frame;
calculating whether the sum of the number of the tracking detection matching frames and the number of the redundant detection frames is consistent with the number of the current frame detection frames;
if the number of the tracking detection matching frames is consistent, the tracking detection matching frames and the redundant tracking frames are respectively used as the tracking detection matching frames and the redundant tracking frames of the current frame;
if the numbers are inconsistent, the feature vector comparison is repeatedly carried out to obtain the tracking detection matching frame, the redundant tracking frame and the redundant detection frame again for recalculation, and when the recalculated numbers are still inconsistent, an error report is sent to a designated person.
In this embodiment, the number of the tracking detection matching frames and the number of the redundant detection frames are added to be the number of the current frame detection frames. And adding q redundant detection frames and l tracking detection matching frames to be equal to m detection frames, and performing mathematical checking calculation. And if the numbers are equal, respectively using the tracking detection matching frame and the redundant tracking frame as the tracking detection matching frame and the redundant tracking frame of the current frame. And if the numbers are not equal, the obtained redundant detection frame q or the tracking detection matching frame l is considered to have errors, the feature vector comparison is carried out again to obtain the tracking detection matching frame, the redundant tracking frame and the redundant detection frame again for recalculation, and when the recalculated numbers are still inconsistent, an error report is sent to related personnel, so that the verification in the detection process is realized, and the error rate of the computer is reduced.
In other embodiments, when the numbers are not aligned, the feature vector comparison is not performed again, and an error report is directly sent to the relevant personnel.
S4: and sequentially judging the content in each redundant tracking frame through a pre-trained livestock classification model, and if the content judgment result in the redundant tracking frame is real livestock, taking the redundant tracking frame as a tracking and compensation missed detection frame.
In the embodiment, the content in the redundant tracking frames can be judged through a livestock classification model, and when the livestock is judged to be real livestock, the livestock can be used as the tracking and compensation missing detection frame, so that the missing detection livestock aiming at the single-frame image can be found.
The method comprises the following steps of sequentially judging the content in each redundant tracking frame through a pre-trained livestock classification model, and if the content judgment result in the redundant tracking frame is real livestock, taking the redundant tracking frame as a tracking and making up a missed detection frame, wherein the steps comprise:
acquiring the content in the redundant tracking frame of the acquired picture, wherein the content in the redundant tracking frame is an image of a single livestock judged by a model;
classifying the content in each redundant tracking frame in sequence through the livestock classification model;
and judging the redundant tracking frames with the classification probability larger than a preset threshold value as the positions of the real livestock, and acquiring tracking compensation missed detection frames.
In this embodiment, judge whether for real livestock through the classification model, predetermine a threshold value, when categorised probability surpasses this threshold value, then judge that the livestock in the current unnecessary tracking frame belongs to the livestock type of this application, then judge real livestock, prevent that other livestock that do not belong to the livestock type of this application from mixing into the livestock pen, and then influence the calculation of livestock quantity, reduce the many rates of computer output result.
Further, the step of judging the redundant tracking frames to be the positions of the real livestock by judging that the classification probability is greater than a preset threshold value comprises the following steps:
calculating classification probability through a classification probability formula, and judging the redundant tracking frames with the classification probability larger than a preset threshold value as the positions of the real livestock;
the classification probability is calculated by the formula
Wherein i is a category, e is a natural index, P is a probability, and Vi is a classification network output value of the livestock classification model corresponding to the category i.
In the embodiment, whether the livestock is real livestock or not is judged through calculation of the classification probability, so that the situation of false recognition is prevented, and the situation of multi-detection is prevented.
S5: and adding the obtained number of tracking compensation missed detection frames and the number of tracking detection matching frames to obtain the actual number of the livestock as a statistical result.
In this embodiment, the target detection AI model, the depth tracking model and the animal classification model are pre-trained based on a data set of animal types. The depth tracking model comprises a livestock re-identification model and a livestock tracking AI model. The livestock heavy identification model, the livestock tracking AI model and the livestock classification model are all universal heavy identification, tracking and classification models. The number of the tracking compensation missed-detection frames and the number of the tracking detection matching frames are added to serve as the actual number of the livestock, and the multi-detection rate and the missed-detection rate of the livestock detection by the computer are reduced.
S6: and comparing the statistical result with the livestock quantity stored in the database.
In this embodiment, the database pre-stores the livestock quantity due to the farm, and compares the statistical result with the pre-stored quantity to determine whether the livestock quantity of the farm changes.
S7: and if the statistical result is inconsistent with the livestock quantity stored in the database, judging that the livestock quantity is abnormal, and sending an abnormal alarm to the user.
In this embodiment, when livestock is lost, the user is reminded in time, and the video of the time point and the playback descending time period when the quantity of livestock is predicted to descend is found, so that the livestock farm can be helped to find the reason and stop loss in time.
Wherein after the step of comparing the statistical result with the number of livestock stored in the database, further comprising:
and if the statistical result is consistent with the livestock quantity stored in the database, judging that the livestock quantity is normal.
In this embodiment, if the number of animals is consistent, it is determined that the number of animals is not increased or decreased, and the farm is continuously monitored.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 3, as an implementation of the method shown in fig. 2 described above, the present application provides an embodiment of an apparatus for monitoring the number of animals, which corresponds to the embodiment of the method shown in fig. 2, and which is particularly applicable in various electronic devices.
As shown in fig. 3, the apparatus 300 for monitoring the number of animals according to the present embodiment includes: the system comprises an acquisition module 301, a detection module 302, an acquisition module 303, a judgment module 304, a calculation module 305, a comparison module 306 and an alarm module 307. Wherein:
the acquisition module 301 is used for controlling a camera mounted in a livestock pen to acquire a video, and intercepting the video according to frames to obtain an acquired picture;
the detection module 302 is configured to perform livestock detection on the acquired pictures according to a pre-trained target detection AI model to obtain at least one detection frame, where the content in the detection frame is an image of a single livestock determined by the model;
an obtaining module 303, configured to intercept images of a single animal in the detection frame, obtain an animal sample illustration, and compare all animal sample illustrations of a current frame with all animal sample illustrations of a previous frame through a pre-trained depth tracking model, so as to obtain a tracking detection matching frame and an excess tracking frame;
a judging module 304, configured to judge, through a pre-trained livestock classification model, the content in each redundant tracking frame in sequence, and if the content in the redundant tracking frame is judged to be a real livestock, take the redundant tracking frame as a tracking compensation missed-detection frame;
a calculating module 305, configured to add the obtained number of tracking compensation missed-detection frames and the number of tracking detection matching frames to obtain an actual number of livestock as a statistical result;
a comparison module 306 for comparing the statistical result with the livestock quantity stored in the database; and
and the warning module 307 is used for judging that the number of the livestock is abnormal when the statistical result is inconsistent with the number of the livestock stored in the database, and sending an abnormal alarm to the user.
In the embodiment, a depth tracking model and a livestock classification model are introduced on the basis of a target detection AI model, so that time information captured by a camera is fully utilized, and a detection frame identified by mistake is determined by comparing detected livestock images of a current frame and a previous frame, so that the multi-detection rate is reduced; the missing rate is reduced by judging the real livestock in the obtained redundant tracking frames, and the detection accuracy of the model is further improved.
The acquisition module 301 is further configured to control a camera installed in the livestock pen to perform video acquisition on the livestock pen at a frequency of 1 time/second and a duration of 1 second/time. The depth tracking model comprises a livestock re-identification model and a livestock tracking AI model, and the obtaining module 303 comprises a re-identification submodule and an obtaining submodule. The re-identification submodule is used for carrying out livestock re-identification on the livestock sample drawing through the livestock re-identification model to obtain the feature vectors with the same number as the detection frames. And the obtaining submodule is used for comparing all the feature vectors of the current frame with all the feature vectors of the previous frame through the motion matching degree and the apparent matching degree in the livestock tracking AI model to obtain a tracking detection matching frame and a redundant tracking frame of the current frame.
The acquisition submodule comprises an input unit and a comparison unit, the input unit is used for inputting the acquired picture into the livestock tracking AI model, and the livestock tracking AI model outputs a tracking frame; the comparison unit is used for comparing all the feature vectors of the current frame with all the feature vectors of the previous frame through the motion matching degree and the apparent matching degree in the livestock tracking AI model, taking the detection frame with the same feature vector of the current frame and the previous frame as a tracking detection matching frame, taking the tracking frame in the previous frame tracking frame and different feature vectors of the current frame detection frame as a redundant tracking frame, and obtaining the tracking detection matching frame and the redundant tracking frame of the current frame.
The comparison unit comprises a comparison subunit, a calculation subunit, an output subunit and a re-identification subunit, wherein the comparison subunit is used for taking the detection frames with different feature vectors of the current frame and the previous frame as redundant detection frames; the calculating subunit is used for calculating whether the sum of the number of the tracking detection matching frames and the number of the redundant detection frames is consistent with the number of the current frame detection frames; the output subunit is configured to, when the number of the tracking detection matching frames is consistent, take the tracking detection matching frame and the redundant tracking frame as the tracking detection matching frame and the redundant tracking frame of the current frame, respectively; and the re-identifying subunit is used for repeatedly carrying out feature vector comparison to re-acquire the tracking detection matching frame, the redundant tracking frame and the redundant detection frame for recalculation when the number is inconsistent, and sending an error report to a designated person when the recalculated number is still inconsistent.
The judging module 304 comprises an obtaining submodule, a classifying submodule and a threshold submodule, wherein the obtaining submodule is used for obtaining the content in the redundant tracking frame of the acquired picture, and the content in the redundant tracking frame is the image of the single livestock judged by the model; the classification submodule is used for sequentially classifying the content in each redundant tracking frame through the livestock classification model; and the threshold submodule is used for judging the redundant tracking frames with the classification probability larger than a preset threshold as the positions of the real livestock to obtain tracking compensation missed detection frames.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 200 comprises a memory 201, a processor 202, a network interface 203 communicatively connected to each other via a system bus. It is noted that only computer device 200 having components 201 and 203 is shown, but it is understood that not all of the illustrated components are required and that more or fewer components may alternatively be implemented. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 201 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 201 may be an internal storage unit of the computer device 200, such as a hard disk or a memory of the computer device 200. In other embodiments, the memory 201 may also be an external storage device of the computer device 200, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 200. Of course, the memory 201 may also include both internal and external storage devices of the computer device 200. In this embodiment, the memory 201 is generally used for storing an operating system installed in the computer device 200 and various types of application software, such as program codes of a method for monitoring the number of livestock, and the like. Further, the memory 201 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 202 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 202 is generally operative to control overall operation of the computer device 200. In this embodiment, the processor 202 is configured to run program code stored in the memory 201 or process data, for example, program code for running the method for monitoring the number of animals.
The network interface 203 may comprise a wireless network interface or a wired network interface, and the network interface 203 is generally used for establishing communication connection between the computer device 200 and other electronic devices.
In the embodiment, the time information captured by the camera is fully utilized, and the detection frame identified by mistake is determined by comparing the detected livestock images of the current frame and the previous frame, so that the multi-detection rate is reduced; the missing rate is reduced by judging the real livestock in the obtained redundant tracking frames, and the detection accuracy of the model is further improved.
The present application further provides another embodiment, namely a computer readable storage medium having stored thereon a program for monitoring a quantity of animals, the program being executable by at least one processor for causing the at least one processor to perform the steps of the method for monitoring a quantity of animals as described above. Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
In the embodiment, the time information captured by the camera is fully utilized, and the detection frame identified by mistake is determined by comparing the detected livestock images of the current frame and the previous frame, so that the multi-detection rate is reduced; the missing rate is reduced by judging the real livestock in the obtained redundant tracking frames, and the detection accuracy of the model is further improved.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A method of monitoring the quantity of animals, comprising the steps of:
controlling a camera installed in a livestock pen to collect videos, and intercepting the videos according to frames to obtain collected pictures;
carrying out livestock detection on the collected pictures according to a pre-trained target detection AI model to obtain at least one detection frame, wherein the content in the detection frame is an image of a single livestock judged by the model;
intercepting images of single livestock in the detection frame to obtain a livestock sample illustration, and comparing all the livestock sample illustrations of the current frame with all the livestock sample illustrations of the previous frame through a pre-trained depth tracking model to obtain a tracking detection matching frame and a redundant tracking frame;
sequentially judging the content in each redundant tracking frame through a pre-trained livestock classification model, and if the content in the redundant tracking frames is judged to be real livestock, taking the redundant tracking frames as tracking compensation missed detection frames;
adding the obtained number of tracking compensation missed detection frames and the number of tracking detection matching frames to obtain the actual number of the livestock as a statistical result;
comparing the statistical result with the livestock quantity stored in a database; and
and if the statistical result is inconsistent with the livestock quantity stored in the database, judging that the livestock quantity is abnormal, and sending an abnormal alarm to the user.
2. The method of monitoring livestock quantity according to claim 1, wherein said depth tracking models include a livestock weight recognition model and a livestock tracking AI model, and said step of obtaining tracking detection matching boxes and redundant tracking boxes by comparing all livestock sample instances of a current frame with all livestock sample instances of a previous frame through a pre-trained depth tracking model comprises:
carrying out livestock re-identification on the livestock sample drawing through the livestock re-identification model to obtain feature vectors with the number equal to that of the detection frames;
and comparing all the feature vectors of the current frame with all the feature vectors of the previous frame through the motion matching degree and the apparent matching degree in the livestock tracking AI model to obtain a tracking detection matching frame and a redundant tracking frame of the current frame.
3. The method of monitoring livestock numbers according to claim 2, wherein said step of obtaining tracking detection matching frame and redundant tracking frame of current frame by comparing all feature vectors of current frame with all feature vectors of previous frame through said livestock tracking motion matching degree and apparent matching degree in AI model comprises:
inputting the collected picture into the livestock tracking AI model, and outputting a tracking frame by the livestock tracking AI model;
comparing all the feature vectors of the current frame with all the feature vectors of the previous frame through the motion matching degree and the apparent matching degree in the livestock tracking AI model, and taking the detection frame with the same feature vectors of the current frame and the previous frame as a tracking detection matching frame;
and comparing all the feature vectors of the current frame with all the feature vectors in the tracking frame of the previous frame, and taking the tracking frame which is different from the feature vectors of the detection frame of the current frame in the tracking frame of the previous frame as a redundant tracking frame to obtain a tracking detection matching frame and the redundant tracking frame of the current frame.
4. The method of monitoring livestock quantity according to claim 3, characterized in that said step of obtaining tracking detection matching boxes and redundant tracking boxes of the current frame comprises:
taking the detection frame with different feature vectors of the current frame and the previous frame as a redundant detection frame;
calculating whether the sum of the number of the tracking detection matching frames and the number of the redundant detection frames is consistent with the number of the current frame detection frames;
if the number of the tracking detection matching frames is consistent, the tracking detection matching frames and the redundant tracking frames are respectively used as the tracking detection matching frames and the redundant tracking frames of the current frame;
if the numbers are inconsistent, the feature vector comparison is repeatedly carried out to obtain the tracking detection matching frame, the redundant tracking frame and the redundant detection frame again for recalculation, and when the recalculated numbers are still inconsistent, an error report is sent to a designated person.
5. The method of monitoring livestock quantity according to claim 1, wherein said step of determining the contents of each of said redundant tracking frames in turn by means of a pre-trained livestock classification model, and if the contents of said redundant tracking frames are determined to be true livestock, using said redundant tracking frames as tracking compensation missed detection frames comprises:
acquiring the content in the redundant tracking frame of the acquired picture, wherein the content in the redundant tracking frame is an image of a single livestock judged by a model;
classifying the content in each redundant tracking frame in sequence through the livestock classification model;
and judging the redundant tracking frames with the classification probability larger than a preset threshold value as the positions of the real livestock, and acquiring tracking compensation missed detection frames.
6. The method of monitoring livestock quantity according to claim 5, wherein said step of determining said redundant tracking frame as a location of a real livestock having a classification probability greater than a preset threshold comprises:
calculating classification probability through a classification probability formula, and judging the redundant tracking frames with the classification probability larger than a preset threshold value as the positions of the real livestock;
the classification probability is calculated by the formula
Wherein i is a category, e is a natural index, P is a probability, and Vi is a classification network output value of the livestock classification model corresponding to the category i.
7. The method of monitoring livestock according to claim 1, wherein said step of controlling video capture by cameras mounted to said livestock pen comprises:
and controlling a camera arranged in the livestock pen to carry out video acquisition on the livestock pen at the frequency of 1 time/second and the time length of 1 second/time.
8. An apparatus for monitoring the quantity of livestock, comprising:
the acquisition module is used for controlling a camera installed in the livestock pen to acquire videos and intercepting the videos according to frames to obtain acquired pictures;
the detection module is used for carrying out livestock detection on the collected pictures according to a pre-trained target detection AI model to obtain at least one detection frame, wherein the content in the detection frame is an image of a single livestock judged by the model;
the acquisition module is used for intercepting images of single livestock in the detection frame to obtain a livestock sample illustration, and comparing all the livestock sample illustrations of the current frame with all the livestock sample illustrations of the previous frame through a pre-trained depth tracking model to obtain a tracking detection matching frame and a redundant tracking frame;
the judging module is used for sequentially judging the content in each redundant tracking frame through a pre-trained livestock classification model, and if the content in the redundant tracking frames is judged to be real livestock, the redundant tracking frames are used as tracking compensation missed detection frames;
the calculation module is used for adding the obtained number of the tracking compensation missed detection frames and the number of the tracking detection matching frames to obtain the actual number of the livestock as a statistical result;
the comparison module is used for comparing the statistical result with the livestock quantity stored in the database; and
and the warning module is used for judging that the quantity of the livestock is abnormal and sending an abnormal alarm to the user if the statistical result is inconsistent with the quantity of the livestock stored in the database.
9. A computer arrangement, characterized by comprising a memory in which a computer program is stored and a processor which, when executing the computer program, carries out the steps of the method of monitoring the number of animals of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the method of monitoring the number of animals of any one of claims 1 to 7.
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| PCT/CN2020/105770 WO2021217934A1 (en) | 2020-04-28 | 2020-07-30 | Method and apparatus for monitoring number of livestock, and computer device and storage medium |
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| CN111680551B (en) | 2024-06-11 |
| WO2021217934A1 (en) | 2021-11-04 |
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