Livestock supervision system based on visual images

Abstract

The invention discloses a livestock supervision system based on visual images, and belongs to the technical field of livestock supervision systems. Comprising the steps of collecting livestock behavior triggering state data, collecting livestock action, water spray and other data, marking the position of livestock, the splashing direction of water spray and the position of an object according to the livestock behavior triggering state data, and conducting data cleaning on the livestock behavior triggering state data. The livestock behavior triggering state model deduces the image acquired from the camera in real time, judges the position, direction or force of the livestock, the spray or the object to protect the monitoring head, and through the setting of the livestock behavior triggering state model, in the process of supervising and monitoring the livestock, the livestock behavior triggering state can be monitored in real time. The behavior of the livestock can be acquired, or when the monitoring head for monitoring the lower side is possibly damaged or contacted by an object or a water drop, position changing or protection can be carried out to prevent the livestock from damaging the monitoring head, and the service life is prolonged.

Description

Technical Field

[0001] The present application relates to the technical field of livestock supervision systems, and more specifically, to a livestock supervision system based on visual images. Background Art

[0002] With the development of science and technology, many industries will be managed through intelligent monitoring. In animal husbandry, the growth status of livestock can also be monitored through cameras. However, during the monitoring process, the cameras are only placed at high places with limited range. If they are placed too low, livestock can easily cause damage.

[0003] The document with the prior art publication number CN115088636B provides an intelligent monitoring device for animal husbandry based on image processing. The device is provided through a breeding shed. Two rows of breeding troughs are arranged on the ground inside the breeding shed along the length direction. A circular square tube is fixedly installed on the top surface of the breeding shed through several first brackets. The square tube is located above all the breeding troughs. A water supply connection device is provided on one side of the breeding shed, and the water supply connection device is communicated with the inside of the square tube; several water outlet valve structures are arranged along the length direction of the square tube at the bottom of the square tube, and a sensing device of a sensing frame is arranged corresponding to each water outlet valve structure at the bottom of the square tube. A C-shaped frame with an opening facing upward is sleeved on the outer periphery of the square tube, and the movable end of the elastic telescopic rod is installed on the roller that rolls on the top surface of the square tube. The frame is also provided with a driving mechanism that drives it to move along the square tube and can push the frame to A mobile pushing structure that moves downward, a water spray camera structure with an adjustable water outlet state perpendicular to the square tube is provided under the frame, the frame is provided with a peristaltic transmission structure that is communicated with the water spray camera structure, and a power drive component that simultaneously drives the mobile pushing structure to roll and the peristaltic transmission structure to work is provided at the bottom of the frame. When in use, the water spray camera structure can take real-time images of the lower part of the breeding shed and provide feedback, the sensing device feeds back information to the computer terminal, the computer terminal causes the power drive component to rotate in the opposite direction to cause the peristaltic transmission structure to work and cause the mobile pushing structure to release the pushing of the frame, the mobile pushing structure no longer moves along the square tube, and at the same time the peristaltic transmission structure opens the water outlet valve structure at the corresponding position, so that the water in the square tube passes through the peristaltic transmission structure from the square tube into the water spray camera structure and is sprayed downward.

[0004] Although the above-mentioned prior art solution can achieve the relevant beneficial effects through the structure of the prior art, it still has the following defects: the device is equipped with a monitoring head directly above for monitoring. During use, the monitoring angle is limited, so the diversity of the data samples obtained is limited, which affects the accurate judgment of the growth or behavior status of livestock, resulting in the inability to detect abnormalities in time.

[0005] With respect to the above-mentioned related technologies, the inventors believe that in the process of livestock supervision and monitoring, it is necessary to monitor the conditions of livestock from multiple angles to avoid situations where damage or abnormalities cannot be discovered and handled in a timely manner, thereby improving the effectiveness of supervision and ensuring the healthy growth of livestock.

[0006] In view of this, we propose a livestock supervision system based on visual images. Summary of the invention

[0007] 1. Technical problems to be solved

[0008] The purpose of the present application is to provide a livestock monitoring system based on visual images, which solves the problem that the device in the above-mentioned background technology is equipped with a monitoring head directly above for monitoring. During use, the monitoring angle is effective, so that the diversity of the data samples obtained is effective, which affects the accurate judgment of the growth or behavioral status of livestock, resulting in the technical problem that abnormalities cannot be discovered in time, thereby achieving a technical effect.

[0009] 2. Technical solution

[0010] The technical solution of this application provides a livestock supervision system based on visual images, including:

[0011] Collect livestock behavior-induced status data, collect livestock movement, water splash and other data;

[0012] The livestock behavior-induced state data is labeled, and the livestock position, water splash direction, and object position of the livestock behavior-induced state data are labeled;

[0013] Preprocessing of livestock behavior-induced state data, including data cleaning and establishment of data pipelines;

[0014] Establishing livestock behavior induced state model training, using livestock behavior induced state data to establish livestock behavior induced state model and conduct training;

[0015] Optimization and verification of livestock behavior-induced state model: verify the livestock behavior-induced state model and optimize it according to the verification results;

[0016] Livestock behavior-induced state model deployment, embedding the trained livestock behavior-induced state model into the system to monitor the external environment in real time;

[0017] Execute the device. When the device is working, the livestock behavior-induced state model will infer the image obtained from the camera in real time, determine the position, direction or strength of the livestock, water splashes or objects, and protect the monitoring head.

[0018] By adopting the above technical solution and setting up a livestock behavior-induced state model, in the process of livestock supervision and monitoring, the livestock behavior can be obtained or when objects or water droplets may cause damage or contact to the monitoring head monitoring the lower side, the position can be changed or protection can be taken to prevent livestock from damaging it, thereby extending the service life.

[0019] As an optional solution of the technical solution of the present application document, the state data triggered by livestock behavior is collected by using a monitoring head, including different actions of livestock, or behaviors of each other, or image data of splashes or objects caused by behaviors, including situations in various environments and angles.

[0020] As an optional solution of the technical solution of the present application document, the livestock behavior triggers the state data annotation, using Bounding Box annotation, adapted to the task of detecting the position of the object, including drawing a rectangular box in the image to frame the target object in the image, and providing the coordinate information of the rectangular box;

[0021] Label each pixel in the image to indicate which object or category it belongs to, by drawing the outline or boundary of each object;

[0022] Using keypoint annotation, we can capture the key parts of an object, including marking the key points of the object in the image, such as animal joints.

[0023] As an optional solution of the technical solution of the present application document, the livestock behavior triggers state data preprocessing, loading the collected image data into a computer, checking and cleaning the data, including processing any missing or damaged images, ensuring the integrity of the data, including deleting incomplete or unusable images;

[0024] Divide the number of livestock behavior-induced states into small batches for batch gradient descent when training the livestock behavior-induced state model. Design a data pipeline to ensure that data can be efficiently loaded and processed during training. Save the preprocessed data as input for livestock behavior-induced state model training.

[0025] As an optional solution of the technical solution of the present application document, the livestock behavior-induced state model training is constructed using convolutional neural networks, which perform well in processing image data. During the training process, the livestock behavior-induced state model will learn to recognize and understand livestock, water splashes or objects in different environments.

[0026] As an optional solution to the technical solution of the present application document, the livestock behavior induced state model optimization verification selects different optimization algorithms, stochastic gradient descent, and in each iteration, the error of the current model is measured by calculating the gradient of the loss function with respect to the livestock behavior induced state model parameters. SGD uses only one random sample to calculate the gradient in each iteration, and uses the calculated gradient to update the parameters of the livestock behavior induced state model. The above steps are repeated until a predetermined number of iterations is reached or the stopping condition is met.

[0027] As an optional solution of the technical solution of this application document, the execution device includes:

[0028] livestock sheds;

[0029] A monitoring head is arranged inside the livestock shed through a protective component, and monitors the lower side through a moving component.

[0030] 3. Beneficial effects

[0031] One or more technical solutions provided in the technical solution of this application have at least the following technical effects or advantages:

[0032] 1. This application sets up a livestock behavior-induced state model. During the supervision and monitoring of livestock, the livestock behavior can be obtained or the monitoring head monitoring the lower side can be changed or protected when objects or water droplets may cause damage or contact to prevent livestock from damaging it, thereby extending the service life.

[0033] 2. Through the setting of the protection component, the present application can form an elliptical protection cover through the protection cover, so that the monitoring head is protected internally. At the same time, through the setting of this shape, the strength of the protection can be increased, avoiding the situation where it is easily damaged by force, thereby improving the protection effect.

[0034] 3. This application can monitor the condition of livestock from multiple angles by setting up mobile components, so that the monitoring can be moved from top to bottom, avoiding the situation where damage or abnormalities cannot be discovered and handled in time, thereby improving the effect of supervision and ensuring the healthy growth of livestock. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a schematic diagram of the structure of a livestock supervision system based on visual images disclosed in a preferred embodiment of the present application;

[0036] Figure 2 This is a schematic diagram of the overall structure of a livestock supervision system based on visual images disclosed in a preferred embodiment of the present application;

[0037] Figure 3This is a schematic diagram of the structure of a mobile component of a livestock supervision system based on visual images disclosed in a preferred embodiment of the present application;

[0038] Figure 4 This is an enlarged view of the structure of location A of a livestock supervision system based on visual images disclosed in a preferred embodiment of the present application;

[0039] Figure 5 An exploded diagram of the protective component structure of a livestock monitoring system based on visual images disclosed in a preferred embodiment of the present application;

[0040] Figure 6 A schematic diagram of the structure of a protection component of a livestock monitoring system based on visual images disclosed in a preferred embodiment of the present application;

[0041] Figure 7 A schematic diagram of the screw structure of a livestock monitoring system based on visual images disclosed in a preferred embodiment of the present application;

[0042] Explanation of the numbers in the figure: 1. Animal shed; 2. Monitoring head; 3. Protection assembly; 31. Protection cover; 32. Fixed block; 33. Connecting rod; 34. Fixed plate; 35. Movable plate; 36. Pull rod; 37. Hinge seat; 38. Driving rod; 39. Limit block; 310. Fixed seat; 311. Drive box; 312. Bevel gear A; 313. Bevel gear B; 314. Screw; 315. Threaded hole; 316. First motor; 317. Sliding block; 318. Slide groove; 4. Moving assembly; 41. Moving track; 42. Connecting frame; 43. Roller; 44. Second motor. DETAILED DESCRIPTION

[0043] The present application is further described in detail below in conjunction with the accompanying drawings.

[0044] Reference Figure 1 , the embodiment of the present application provides a livestock supervision system based on visual images, including:

[0045] Collect livestock behavior-induced status data, collect livestock movement, water splash and other data;

[0046] The livestock behavior-induced state data is labeled, and the livestock position, water splash direction, and object position of the livestock behavior-induced state data are labeled;

[0047] Preprocessing of livestock behavior-induced state data, including data cleaning and establishment of data pipelines;

[0048] Establishing livestock behavior induced state model training, using livestock behavior induced state data to establish livestock behavior induced state model and conduct training;

[0049] Optimization and verification of livestock behavior-induced state model: verify the livestock behavior-induced state model and optimize it according to the verification results;

[0050] Livestock behavior-induced state model deployment, embedding the trained livestock behavior-induced state model into the system to monitor the external environment in real time;

[0051] Execute the device. When the device is working, the livestock behavior-induced state model will infer the image obtained from the camera in real time, determine the position, direction or strength of the livestock, water splashes or objects, and protect the monitoring head 2.

[0052] Through the setting of the livestock behavior-induced state model, in the process of livestock supervision and monitoring, the livestock behavior can be obtained or when objects or water droplets may cause damage or contact to the monitoring head monitoring the lower side, the position can be changed or protection can be taken to prevent livestock from damaging it, thereby extending the service life.

[0053] Reference Figure 1 and Figure 2 The embodiment of the present application provides a livestock monitoring system based on visual images, which collects status data caused by livestock behavior, and uses a monitoring head 2 to collect data, including different actions of livestock, or behaviors of each other, or image data of splashes or objects caused by behaviors, including situations in various environments and angles.

[0054] The livestock behavior-induced state data is labeled using Bounding Box annotation, which is adapted to the task of detecting the position of an object. It includes drawing a rectangular box in the image to enclose the target object in the image, and providing the coordinate information of the rectangular box, usually the coordinates of the upper left corner and the lower right corner;

[0055] Label each pixel in the image to indicate which object or category it belongs to, by drawing the outline or boundary of each object;

[0056] Using keypoint annotation, we can capture the key parts of an object, including marking the key points of the object in the image, such as animal joints.

[0057] Livestock behavior-induced state data preprocessing, loading the collected image data into a computer, checking and cleaning the data, including processing any missing or damaged images, ensuring data integrity, including deleting incomplete or unusable images;

[0058] Divide the number of livestock behavior-induced states into small batches for batch gradient descent when training the livestock behavior-induced state model. Design a data pipeline to ensure that data can be efficiently loaded and processed during training. Save the preprocessed data as input for livestock behavior-induced state model training.

[0059] The livestock behavior-induced state model training is constructed using convolutional neural networks (CNNs), which perform well in processing image data. During the training process, the livestock behavior-induced state model will learn to recognize and understand livestock, water splashes or objects in different environments.

[0060] Optimization and verification of the livestock behavior-induced state model. A different optimization algorithm is selected, stochastic gradient descent (SGD). In each iteration, the error of the current model is measured by calculating the gradient of the loss function with respect to the parameters of the livestock behavior-induced state model. SGD uses only one random sample or a small batch of samples to calculate the gradient in each iteration. The calculated gradient is used to update the parameters of the livestock behavior-induced state model. The above steps are repeated until the predetermined number of iterations is reached or the stopping condition is met.

[0061] Execution equipment, including:

[0062] Livestock shed 1;

[0063] The monitoring head 2 is arranged inside the livestock shed 1 through the protective component 3, and monitors the lower side through the moving component 4.

[0064] Reference Figure 2 and Figure 3 The embodiment of the present application provides a livestock supervision system based on visual images, comprising:

[0065] Livestock shed 1;

[0066] The monitoring head 2 is arranged inside the livestock shed 1 through the protective component 3, and monitors the lower side through the moving component 4.

[0067] Reference Figure 5 , Figure 6 and Figure 7 , the embodiment of the present application provides a livestock supervision system based on visual images, the protection component 3 includes a protection cover 31, a plurality of protection covers 31 are provided, and the protection covers 31 are arranged in an arc-shaped structure, and the plurality of protection covers 31 are arranged in an elliptical structure;

[0068] The protection assembly 3 also includes a fixed block 32, a connecting rod 33 is fixedly provided on one side of the fixed block 32, a fixed plate 34 is fixedly provided on the outer end of the connecting rod 33, a movable plate 35 is provided on the outer side of the fixed plate 34, a pulling rod 36 is provided on the outer side of the movable plate 35, and hinge seats 37 are provided at both ends of the pulling rod 36, the outer hinge seat 37 is fixedly provided on the outer wall of the movable plate 35, and the inner hinge seat 37 is rotatably provided with a driving rod 38, and the driving rod 38 is fixedly provided on the inner wall of the protective cover 31;

[0069] The driving rod 38 is arranged in a V-shaped structure, and a limit block 39 is fixedly arranged in the middle of the driving rod 38, and the limit block 39 is rotatably connected to a fixing seat 310 fixedly arranged on the outer wall of the fixing plate 34;

[0070] A driving box 311 is fixedly arranged on the outside of the fixed plate 34, a bevel gear A312 is rotatably arranged inside the driving box 311, a bevel gear B313 is meshedly connected to the outer wall of the bevel gear A312, a screw rod 314 is slidably arranged in the middle of the bevel gear B313, the screw rod 314 is threadedly connected to a threaded hole 315 in the middle of the fixed plate 34, the screw rod 314 passes through the threaded hole 315 and extends to the outside and is rotatably connected to the outer wall of the movable plate 35;

[0071] A first motor 316 is coaxially fixedly disposed on one side of the bevel gear A312;

[0072] A sliding block 317 is fixedly arranged in the middle of the bevel gear B313 , and the sliding block 317 is slidably connected to a sliding groove 318 on the outer wall of the screw rod 314 .

[0073] The rotation of the output shaft of the first motor 316 drives the bevel gear A312 to rotate, and the bevel gear A312 is meshed with the bevel gear B313, so that the rotation of the bevel gear B313 drives the screw rod 314 to rotate, and the screw rod 314 is threadedly connected to the threaded hole 315 in the middle of the fixed plate 34, thereby driving the movable plate 35 to move up and down, and at the same time cooperates with the slide groove 318 to slide on the sliding block 317 fixed in the middle of the bevel gear B313, so that the movement of the movable plate 35 drives the pulling rod 36 to pull the driving rod 38 to move, thereby driving the protective cover 31 to expand or close, and the screw rod 314 can be used for effective limiting. Through the setting of the protective component, an elliptical protective cover can be formed through the protective cover, so that the monitoring head can be protected inside. At the same time, through the setting of this shape, the strength of the protection can be increased to avoid the situation where it is easily damaged by force, thereby improving the protection effect.

[0074] Reference Figure 3 and Figure 4 , the embodiment of the present application provides a livestock supervision system based on visual images, the mobile component 4 includes a mobile track 41, and the mobile track 41 is arranged in an L-shaped structure;

[0075] The cross section of the moving track 41 is arranged in a trapezoidal structure, and a connecting frame 42 is fixedly arranged on the outer wall of the fixed block 32. The connecting frame 42 is arranged in a C-shaped structure and is slidably arranged on the outer side of the moving track 41;

[0076] The inner wall of the connecting frame 42 is provided with rollers 43 for rotation, and the rollers 43 are connected with the outer wall of the moving track 41 in a rolling manner;

[0077] One of the rollers 43 is coaxially connected to a second motor 44 fixedly disposed on the outside of the connecting frame 42 .

[0078] The output shaft of the second motor 44 rotates to drive the roller 43 to rotate, and the upper and lower rollers 43 are limited on the movable track 41, so that the connecting frame 42 can move along the movable track 41 and the monitoring head 2 can move. By setting the mobile component, the situation of livestock can be monitored from multiple angles, and the monitoring can be moved from top to bottom to avoid damage or abnormality that cannot be discovered and handled in time, thereby improving the supervision effect and ensuring the healthy growth of livestock.

[0079] When the animal husbandry supervision system based on visual images is needed, first, collect data such as livestock movements and water splashes, and use the monitoring head 2 to collect data, including different livestock movements, or mutual behaviors, or image data of water splashes or objects caused by behaviors, including various environments and angles, and mark the position of livestock, the splashing direction of water splashes, and the position of objects in the state data triggered by livestock behavior; use Bounding Box annotation to adapt to the task of detecting the position of objects, including drawing a rectangular box in the image to frame the target object in the image, and providing coordinate information of the rectangular box, usually the coordinates of the upper left corner and the lower right corner; annotate each pixel in the image to indicate which object or category it belongs to, which is completed by drawing the outline or boundary of each object; use key point annotation to capture the task of key parts of the object, including marking the key points of the object in the image, such as animal joints, etc., and perform data cleaning, establish data pipelines and other preprocessing on the state data triggered by livestock behavior; load the collected image data into the computer, check and clean the data, including processing any missing or damaged images, ensure the integrity of the data, including deleting incomplete or unavailable images;

[0080] Divide the number of livestock behavior induced states into small batches for batch gradient descent when training the livestock behavior induced state model. Design a data pipeline to ensure that data can be efficiently loaded and processed during training. Save the preprocessed data as the input for livestock behavior induced state model training. Use the livestock behavior induced state data to build the livestock behavior induced state model and train it. Use convolutional neural networks (CNNs) for architecture. They perform well in processing image data. During training, the livestock behavior induced state model will learn to recognize and understand livestock, water splashes or objects in different environments, and perform livestock behavior induced state training on livestock behavior induced state models. The model is verified and optimized according to the verification results; different optimization algorithms are selected, such as stochastic gradient descent SGD. In each iteration, the error of the current model is measured by calculating the gradient of the loss function to the parameters of the livestock behavior-induced state model. SGD uses only one random sample or a small batch of samples to calculate the gradient in each iteration, and uses the calculated gradient to update the parameters of the livestock behavior-induced state model. Repeat the above steps until the predetermined number of iterations is reached or the stopping condition is met. The trained livestock behavior-induced state model is embedded in the system to monitor the external environment in real time. When the equipment is working, the livestock behavior-induced state model will be updated in real time. Infer the image obtained from the camera, judge the position, direction or strength of livestock, water or objects to protect the monitoring head 2, at this time, the output shaft of the first motor 316 rotates to drive the bevel gear A312 to rotate, and the bevel gear A312 is engaged with the bevel gear B313, so that the rotation of the bevel gear B313 drives the screw rod 314 to rotate, and the screw rod 314 is threadedly connected with the threaded hole 315 in the middle of the fixed plate 34, thereby driving the movable plate 35 to move up and down, and at the same time cooperates with the slide groove 318 to slide on the sliding block 317 fixed in the middle of the bevel gear B313, so that the movement of the movable plate 35 drives the pulling rod 36 Pulling the driving rod 38 to move drives the protective cover 31 to expand or close, and the screw rod 314 can be used for effective limiting, and the output shaft of the second motor 44 drives the roller 43 to rotate, and the upper and lower rollers 43 are limited on the moving track 41, so that the connecting frame 42 moves along the moving track 41 and the monitoring head 2 moves. Through the setting of the mobile component, the situation of the livestock can be monitored from multiple angles, and the monitoring can be moved from the top to the bottom to avoid damage or abnormalities that cannot be discovered and handled in time, thereby improving the supervision effect and ensuring the healthy growth of livestock.

Claims

1. A livestock supervision system based on visual images, characterized in that: Include: Collect livestock behavior-induced status data, collect livestock movement, splash and other data; The livestock behavior-induced state data is labeled, and the livestock position, water splash direction, and object position of the livestock behavior-induced state data are labeled; Preprocessing of livestock behavior-induced state data, including data cleaning and establishment of data pipelines; Establishing livestock behavior induced state model training, using livestock behavior induced state data to establish livestock behavior induced state model and conduct training; Optimization and verification of livestock behavior-induced state model: verify the livestock behavior-induced state model and optimize it according to the verification results; Livestock behavior-induced state model deployment, embedding the trained livestock behavior-induced state model into the system to monitor the external environment in real time; Execute the device. When the device is working, the livestock behavior-induced state model will infer the image obtained from the camera in real time, determine the position, direction or strength of the livestock, water splashes or objects, and protect the monitoring head.

2. The livestock supervision system based on visual images according to claim 1 is characterized in that: The state data triggered by livestock behavior is collected by using a monitoring head, including different movements of livestock, or behaviors of each other, or image data of splashes or objects caused by behaviors, including situations in various environments and angles.

3. The livestock monitoring system based on visual images according to claim 1, characterized in that: The livestock behavior-induced state data annotation uses Bounding Box annotation, which is adapted to the task of detecting the position of an object, and includes drawing a rectangular box in the image to frame the target object in the image, and providing coordinate information of the rectangular box; Label each pixel in the image to indicate which object or category it belongs to, by drawing the outline or boundary of each object; Using keypoint annotation, we can capture the key parts of an object, including marking the key points of an object in an image, such as animal joints.

4. The livestock monitoring system based on visual images according to claim 1 is characterized in that: The livestock behavior triggers state data preprocessing, loading the collected image data into a computer, checking and cleaning the data, including processing any missing or damaged images, ensuring the integrity of the data, including deleting incomplete or unusable images; Divide the number of livestock behavior-induced states into small batches for batch gradient descent when training the livestock behavior-induced state model. Design a data pipeline to ensure that data can be efficiently loaded and processed during training. Save the preprocessed data as input for livestock behavior-induced state model training.

5. The livestock monitoring system based on visual images according to claim 1 is characterized in that: The livestock behavior-induced state model training is constructed using convolutional neural networks, which perform well in processing image data. During the training process, the livestock behavior-induced state model will learn to recognize and understand livestock, water splashes or objects in different environments.

6. The livestock supervision system based on visual images according to claim 1 is characterized in that: The livestock behavior-induced state model optimization verification selects different optimization algorithms, such as stochastic gradient descent. In each iteration, the error of the current model is measured by calculating the gradient of the loss function with respect to the livestock behavior-induced state model parameters. SGD uses only one random sample to calculate the gradient in each iteration, and uses the calculated gradient to update the parameters of the livestock behavior-induced state model. The verification is repeated until a predetermined number of iterations is reached or a stopping condition is met.

7. The livestock monitoring system based on visual images according to claim 1 is characterized in that: The execution device comprises: livestock sheds; A monitoring head is arranged inside the livestock shed through a protective component, and monitors the lower side through a moving component.

Images