Body weight measuring device and body weight measuring method

Abstract

To provide a body weight measuring device capable of measuring a body weight of an object animal appropriately.SOLUTION: A body weight measuring device includes: image acquisition means 31 for imaging an object animal with a camera 26 and acquiring a plane image from above and a side image from a side of the object animal; plane dimension and width information acquisition means 33 for acquiring plane dimension and width information on the object animal on the basis of the plane image; side surface dimension and width information acquisition means 34 for acquiring side surface dimension and width information on the object animal on the basis of the side image; and body weight information acquisition means 40 including a machine learning model 41 acquired by machine learning with the plane dimension and width information and the side surface dimension and width information as explanatory variables, and with weight information on the object animal as objective variables for acquiring body weight information, which is the objective variables, by giving the acquired plane dimension and width information, and side surface dimension and width information to the machine learning model as the explanatory variables.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a weight measurement device and a weight measurement method. [Background technology]

[0002] The pig weight measuring device described in Patent Document 1 consists of a dustproof box attached to the ceiling of a pigpen near the water supply, a projector with a checkered slide attached, and a video camera installed inside the dustproof box. When light is projected from the projector, a checkered pattern is projected onto the floor and the surface of the pig's body. This image is captured by a video camera, and the pig's withers height (H) is calculated based on the misalignment of the checkered pattern in the captured image. The captured image is also binarized to black and white to determine the pig's projected area (A). There is a close relationship between the pig's withers height (H), projected area (A), and weight (W), expressed as W = aHbAc. Furthermore, the dimension of this multiple regression equation (d = b + 2c) is close to the dimension of volume (d = 3). Therefore, weight (W) is calculated using the withers height (H) and projected area (A).

[0003] Patent Document 2 discloses that a measuring device is provided that sets three selection criteria for fish during the fish farming process or at the time of shipping: body length only, body length and weight, and weight only, and that by selecting one of these criteria, the body length and weight of the fish can be measured efficiently and accurately without weakening them while they are still alive, allowing for multi-stage selection.

[0004] This measuring device is equipped with a measuring chute that slopes downward to allow a constant flow of water, and a passage detector attached midway to detect fish passing through the chute. Light sources and cameras capable of taking still images of fish based on the detector's detection signal are installed above and below the measuring chute. Meanwhile, a load cell that emits an output proportional to the fish's weight has its movable part attached to the measuring chute and its fixed part attached to the weighing frame. Images are processed using an image processing device and image memory built into the control device, and the image and load cell output are calculated into body length and weight data by the control device's CPU. The control device's sorting criteria setting device compares the input with three sorting criteria: body length only, body length and weight, and weight only, and a sorting device attached downstream sorts the fish into multiple stages.

[0005] Patent Document 3 describes the provision of an animal weight measuring device, a measuring facility, and an automatic measuring device.

[0006] This weighing device is composed of a load sensor, a weighing platform attached horizontally to the top of the sensor and of a predetermined size depending on the target animal, side fences arranged approximately vertically on the left and right ends of the weighing platform and of a predetermined height and depth depending on the target animal, and a front fence installed on the front end of the weighing platform and shorter than the side fences.The rear end between the side fences is open.The weighing facility is composed of one or more weighing devices arranged in a ring or horizontal row, a feeder 11 placed near the front of the front fence, and barrier members placed outside the side fences to prevent access to the feeder.The automatic weighing device is composed of a weighing device, a cable for transmitting the sensor signal, and a data collection and processing device. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-286421 [Patent Document 2] Japanese Patent Application Publication No. 8-050052 [Patent Document 3] Japanese Patent Application Laid-Open No. 2003-114145 Summary of the Invention [Problem to be solved by the invention]

[0008] In the case of Patent Document 1, it is difficult to project the grid pattern onto the floor and the surface of the pig's body, and therefore appropriate measurements cannot always be made. The case of Patent Document 2 is based on measuring fish, and has the problem of being difficult to apply to measuring the weight of other animals.

[0009] The device in Patent Document 3 is premised on guiding the animal to the weighing platform, which is difficult. The biggest issue facing pig farms is the aging population and declining working population. Weighing pigs requires a great deal of effort, which leads to the problem that pigs cannot be weighed and are not shipped at the optimal shipping time. An embodiment of the present invention provides a weight measuring device that can appropriately measure the weight of the target animal. [Means for solving the problem]

[0010] A weight measuring device according to an embodiment of the present invention is characterized by comprising: an image acquisition means for capturing an image of a target animal using a camera and obtaining a planar image from above and a side image from the side of the target animal; a planar dimension / area information acquisition means for obtaining planar dimension / area information of the target animal based on the planar image; a side dimension / area information acquisition means for obtaining side dimension / area information of the target animal based on the side image; and a machine learning model obtained by machine learning using the planar dimension / area information and the side dimension / area information as explanatory variables and the weight information of the target animal as a target variable, and providing the obtained planar dimension / area information and the side dimension / area information as explanatory variables to the machine learning model to obtain weight information, which is the target variable.

[0011] A weight measuring method according to an embodiment of the present invention comprises an image acquisition step of capturing an image of a target animal using a camera to obtain a planar image from above and a side image from the side of the target animal; a planar dimension and width information acquisition step of obtaining planar dimension and width information of the target animal based on the planar image; a side dimension and width information acquisition step of obtaining side dimension and width information of the target animal based on the side image; and a weight information acquisition step of using a machine learning model obtained by machine learning with the planar width information, the planar length information, and the side dimension and width information as explanatory variables and the weight information of the target animal as a target variable, and providing the obtained planar width information, the planar length information, and the side dimension and width information to the machine learning model as explanatory variables to obtain weight information, which is the target variable. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a configuration diagram of a weight measurement device according to an embodiment of the present invention configured by a computer. [Figure 2] FIG. 1 is a functional block diagram of a weight measurement device according to a first embodiment of the present invention. [Figure 3] FIG. 2 is an explanatory diagram of planar dimension / area information acquired by a weight measurement device according to an embodiment of the present invention. [Figure 4] FIG. 3 is an explanatory diagram of side dimension / area information acquired by a weight measurement device according to an embodiment of the present invention. [Figure 5] 4 is a flowchart showing the operation of the weight measurement device according to the first embodiment of the present invention. [Figure 6] FIG. 6 is a functional block diagram of a weight measurement device according to a second embodiment of the present invention. [Figure 7] FIG. 10 is an explanatory diagram of appropriate image selection by the weight measurement device according to the embodiment of the present invention. [Figure 8] FIG. 4 is an explanatory diagram of a correction process performed by the weight measurement device according to the embodiment of the present invention. [Figure 9] 10 is a flowchart showing the operation of a weight measurement device according to a second embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0013] A weight measuring device and a weight measuring method according to an embodiment of the present invention will be described below with reference to the accompanying drawings. In each drawing, the same components are designated by the same reference numerals, and duplicate explanations will be omitted. FIG. 1 shows a configuration diagram of a computer in which a weight measuring device according to an embodiment of the present invention is configured by a computer. That is, a CPU 10 configures the weight measuring device using programs and data in a main memory 11. An external storage interface 13, an input interface 14, a display interface 15, and a data input interface 16 are connected to the CPU 10 via a bus 12.

[0014] An external storage device 23 is connected to the external storage interface 13. The external storage device 23 stores programs and data for the operation of this weight measurement device, which can be read out and used by the CPU 10 in the main memory 11 as appropriate. For this reason, the external storage device 23 stores programs for implementing the machine learning models and various means described below. An input device 24 such as a keyboard or touch panel and a pointing device 22 such as a mouse are connected to the input interface 14. A display device 25 having a screen such as an LCD is connected to the display interface 15. Cameras 26-1 to 26-m are connected to the data input interface 16, and these cameras 26-1 to 26-m function to capture images of the target animal. The number of cameras 26-1 to 26-m is arbitrary. Image data obtained by the cameras 26-1 to 26-m is input by the data input interface 16 to required parts such as the CPU 10 and the display interface 15.

[0015] 2 shows a functional block diagram of a weight measuring device according to a first embodiment of the present invention. This embodiment is equipped with a camera 26, image acquisition means 31, individual information management means 32, planar dimension / area information acquisition means 33, lateral dimension / area information acquisition means 34, and weight information acquisition means 40. These means can be realized by the CPU 10 appropriately reading a program from the external storage device 23 into the main memory 11, or can be realized using a program that is initially stored in the main memory 11.

[0016] The camera 26 can be a 3D camera. The image acquisition means 31 images the target animal using the camera 26 and obtains a planar image from above and a side image from the side of the target animal. The individual information management means 32 uses the images obtained by the image acquisition means 31 to identify the target animal individually, attaches identification information to the image of each individual target animal, and provides the image with this identification information attached to the planar dimension / area information acquisition means 33, the side dimension / area information acquisition means 34, and the weight information acquisition means 40.

[0017] The planar dimension / area information acquisition means 33 acquires planar dimension / area information of the target animal based on the planar image acquired by the image acquisition means. In this embodiment, the planar dimension / area information refers to planar width information and planar length information. However, it goes without saying that dimensional information in diagonal directions on a plane or area information of a specific portion of the plane may also be used. Planar width information of the target animal will now be described. Assuming that an image of the target animal from above (here, an image facing left) is shown in U of FIG. 3, the image is divided vertically in the figure, and the width of the animal image (the vertical length in the figure) is measured from the center to the left in the resulting areas. The measurement value of the largest divided area is taken as the shoulder width (FIG. 3(B)). Furthermore, measurements are also made from the same center to the right, and the measurement value of the largest divided area is taken as the hip width (FIG. 3(B)). These values ​​can be used as planar width information. Alternatively, waist width information, etc., determined by a predetermined definition, may also be used as planar width information. The planar width information can be at least one of shoulder width information of the target animal, waist width information of the target animal, and hip width information of the target animal.

[0018] Next, the planar length information will be explained. As described above, the shoulder position and hip position are determined in the process of obtaining the planar width information, and the distance between the shoulder position and hip position is taken as the body length. This body length can be used as the planar length information. The distance between the shoulder position and hip position can also be taken as the body length.

[0019] The lateral dimension / area information acquisition means 34 acquires the lateral dimension / area information of the target animal based on the lateral image acquired by the image acquisition means 31. In this embodiment, the lateral dimension / area information is body height information, but it goes without saying that dimension information in diagonal directions on the side or area information of a specific portion of the side may also be used. The planar lateral dimension / area information of the target animal may be body height information, which is body height information of at least one of the shoulder, hip, and rump positions of the target animal. For example, as shown in FIG. 4, using a planar image U and a lateral image S of the target animal, first, the highest point in the longitudinal direction of the same divided area on the lateral image S is determined. Measurements are made from the center of the image to the left, and the height measurement of the highest divided area is taken as the shoulder height. Measurements are made from the center of the image to the right, and the height measurement of the highest divided area is taken as the rump height. Alternatively, the lateral dimension / area information of the hips, etc., determined by a predetermined definition, may be used as the planar width information. The planar side dimension / area information can be withers height information, which is withers height information at least one of the shoulder position, waist position, and rump position of the target animal.

[0020] The weight information acquisition means 40 is equipped with a machine learning model 41 obtained by machine learning using the planar dimension / area information and the side dimension / area information as explanatory variables and the weight information of the target animal as a response variable. This machine learning model 41 can be created by performing machine learning using the planar dimension / area information and the side dimension / area information obtained in this embodiment as explanatory variables and actually measuring the weight information of the target animal as a response variable. The weight information acquisition means 40 provides the obtained planar dimension / area information and the side dimension / area information as explanatory variables to the machine learning model 41 to obtain weight information, which is the response variable.

[0021] The weight measuring device configured as described above performs processing according to a program corresponding to the flowchart shown in Fig. 5. The operation will be described below based on the flowchart. The target animal is imaged by the camera 26 (S11). The process of imaging the target animal by the camera 26 continues in the processes in the following steps.

[0022] When the target animal is imaged by the camera 26, the CPU 10 receives the image data (S12) and calculates planar width information, planar length information, and side dimension / area information (S13). The CPU 10 provides the calculated planar width information, planar length information, and side dimension / area information to a machine learning model to obtain the weight of the target animal (S14). In this way, it becomes possible to appropriately measure the weight of the target animal.

[0023] Next, a weight measuring device according to a second embodiment of the present invention will be described. Fig. 6 shows a functional block diagram of a weight measuring device according to the second embodiment of the present invention. In this embodiment, the camera 26 and image acquisition means 31 are the same as those in the first embodiment. This embodiment also includes individual information management means 35, individual target animal image acquisition means 36, appropriate image selection means 37, and correction processing means 38. Furthermore, in this embodiment, the target animal will be described as a pig, but the target animal of the present invention is not limited to this.

[0024] The individual information management means 35 uses the images obtained by the image acquisition means 31 to perform individual identification (image recognition) of the target animals, assigns identification information to the images of each individual target animal, and provides the images with this identification information to the planar dimension / area information acquisition means 33, the lateral dimension / area information acquisition means 34, and the weight information acquisition means 40. In other words, when multiple target animals are kept together, the individual information management means 35 assigns identification information to each target animal and manages the images. More specifically, it uses RGB images for tracking and generates a bounding box for each pig. When there are multiple pigs, a separate bounding box and its coordinates are calculated for each pig, and the results are passed to the individual target animal image acquisition means 36, which is a target selection unit.

[0025] This embodiment is suitable for cases where multiple target animals are kept together, and the camera 26 captures an image of the location where the multiple target animals are present. As a result, the multiple target animals appear overlapping in the image. The individual target animal image acquisition means 36 separates the images of the individual target animals from the image captured by the camera 26 to acquire the individual target animal images. In this embodiment, a segmentation method can be employed. By using this segmentation method, even overlapping pigs can be clearly separated, and a mask image of each pig is generated and used for processing by the weight information acquisition means 40.

[0026] The appropriate image selection means 37 performs processing to select only images that can be processed appropriately when providing images with the above-mentioned identification information to the planar dimension / area information acquisition means 33, the side dimension / area information acquisition means 34, and the weight information acquisition means 40. If the pig's posture is bent, the values ​​of each part cannot be measured correctly. Therefore, we considered what to do with images in which the body is bent. Since this embodiment uses a fixed camera, it is possible to photograph the same pig multiple times, so we decided to discard data when the body is bent and use only images taken in a good state.

[0027] Therefore, we decided to eliminate images in which the back was bent. Next, we can determine whether the body is bent as follows. When obtaining planar dimension and width information, multiple divided areas are created as shown in Figure 3, and the line connecting the highest points of each divided area in Figure 7 corresponds to the spine. Detecting the degree of curvature of this spine can determine the pig's posture. An ellipse is fitted in advance using image processing, and the center line of this ellipse is the ideal position of the spine. Images that deviate significantly from this line are judged to have high curvature and are eliminated (Figure 7(B)), while images that deviate less from this line are retained (7(A)). For example, images that are more than a certain distance from the line connecting the highest points of each divided area can be eliminated. Furthermore, because pigs judged to be normal are also slightly bent, data on the deviation from the ideal position indicating the curvature of the spine (e.g., variance) can also be added as an explanatory variable.

[0028] The correction processing means 38 performs correction processing to orient the image selected by the appropriate image selection means 37 in a predetermined direction. In this embodiment, the planar dimension / area information acquisition means 33 and the lateral dimension / area information acquisition means 34 use 3D images of a single animal extracted by the individual target animal image acquisition means 36 using a segmentation technique to measure each part. The parts to be measured can include shoulder width information, hip width information, hip width information, length from shoulder to hip, and total body area. To measure each part, it is easy to calculate the measurements by cutting the image of the pig into slices at regular intervals and processing them sequentially from the head, as described above. However, in this embodiment, the pig is photographed as it comes to eat, so images are acquired in various orientations. Since the posture is not constant, some ingenuity is required. The processing performed by the correction processing means 38, including solutions to these issues, is described below.

[0029] <Correction processing> The 3D image is binarized and an ellipse is approximated along the boundary of the body. If the image of the pig is cut out cleanly, an ellipse that fits the body can be drawn, with the center of the ellipse corresponding to the center of the body and the angle of the ellipse matching the angle of the pig. Therefore, the angle of the ellipse in the state shown in Figure 8(A) is calculated, and the image is rotated using an affine transformation with the center of the ellipse as the rotation axis so that the angle of the ellipse that makes the image horizontal is horizontal (Figure 8(B)).

[0030] The weight measuring device configured as described above performs processing according to a program corresponding to the flowchart shown in Fig. 9. The operation will be described below based on the flowchart. CPU 10 captures an image of the target animal with camera 26 (S11). Once the target animal is captured by camera 26, CPU 10 receives the captured image data (S12) and separates overlapping images of the target animals into a single image (S21).

[0031] Next, CPU 10 performs individual identification (image recognition) and assigns identification information to the image of each individual target animal (S22). Next, disregarded images are discarded and only images captured in good condition are used (S23). CPU 10 also performs ellipse approximation along the boundary surface of the target animal's body, calculates the angle of the ellipse, and performs correction by rotating the image using affine transformation with the center of the ellipse as the axis of rotation so that the angle of the ellipse is horizontal (S24). Furthermore, planar width information, planar length information, and side dimension / area information are calculated (S13). CPU 10 provides the calculated planar width information, planar length information, side dimension / area information to a machine learning model to obtain the weight of the target animal (S14). In this way, it is possible to accurately measure the weight of the target animal.

[0032] Furthermore, the weight information acquisition means 40 may calculate the average value of weight information obtained within a predetermined time period for a target animal identified as an individual, and use the calculated average value as the weight of the target animal. Even if the target animal remains within the field of view, there may be cases where weight estimation is not possible due to poor posture or other conditions, resulting in a reduced number of weight measurements. If the acquired weight measurement value is below a set threshold, the acquired data may be discarded. Only when weight measurements are obtained the threshold number of times or more may the data be averaged, and the weight measurement value for one target animal may be completed.

[0033] Furthermore, the weight measuring device may be provided with a program that realizes a type determination means (not shown) that determines type information within the target animal based on the planar dimension / area information and the side dimension / area information. In this case, the individual information management means 35 performs an operation of providing an image with identification information attached together with the type information determined by the type determination means to the planar dimension / area information acquisition means 33, the side dimension / area information acquisition means 34, and the weight information acquisition means 40, and the weight information acquisition means 40 may adjust the weight information using parameters according to the type information within the target animal when acquiring weight information.

[0034] The above-described embodiments can reduce the labor required for measuring pig weight, enable daily management and data collection of pig weight, and can also lead to maximization of profits for pig farms by optimizing shipping times. [Explanation of symbols]

[0035] 10 CPU 11 Main Memory 12 Bus 13 External memory interface 14 Input Interface 15 Display Interface 16 Data Input Interface 22 Pointing Device 23 External storage device 24 Input Devices 25 Display device 26 Camera 31 Image acquisition means 32 Individual information management means 33 Information acquisition means 34 Information acquisition means 35 Individual information management means 36 Individual target animal image acquisition means 37 Appropriate image selection method 38 Correction processing means 40 Weight information acquisition means 41 Machine Learning Models

Claims

1. an image acquisition means for capturing an image of a target animal using a camera to obtain a planar image from above and a side image from the side of the target animal; a planar size / area information acquisition means for acquiring planar size / area information of the target animal based on the planar image; side dimension / area information acquisition means for acquiring side dimension / area information of the target animal based on the side image; a weight information acquisition means for providing the planar dimension / area information and the side dimension / area information as explanatory variables to the machine learning model, the planar dimension / area information and the side dimension / area information as explanatory variables, and the weight information of the target animal as a response variable, to the machine learning model to obtain the weight information as a response variable; A weight measuring device comprising:

2. The weight measuring device according to claim 1, further comprising an individual information management means for performing individual identification of the target animal using the image obtained by the image acquisition means, attaching identification information to the image of each individual target animal, and providing the image with this identification information attached to the planar dimension / area information acquisition means, the lateral dimension / area information acquisition means, and the weight information acquisition means.

3. the camera captures an image of a location where a plurality of the target animals are present; 2. The weight measuring device according to claim 1, further comprising an individual target animal image acquiring means for acquiring individual target animal images by separating images of the individual target animals from the images obtained by the camera.

4. The weight measuring device according to claim 2, further comprising an appropriate image selection means for selecting only images that can be processed appropriately when the image with the identification information attached is provided to the planar dimension / area information acquisition means, the side dimension / area information acquisition means, and the weight information acquisition means.

5. 5. The weight measuring device according to claim 4, further comprising a correction processing unit that performs a correction process on the image selected by the appropriate image selection unit so that the image is oriented in a predetermined direction.

6. 2. The weight measuring device according to claim 1, wherein the planar width information of the target animal is at least one of shoulder width information of the target animal, waist width information of the target animal, and hip width information of the target animal.

7. The weight measuring device according to claim 1, wherein the planar length information of the target animal is a body length, which is the length from the shoulder to the hip of the target animal.

8. 2. The weight measuring device according to claim 1, wherein the information on the planar side dimensions and width of the target animal is information on withers height at at least one position of the shoulder, waist, and hips of the target animal.

9. The weight measuring device according to claim 2, characterized in that the weight information acquisition means calculates the average value of weight information obtained within a predetermined time period for a target animal identified as an individual, and sets the calculated average value as the weight of the target animal.

10. a species determination means for determining species information within the target animal based on the planar dimension / area information and the side dimension / area information; the individual information management means performs an operation of providing an image to which identification information is attached together with the type information determined by the type determination means to the planar dimension / area information acquisition means, the side dimension / area information acquisition means, and the weight information acquisition means; 3. The weight measurement device according to claim 2, wherein the weight information acquisition means adjusts the weight information by using a parameter corresponding to the type information when acquiring the weight information.

11. an image acquisition step of capturing an image of a target animal using a camera to obtain a planar image from above and a side image from the side of the target animal; a planar size / area information acquisition step of acquiring planar size / area information of the target animal based on the planar image; a side dimension / area information acquisition step of acquiring side dimension / area information of the target animal based on the side image; a weight information acquisition step of using a machine learning model obtained by machine learning with the planar dimension / area information and the side dimension / area information as explanatory variables and the weight information of the target animal as a target variable, and providing the planar dimension / area information and the side dimension / area information obtained as explanatory variables to the machine learning model to obtain weight information as a target variable; A weight measurement method comprising:

12. The weight measurement method according to claim 11, further comprising an individual information management step of performing individual identification of the target animal using the image obtained by the image acquisition step, attaching identification information to the image of each individual target animal, and providing the image with this identification information attached to the planar dimension / area information acquisition step, the lateral dimension / area information acquisition step, and the weight information acquisition step.

13. the camera captures an image of a location where a plurality of the target animals are present; The weight measurement method according to claim 11, further comprising an individual target animal image acquisition step of acquiring individual target animal images by separating images of the individual target animals from the image obtained by imaging with the camera.

14. The weight measurement method according to claim 12, further comprising an appropriate image selection step for selecting only images that can be processed appropriately when the image with the identification information attached is provided to the planar dimension / area information acquisition step, the side dimension / area information acquisition step, and the weight information acquisition step.

15. 15. The weight measurement method according to claim 14, further comprising a correction processing step of performing a correction process to orient the image selected in the appropriate image selection step so that the image is oriented in a predetermined direction.

16. The weight measurement method according to claim 11, wherein the planar width information of the target animal is at least one of shoulder width information of the target animal, waist width information of the target animal, and hip width information of the target animal.

17. The weight measurement method according to claim 11, wherein the planar length information of the target animal is the length from the shoulder to the buttocks of the target animal.

18. 12. The weight measuring method according to claim 11, wherein the planar side dimension / area information of the target animal is withers height information of at least one of the shoulder position, waist position, and hip position of the target animal.

19. The weight measurement method according to claim 12, characterized in that the weight information acquisition step calculates the average value of weight information obtained within a predetermined time period for a target animal identified as a single individual, and sets the calculated average value as the weight of the target animal.

20. a type determination step of determining type information of the target animal based on the planar dimension / area information and the side dimension / area information, the individual information management step performs an operation of providing an image to which identification information is attached together with the type information determined in the type determination step to the planar dimension / area information acquisition step, the side dimension / area information acquisition step, and the weight information acquisition step; 13. The weight measurement method according to claim 12, wherein the weight information acquisition step adjusts the weight information by using a parameter corresponding to the type information when obtaining the weight information.

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