CN112734730A - Livestock quantity identification method, device, equipment and storage medium - Google Patents
Livestock quantity identification method, device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a method, a device, equipment and a storage medium for identifying the quantity of livestock, wherein the method comprises the steps of acquiring the condition that a livestock transport vehicle is stopped on a wagon balance, and taking a picture of the livestock; carrying out livestock quantity identification on the pictures obtained by photographing to obtain the quantity of first livestock; acquiring the condition that the livestock slides down from the slideway, and shooting a video of the livestock when the livestock slides down; identifying the number of livestock in the video to obtain the number of second livestock; when the number of the first livestock is equal to that of the second livestock, the final number of the livestock is determined, so that the accuracy of identification data can be improved, the labor cost is reduced, the biological safety risk is reduced, and the unmanned operation of a selling link is realized. The livestock number identification device, the equipment and the storage medium have the same advantages as the livestock number identification method.
Description
Technical Field
The invention belongs to the technical field of livestock breeding, and particularly relates to a method, a device, equipment and a storage medium for identifying the number of livestock.
Background
The livestock breeding industry is labor-intensive, in the prior art, when the quantity of livestock needs to be checked, one mode is a manual mode, but the labor and the time are wasted, the method depends on personal quality of operators, the quantity of the livestock is not easy to be checked correctly due to the influence of various factors, the other mode is to identify the quantity of the livestock depending on an automatic technology, two target detection methods adopted by the automatic technology are adopted, one method is to carry out edge detection through the traditional opencv to obtain a target contour, but the method is difficult to adapt to the requirement of target detection of a complex scene, the other method is a deep learning method, but mainly aims at photos of pedestrians, vehicles and the like, the accuracy is low, and the method is not suitable for being applied to the field of livestock quantity identification.
Disclosure of Invention
In order to solve the problems, the invention provides a method, a device, equipment and a storage medium for identifying the quantity of livestock, which can improve the accuracy of identification data, reduce labor cost, reduce biological safety risk and realize unmanned operation in a marketing link.
The invention provides a livestock quantity identification method, which comprises the following steps:
acquiring the condition that the livestock transport vehicle is stopped on a wagon balance, and taking a picture of the livestock;
carrying out livestock quantity identification on the pictures obtained by photographing to obtain the quantity of first livestock;
acquiring the condition that the livestock slides down from the slideway, and shooting a video of the livestock when the livestock slides down;
identifying the number of livestock in the video to obtain the number of second livestock;
and when the number of the first livestock is equal to that of the second livestock, determining the final number of the livestock.
Preferably, in the method for identifying the number of livestock, the identifying the number of livestock on the photo obtained by photographing to obtain the first number of livestock includes:
and carrying out livestock quantity identification on the shot pictures by utilizing a trained mrcnn model.
Preferably, in the method for identifying the number of livestock, the identifying the number of livestock in the video to obtain a second number of livestock includes:
and identifying the number of livestock in the video by using the trained mrcnn model and the tracking model.
Preferably, in the above livestock number identification method, after the shooting the livestock, the method further comprises:
labeling the obtained picture to be used as a sample of an mrcnn model for deep learning;
expanding the samples by data enhancement to obtain a sample set for model training;
adjusting the pictures in the sample set to the same size and obtaining real BBox position information of the pictures, obtaining predicted BBox information through an mrcnn model, comparing the real BBox position information with the predicted BBox information, taking the sum of the classifications loss, confidence loss, location loss and iou loss as final loss, and updating the weight by using a back propagation algorithm until the mrcnn model converges or meets an iteration termination condition, so as to obtain the trained mrcnn model.
Preferably, in the above method for identifying the number of livestock, the augmenting the sample with data enhancement is:
and expanding the sample by means of mixup, turning, translation, random cutting and random noise addition.
Preferably, in the method for identifying the number of livestock, the livestock is photographed by a visible light camera or an infrared camera, and the video of the livestock sliding down is photographed by the visible light camera or the infrared camera.
The invention provides a livestock quantity recognition device, which comprises:
the shooting component is used for acquiring the condition that the livestock transport vehicle is stopped on the wagon balance and shooting the livestock;
the first livestock number identification component is used for identifying the quantity of the livestock of the photos obtained by photographing to obtain the number of the first livestock;
the video shooting component is used for acquiring the situation that the livestock slides down from the slideway and shooting the video of the livestock when the livestock slides down;
the second livestock number identification component is used for identifying the quantity of the livestock in the video to obtain the quantity of the second livestock;
animal number determination means for determining a final animal number when said first animal number and said second animal number are equal.
Preferably, in the above livestock number recognition apparatus, the first livestock number recognition component is specifically configured to perform livestock number recognition on the photographed picture by using a trained mrcnn model.
The invention provides a computer device comprising:
a memory for storing a computer program;
a processor for carrying out the steps of the method for animal number identification according to any of the above when executing said computer program.
The present invention provides a computer readable storage medium having stored thereon a computer program for, when executed by a processor, performing the steps of the method for animal quantity identification according to any one of the above.
According to the above description, the method for identifying the number of the livestock provided by the invention comprises the steps of firstly acquiring the condition that the livestock transport vehicle is stopped on the wagon balance, and taking a picture of the livestock; carrying out livestock quantity identification on the pictures obtained by photographing to obtain the quantity of first livestock; then acquiring the situation that the livestock slides down from the slideway, and shooting a video of the livestock when the livestock slides down; then identifying the number of livestock in the video to obtain the number of second livestock; and finally, when the number of the first livestock and the number of the second livestock are equal, the number of the final livestock is determined, the number is calculated in two ways, so that the calculation of the number of the livestock is more accurate, the accuracy of identification data is improved, personnel are not needed to participate in the whole process, the labor cost can be reduced, the biological safety risk is reduced, and the unmanned operation of a selling link is realized. The livestock quantity recognition device, the equipment and the storage medium provided by the invention have the same advantages as the method.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic view of an embodiment of a method for identifying the number of livestock provided by the invention;
fig. 2 is a schematic view of an embodiment of an apparatus for recognizing the amount of livestock according to the present invention;
fig. 3 is a schematic diagram of an embodiment of a computer device provided in the present invention.
Detailed Description
The core of the invention is to provide a method, a device, equipment and a storage medium for identifying the quantity of livestock, which can improve the accuracy of identification data, reduce labor cost, reduce biological safety risk and realize unmanned operation in a marketing link.
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.
Fig. 1 shows an embodiment of a method for identifying a quantity of livestock according to the present invention, where fig. 1 is a schematic diagram of an embodiment of a method for identifying a quantity of livestock according to the present invention, and the method may include the following steps:
s1: acquiring the condition that the livestock transport vehicle is stopped on a wagon balance, and taking a picture of the livestock;
it should be noted that can install the camera above the weighbridge, after the livestock transport vechicle stops to stop steadily on the weighbridge, just can trigger PLC, this PLC can issue the instruction, triggers camera SDK and shoots the livestock to upload the photo of shooing to the server.
S2: carrying out livestock quantity identification on the pictures obtained by photographing to obtain the quantity of first livestock;
specifically, the number of animals can be identified by using a trained mrcnn model, so that the number of the first animals is obtained from the pictures and is temporarily stored.
S3: acquiring the condition that the livestock slides down from the slideway, and shooting a video of the livestock when the livestock slides down;
it should be noted that, selling occasions such as livestock, need catch up the livestock with the slide, the camera can be installed to this slide top, arrives this slide after the livestock transport vechicle leaves the weighbridge, and the staff drives the livestock, catches up the slide with it, and the video acquisition switch obtains triggering this moment, and drive camera SDK records the video to upload to the server the video of will recording.
S4: identifying the number of livestock in the video to obtain the number of second livestock;
specifically, the number of animals in the video can be identified by, but not limited to, a trained mrcnn model and a tracking model, and the tracking model can be preferably a deepsort tracking model, so that the number of second animals can be obtained through the video and temporarily stored.
S5: when the first number of animals and the second number of animals are equal, the final number of animals is determined.
That is to say, utilize two kinds of modes to obtain the livestock number through above step, when the two are equal, then can regard as the number calculation correct, regard this number as final livestock number, it is thus obvious that this just need not artifical individual count, the work load that has significantly reduced moreover, count more accurately, in addition, when these two livestock numbers are unequal, then send picture and video to the staff and check.
As can be seen from the above description, in the embodiment of the method for identifying the number of livestock provided by the invention, the condition that the livestock transport vehicle is stopped on the wagon balance is obtained first, and the livestock is photographed; carrying out livestock quantity identification on the pictures obtained by photographing to obtain the quantity of first livestock; then acquiring the situation that the livestock slides down from the slideway, and shooting a video of the livestock when the livestock slides down; then identifying the number of livestock in the video to obtain the number of second livestock; at last when first livestock number and second livestock number are equal, then determine final livestock quantity, it is visible to adopt two kinds of modes to calculate quantity, consequently can guarantee that livestock quantity calculates more accurately, promotes the accuracy of identification data, and whole journey need not personnel to participate in moreover, consequently can reduce the cost of labor, reduces biological safety risk, realizes selling the unmanned operation of link.
In an embodiment of the above method for identifying the number of livestock, after the livestock is photographed, the method further comprises:
labeling the obtained picture to be used as a sample of an mrcnn model for deep learning;
expanding the sample by using data enhancement to obtain a sample set for model training;
adjusting the pictures in the sample set to the same size and acquiring real BBox position information of the pictures, acquiring predicted BBox information through an mrcnn model, comparing the real BBox position information with the predicted BBox information, taking the sum of the classifications loss, confidence loss, location loss and iou loss as final loss, and updating the weight by using a back propagation algorithm until the mrcnn model converges or meets an iteration termination condition to obtain a trained mrcnn model.
It should be noted that, in this embodiment, a specific scheme of model training is described, because the mrcnn model based on deep learning needs a certain amount of diversified samples, the animal contour is manually marked on the visible light image, and the mrcnn model can be used as knowledge for learning the deep neural network model, after the mrcnn model is trained, a picture is provided as an input of the model, and after the mrcnn model is processed, the animal number in the picture can be obtained, and of course, the model only needs to be trained in advance, and is not trained before each recognition. In the above embodiment, the expanding the sample by using data enhancement may specifically be: the samples are expanded by means of mixup, turning, translation, random cutting and random noise addition, so that more samples can be provided, and the model can be trained more comprehensively.
In another embodiment of the above method for identifying the number of livestock, the livestock is photographed by a visible light camera or an infrared camera, and the video of the livestock sliding down is photographed by the visible light camera or the infrared camera. It should be noted that, when carrying out livestock quantity discernment night, sometimes the shadow appears and can lead to discerning inaccurate, so can adopt the single channel picture of infrared camera shooting to discern night to retrain the model again, the detection effect of model can further be improved.
In summary, the following scheme can be adopted for the livestock quantity identification method provided by the invention, which is described by taking pigs as an example, but the method can also be applied to other livestock quantity identification such as cattle or sheep, and the method is not limited herein. When the pig transporting vehicle reaches the wagon balance, a shooting instruction is triggered, a picture is shot for the pig transporting vehicle, the picture is uploaded to the cloud server, the related algorithm is called to calculate the number of the heads of the pigs, then when the pigs slide down the slide way, a video is shot, the video is uploaded to the cloud server, the related algorithm is called to calculate the number of the heads of the pigs, then the number of the heads calculated in the two modes is checked, when the number of the heads is equal to the number of the heads calculated in the two modes, the number of the heads is recorded as the final correct number of the heads, and if the number of.
Fig. 2 shows an embodiment of a device for identifying the number of animals according to the present invention, and fig. 2 is a schematic view of an embodiment of a device for identifying the number of animals according to the present invention, the device comprising:
the shooting component 201 is used for acquiring the condition that the livestock transport vehicle stops on the wagon balance and shooting the livestock, and it needs to be explained that a camera can be installed above the wagon balance, when the livestock transport vehicle stops on the wagon balance and stops stably, a PLC can be triggered, the PLC can issue an instruction, a camera SDK is triggered to shoot the livestock, and the shot picture is uploaded to a server;
the first livestock number recognition component 202 is used for recognizing the quantity of the livestock of the shot photos to obtain the number of the first livestock, specifically, but not limited to, the shot photos are recognized by utilizing a trained mrcnn model, so that the number of the first livestock is obtained through the photos and is temporarily stored;
the video shooting part 203 is used for acquiring the situation that the livestock slides down the slideway and shooting the video of the livestock when the livestock slides down, and it needs to be explained that in the occasions of selling the livestock and the like, the livestock needs to be driven down the slideway, a camera can be arranged above the slideway, when the livestock transport vehicle leaves a wagon and arrives at the slideway, a worker drives the livestock and drives the livestock down the slideway, at the moment, a video acquisition switch is triggered to drive the camera SDK to record the video, and the recorded video is uploaded to the server;
a second livestock number identification component 204, configured to identify the number of livestock in the video to obtain a second livestock number, specifically, but not limited to, identify the number of livestock in the video by using a trained mrcnn model and a tracking model, which may be preferably a deepsort tracking model, so that the second livestock number can be obtained through the video and temporarily stored;
livestock quantity determining part 205, is used for when first livestock number and second livestock number equals, then determine final livestock quantity, that is to say, utilize two kinds of modes to obtain the livestock number, when the two are equal, then can regard as the number calculation correct, regard this number as final livestock quantity, it is thus obvious that this just need not artifical individual count, the work load that has significantly reduced, and the count is more accurate, in addition, when these two livestock numbers are unequal, then send picture and video to the staff and check.
It can be seen that the device adopts two kinds of modes to calculate quantity, consequently can guarantee that livestock quantity calculates more accurately, promotes the accuracy of identification data, and whole journey need not personnel and participates in moreover, consequently can reduce the cost of labor, reduces biological safety risk, realizes the unmanned operation of selling the link.
In another embodiment of the above livestock number identification device, the photographing part may be a visible light camera or an infrared camera, and the video photographing part may be a visible light camera or an infrared camera. It should be noted that, when carrying out livestock quantity discernment night, sometimes the shadow appears and can lead to discerning inaccurate, so can adopt the single channel picture of infrared camera shooting to discern night to retrain the model again, the detection effect of model can further be improved.
Fig. 3 shows an embodiment of a computer device provided by the present invention, and fig. 3 is a schematic diagram of an embodiment of a computer device provided by the present invention, where the computer device includes:
a memory 301 for storing a computer program;
a processor 302 for implementing the steps of the method for animal quantity identification as described in any of the above when executing a computer program.
In an embodiment of the computer readable storage medium according to the invention, 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 for identifying the quantity of livestock according to any of the above.
To sum up, above-mentioned scheme is through once quantity of discernment above the weighbridge and once quantity of discernment above the slide, checks through two quantities and confirms correct quantity, lets the sales link unmanned, promotes the quantity accuracy, reduces the cost of artifical point.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A livestock quantity identification method is characterized by comprising the following steps:
acquiring the condition that the livestock transport vehicle is stopped on a wagon balance, and taking a picture of the livestock;
carrying out livestock quantity identification on the pictures obtained by photographing to obtain the quantity of first livestock;
acquiring the condition that the livestock slides down from the slideway, and shooting a video of the livestock when the livestock slides down;
identifying the number of livestock in the video to obtain the number of second livestock;
and when the number of the first livestock is equal to that of the second livestock, determining the final number of the livestock.
2. The livestock quantity recognition method of claim 1, wherein said performing livestock quantity recognition on the photographed picture to obtain a first livestock quantity comprises:
and carrying out livestock quantity identification on the shot pictures by utilizing a trained mrcnn model.
3. The animal quantity identification method of claim 1, wherein said identifying the quantity of animals in said video and deriving a second animal quantity comprises:
and identifying the number of livestock in the video by using the trained mrcnn model and the tracking model.
4. The livestock quantity identification method of claim 2 or 3, characterized in that after said photographing livestock, further comprising:
labeling the obtained picture to be used as a sample of an mrcnn model for deep learning;
expanding the samples by data enhancement to obtain a sample set for model training;
adjusting the pictures in the sample set to the same size and obtaining real BBox position information of the pictures, obtaining predicted BBox information through an mrcnn model, comparing the real BBox position information with the predicted BBox information, taking the sum of the classifications loss, confidence loss, location loss and iou loss as final loss, and updating the weight by using a back propagation algorithm until the mrcnn model converges or meets an iteration termination condition, so as to obtain the trained mrcnn model.
5. The livestock quantity identification method of claim 4, wherein said augmenting said sample with data enhancement is:
and expanding the sample by means of mixup, turning, translation, random cutting and random noise addition.
6. The livestock number identification method of any of claims 1-5, characterized in that the livestock is photographed by a visible light camera or an infrared camera, and the video of the livestock when sliding down is photographed by the visible light camera or the infrared camera.
7. An apparatus for recognizing the number of livestock, comprising:
the shooting component is used for acquiring the condition that the livestock transport vehicle is stopped on the wagon balance and shooting the livestock;
the first livestock number identification component is used for identifying the quantity of the livestock of the photos obtained by photographing to obtain the number of the first livestock;
the video shooting component is used for acquiring the situation that the livestock slides down from the slideway and shooting the video of the livestock when the livestock slides down;
the second livestock number identification component is used for identifying the quantity of the livestock in the video to obtain the quantity of the second livestock;
animal number determination means for determining a final animal number when said first animal number and said second animal number are equal.
8. The animal number recognition arrangement according to claim 7, characterized in that said first animal number recognition means is specifically adapted for animal number recognition of a photographed picture using a trained mrcnn model.
9. A computer device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the animal quantity identification method according to any one of claims 1 to 6 when executing said computer program.
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 for livestock quantity identification according to any one of claims 1 to 6.
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| CN113283322A (en) * | 2021-05-14 | 2021-08-20 | 柳城牧原农牧有限公司 | Livestock trauma detection method, device, equipment and storage medium |
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