An automated measurement system for rabbit weight and body size based on machine vision

Abstract

The present invention discloses a machine vision-based automated rabbit weight and body measurement system, which relates to the field of breeding data analysis technology. The system includes an automated measurement platform, which is communicatively connected to an image capture module, a rabbit feature extraction module, a posture transformation module, a body measurement module, a weight measurement module, a data calibration and verification module, and a user interaction module. Each module is electrically connected to the image capture module, which is used to capture rabbit image data from different angles. This machine vision-based automated rabbit weight and body measurement system uses image processing technology to measure rabbit weight and body measurement, monitor rabbit growth, and capture weight and body measurement trends. This allows managers to promptly identify growth abnormalities or potential health problems in rabbits and take appropriate intervention measures, thereby optimizing breeding and management strategies and improving the rabbits' growth performance and health.

Description

Technical Field

[0001] The present invention relates to the technical field of breeding data analysis, and in particular to a machine vision-based automatic measurement system for meat rabbit weight and body size. Background Art

[0002] With the advancement of science and technology, animal husbandry is gradually developing towards intelligence and automation. Meat rabbit breeding occupies a certain position in the animal husbandry industry. However, due to its high breeding density and complex management, the traditional manual method of measuring weight and body size has many inconveniences. Machine vision technology can realize accurate identification and measurement of individual animals through image acquisition, processing and analysis. In the process of measuring the weight and body size of meat rabbits, the use of machine vision technology can achieve rapid and accurate identification of meat rabbits.

[0003] In the existing technology, due to individual differences between meat rabbits, different body shapes and postures of meat rabbits will lead to morphological differences, which in turn interfere with the measurement of the weight and body size of meat rabbits. Therefore, how to reduce the differences in body shape and posture of meat rabbits to improve the accuracy of machine vision recognition and reduce measurement errors is the problem we want to solve. To this end, we now propose an automated measurement system for meat rabbit weight and body size based on machine vision. Summary of the Invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a machine vision-based automated measurement system for rabbit weight and body dimensions, comprising an automated measurement platform, wherein the automated measurement platform is communicatively connected to an image capture module, a rabbit feature extraction module, a posture transformation module, a body dimension measurement module, a weight measurement module, a data calibration and verification module, and a user interaction module, wherein the modules are electrically connected;

[0005] The image capture module is used to capture image data of meat rabbits from different angles and preprocess the image data, including denoising, contrast enhancement, and brightness adjustment, to improve image quality and generate a multi-view image set;

[0006] The rabbit feature extraction module uses edge detection and texture analysis technology to extract the body features of the rabbit, including body contours and joint positions, from the image data of the multi-view image set, and matches feature point pairs to form a body feature sequence;

[0007] The posture transformation module transforms the different postures of the meat rabbit based on the feature data of the body feature sequence, and adjusts the posture of the meat rabbit to a standard posture through image transformation and geometric correction technology to reduce measurement errors caused by posture differences and facilitate subsequent processing;

[0008] The body size measurement module measures the body size data of the meat rabbit by reconstructing a 3D point cloud based on the matched feature point pairs in the image data in combination with the 3D reconstruction technology;

[0009] The weight measurement module measures the weight of the rabbit based on its body shape characteristics, based on the measured body size data and the corrected posture, combined with a weight prediction model constructed using machine learning technology;

[0010] The data calibration and verification module calculates the weight and body size matching coefficient by comparing with the set standard value, calibrates and verifies the measurement results, and analyzes the degree of deviation between the automated measurement data and the standard value;

[0011] The user interaction module includes a display screen and buttons, which are used to display captured images, measurement results and human-computer interaction parameters related to user settings.

[0012] Preferably, in the image capturing module, the process of capturing the rabbit image data includes:

[0013] Configure cameras with high resolution and wide-angle lenses at multiple angles, initialize image capture parameters such as resolution, frame rate, and exposure, and preset the capture angle and position of the rabbit image. The cameras have high resolution and wide-angle lenses to ensure that clear and complete images of the rabbit are captured;

[0014] The rabbit is positioned within the capture area, ensuring that it is within the camera's field of view. The configured camera is adjusted to capture image data of the rabbit from multiple perspectives according to a preset capture angle and position. The captured images cover the entire body of the rabbit, including the side, front, and back.

[0015] Images captured by different cameras are time-synchronized to ensure image data consistency, and the captured image data is pre-processed, including denoising, contrast enhancement, and brightness adjustment. Denoising eliminates noise and interference in the image. Denoising methods include Gaussian filtering and median filtering. Contrast enhancement improves the brightness difference between different areas in the image to make the image clearer. Contrast enhancement methods include linear transformation and histogram equalization. Brightness adjustment improves the brightness level of the image to make it more suitable for observation or analysis needs.

[0016] The pre-processed image data are classified and sorted according to different capture angles, and the classified image data are integrated to generate a multi-view image set containing multiple perspectives of meat rabbits.

[0017] Preferably, in the meat rabbit feature extraction module, the process of acquiring the body feature sequence includes:

[0018] Receive image data from a multi-view image set, including full-body images of the rabbit from the side, front, and back, ensuring that the image data is clear and complete, without obvious blur, occlusion, or noise interference;

[0019] Applying the Sobel operator edge detection technique to the image data, the gradient amplitude of the image is calculated to highlight the edge. The result of the Sobel operator is used to identify the edge features of the rabbit's body contour and joint positions in the image. The edge features are represented by high gradient value areas in the image.

[0020] The texture features in the image are analyzed using the texture analysis technology of the gray-level co-occurrence matrix. The texture feature parameters of contrast, energy, entropy, and correlation are extracted from the gray-level co-occurrence matrix. The skin texture and hair texture features of the meat rabbit are analyzed to reflect the fineness, uniformity, and complexity of the meat rabbit skin texture and hair texture.

[0021] The feature points extracted from the multi-view images are matched, and a feature descriptor is generated for each feature point. The SIFT feature point matching algorithm is used to match the feature point pairs in the images from different viewpoints to determine the feature point pairs of the rabbit's body contour and joint positions. The feature point pairs are locations with obvious features such as corner points and edge intersections in the image.

[0022] The matched feature point pairs are connected in series to form a body feature sequence. The body feature sequence includes the contour line of the body outline (composed of a series of matched edge feature points) and the key points of the joint position (composed of matched joint position feature points). The feature sequence reflects the body features and joint position information of the meat rabbit under different viewing angles.

[0023] Preferably, in the posture transformation module, the process of adjusting the posture of the rabbit includes:

[0024] The body feature sequence outputted from the meat rabbit body feature extraction module is received, a standard posture of the meat rabbit is preset, and computer vision technology is applied to estimate the posture and position of the meat rabbit using key point information in the body feature sequence, wherein the standard posture of the meat rabbit is a standing posture with four limbs;

[0025] Applying image transformation techniques such as affine transformation and perspective transformation to the original image, performing preliminary posture adjustment on the image of the meat rabbit, and preliminarily adjusting the posture of the meat rabbit to a state close to the standard posture;

[0026] Based on the image transformation, the geometric correction technology is applied to calculate the perspective transformation matrix, which is then applied to the image to further adjust the rabbit's posture and correct the geometric distortion caused by the difference in viewing angle to obtain the rabbit image in a standard posture.

[0027] Perform posture transformation and use the calculated transformation matrix to adjust the rabbit's posture to the preset standard posture, ensuring that the accuracy of the rabbit's body proportions and feature point positions is maintained during the transformation process. Use feature point matching technology to check whether the transformed feature points match the feature points of the standard posture and verify whether the transformed posture meets the requirements of the standard posture. If not, the posture needs to be re-estimated and the rabbit image adjusted to the standard posture is output for use by the subsequent weight determination module and body size measurement module.

[0028] Preferably, in the body size measurement module, the process of measuring the body size data of the rabbit includes:

[0029] Extract rabbit images captured from different perspectives to ensure that the images contain sufficient feature information for matching. Apply the SIFT algorithm to each image to extract the image's feature points (i.e., key points) and their descriptors. Use the distance metric (Euclidean distance) between the descriptors to match pairs of feature points in different images.

[0030] The camera's intrinsic parameters (focal length, optical center) and extrinsic parameters (rotation matrix, translation vector) are calculated using the known 3D world coordinate points and the corresponding image coordinate points. The camera's pose, i.e., its position and attitude in space, is estimated using the PnP optimization algorithm using the matched feature point pairs and the known camera intrinsic parameters.

[0031] Based on the matched feature point pairs and the camera's position, the coordinates of the points in 3D space are calculated using the principle of triangulation, thereby reconstructing a sparse 3D point cloud. The reconstructed 3D point cloud is then filtered to remove noise and redundant points caused by matching errors and illumination changes. The point cloud is then segmented into different parts based on its geometric features, and the part of the point cloud representing the meat rabbit is extracted from the segmented point cloud.

[0032] In the extracted meat rabbit point cloud data, the geometric features of body length, body height, and body width are identified. Body length refers to the straight-line distance from the head to the tail, body height refers to the distance from the ground to the highest point, and body width refers to the horizontal distance at the widest point. Based on the identified geometric features, the corresponding dimensions are measured in the point cloud data and the body size data of the meat rabbit is calculated.

[0033] Preferably, the calculation formula of the body length is:

[0034]

[0035]

[0036] Among them, L is the body length, N is the number of feature points in the point cloud used to calculate the body length, and p i is the coordinate of the i-th feature point in the point cloud, q i For p iThe corresponding points represent the corresponding points from the head to the tail, d(p i ,q i ) is point p i and q i The Euclidean distance between i The coordinates of (x ip ,y ip , z ip ), q i The coordinates of (x iq ,y iq , z iq );

[0037] The calculation formula of the body height is:

[0038] H=max(z i )-min(z i );

[0039] Where H is the height, z i is the z coordinate value of all points in the point cloud, that is, the vertical height of the ground, max(z i ) is the maximum value of the z coordinate of all points in the point cloud, min(z i ) is the minimum value of the z coordinate of all points in the point cloud;

[0040] The calculation formula of the body width is:

[0041]

[0042] Where W is the body width, M is the number of feature points in the point cloud used to calculate the body width, and w j is the vertical distance from the jth feature point in the point cloud to the body length line.

[0043] Preferably, in the weight measurement module, the process of measuring the weight of the rabbit includes:

[0044] Receive the body size data of the meat rabbit output from the body size measurement module and the posture image processed by the posture correction module, and extract body shape features related to weight from the corrected posture image, including body size, proportion, and shape, wherein the body size includes body length, body width, and body height, which are obtained by measuring the outline of the meat rabbit in the image or the distance between key points; the proportion includes the head-to-body ratio and the ratio of the limbs to the body, which are obtained by calculating the ratio of related dimensions; the shape includes the outline shape of the body and the degree of curvature of the limbs, which are obtained through image processing technology such as edge detection and contour extraction;

[0045] Pre-train a weight prediction model and deploy it in production. Input the extracted body shape features into the pre-trained weight prediction model to output the predicted weight range.

[0046] According to the predicted weight range and the body characteristics of meat rabbits, the weight measurement index is calculated, and the weight measurement standard of meat rabbits is preset and compared with the calculated weight measurement index to evaluate the growth and development status and health level of meat rabbits and obtain the weight measurement evaluation results.

[0047] Preferably, the calculation formula of the body weight index is:

[0048]

[0049] Where I is the body weight index, W is the body width, and W base W is the reference body width of meat rabbit based on age and body size, std is the standard deviation of body width, which is used to adjust the sensitivity of the exponential function, L is the body length, L base is the reference length of meat rabbit based on age and body shape, H is the height, H base The base height of meat rabbits based on age and size.

[0050] Preferably, in the data calibration and verification module, the analysis process of automatically determining the degree of deviation between the data and the standard value includes:

[0051] According to the breeding standards, feeding and management requirements, and industry standards for meat rabbits, standard values for weight and body dimensions (length, height, and width) are set. Weight and dimension data output by the automated measurement system are collected, ensuring a unified data format, including date, time, individual ID, weight, length, height, and width fields, to facilitate subsequent processing and analysis.

[0052] Compare the automated measurement data with the set standard values, check the difference between each measurement value (weight, length, height, width) and the standard value one by one, and calculate the deviation of each measurement value, that is, the difference between the measurement value and the standard value;

[0053] Based on the size of the deviation, calculate the weight and size matching coefficient, analyze the proximity of the automated measurement data to the standard value, analyze the degree of deviation of the automated measurement data from the standard value, and determine whether the data is within the acceptable error range. If the deviation is large, further investigation is required to investigate the cause, including measurement equipment failure, data input errors, or feeding and management problems;

[0054] Based on the analysis results of the degree of deviation and the weight and body size matching coefficient, the automated measurement system is calibrated and adjusted, and the calibration and verification results are output to generate a measurement report. The report contains detailed data comparison, weight and body size matching coefficient, and calibrated data. The calibration verification results and measurement report are then fed back to relevant personnel.

[0055] Preferably, the calculation formula of the weight and body size matching coefficient is:

[0056]

[0057] Among them, P is the weight and size matching coefficient, X k is the kth data point (weight, length, height, width) measured automatically, Y k is the kth standard value corresponding to the automated determination, n is the number of determination values, |X k -Y k | is the absolute difference between the kth measured value and its standard value, and the value of P ranges from 0 to 1.

[0058] The present invention provides a machine vision-based automated measurement system for rabbit weight and size. It has the following beneficial effects:

[0059] 1. This machine vision-based automated rabbit weight and size measurement system uses image processing technology to measure the weight and size of meat rabbits, monitor their growth, and capture changing trends in weight and size. This allows managers to promptly identify growth abnormalities or potential health problems in meat rabbits and take appropriate intervention measures, thereby optimizing feeding and management strategies and improving the growth performance and health of meat rabbits.

[0060] 2. This machine vision-based automated rabbit weight and body size measurement system avoids direct contact and interference with the rabbits through automated non-contact measurement, reduces their stress response, and calculates the weight and body size matching coefficient by comparing the automated measurement data with the standard values, and analyzes the degree of proximity between the automated measurement data and the standard values, providing a scientific basis for the breeding and feeding management of meat rabbits. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a module diagram of a machine vision-based automated measurement system for rabbit weight and size according to the present invention;

[0062] Figure 2 This is a flow chart for measuring the body size data of meat rabbits of the present invention;

[0063] Figure 3 The present invention is a flow chart for measuring the weight of meat rabbits. DETAILED DESCRIPTION

[0064] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described to better illustrate the principles of the invention and its practical application, and to enable those skilled in the art to understand the invention and design various embodiments with various modifications suitable for specific applications.

[0065] The first embodiment, as Figure 1 、 Figure 2 As shown, the present invention provides a technical solution: a machine vision-based automated measurement system for rabbit weight and body size, comprising an automated measurement platform, wherein the automated measurement platform is communicatively connected to an image capture module, a rabbit feature extraction module, a posture transformation module, a body size measurement module, a weight measurement module, a data calibration and verification module, and a user interaction module, wherein electrical signals are connected between the modules;

[0066] The image capture module is used to capture image data of meat rabbits from different angles and pre-process the image data, including denoising, contrast enhancement, and brightness adjustment to improve image quality, generate a multi-view image set, configure cameras with high-resolution and wide-angle lenses at multiple angles, initialize image capture parameters such as resolution, frame rate, and exposure, and preset the capture angle and position of the meat rabbit image. The camera has a high-resolution and wide-angle lens to ensure that a clear and complete meat rabbit image is captured. The meat rabbit is placed in the capture area and is within the camera's field of view. According to the preset capture angle and position, the configured camera is adjusted to capture multiple-view image data of the meat rabbit. The captured image covers the entire body of the meat rabbit, including the side, front, and back. , the images captured by different cameras are time-synchronized to ensure the consistency of image data, and the captured image data is pre-processed, including denoising, contrast enhancement and brightness adjustment. Denoising is used to eliminate noise and interference in the image. Denoising methods include Gaussian filtering and median filtering. Contrast enhancement is used to increase the brightness difference between different areas in the image to make the image clearer. Contrast enhancement methods include linear transformation and histogram equalization. Brightness adjustment is used to improve the brightness level of the image to make it more suitable for observation or analysis needs. The pre-processed image data is classified and sorted according to different capture angles, and the classified image data is integrated to generate a multi-view image set containing multiple perspectives of meat rabbits;

[0067] The meat rabbit feature extraction module uses edge detection and texture analysis technology to extract the body features of the meat rabbit from the image data of the multi-view image set, including body contours and joint positions, and matches feature point pairs to form a body feature sequence. It receives image data from the multi-view image set, including full-body images of the meat rabbit from the side, front, and back, to ensure that the image data is clear and complete without obvious blur, occlusion, or noise interference. It applies the edge detection technology of the Sobel operator to the image data, calculates the gradient amplitude of the image to highlight the edge, and identifies the edge features of the meat rabbit's body contour and joint positions in the image through the results of the Sobel operator. Among them, the edge features are manifested as high gradient value areas in the image. The texture analysis technology of the gray level co-occurrence matrix is used to analyze the texture features in the image, and the contrast, energy, and entropy are extracted from the gray level co-occurrence matrix. , and texture feature parameters of correlation, analyze the skin texture and hair texture features of meat rabbits to reflect the fineness, uniformity and complexity of the skin texture and hair texture of meat rabbits, match the feature points extracted from multi-view images, generate a feature descriptor for each feature point, use the SIFT feature point matching algorithm to match the feature point pairs in images of different viewpoints to determine the feature point pairs of the meat rabbit's body contour and joint positions, where the feature point pairs are positions with obvious features such as corner points and edge intersections in the image, connect the matched feature point pairs in series to form a body feature sequence, which includes the contour line of the body contour (composed of a series of matched edge feature points) and the key points of the joint positions (composed of matched joint position feature points), and the feature sequence reflects the body features and joint position information of meat rabbits under different viewpoints;

[0068] The posture transformation module transforms the different postures of the meat rabbit based on the feature data of the body feature sequence. Through image transformation and geometric correction technology, the posture of the meat rabbit is adjusted to the standard posture to reduce the measurement error caused by the posture difference and facilitate subsequent processing. The body feature sequence output from the meat rabbit body feature extraction module is received, the standard posture of the meat rabbit is preset, and computer vision technology is applied to the key point information in the body feature sequence to estimate the posture and position of the meat rabbit. Among them, the standard posture of the meat rabbit is a standing posture with four limbs. The image transformation technology of affine transformation and perspective transformation is applied to the original image to perform preliminary posture adjustment on the meat rabbit image and adjust the meat rabbit's posture to a state close to the standard posture. Based on the transformation, the geometric correction technology is applied to calculate the perspective transformation matrix and apply it to the image to further adjust the posture of the meat rabbit and correct the geometric distortion caused by the difference in viewing angle to obtain the meat rabbit image in the standard posture. The posture transformation is performed and the calculated transformation matrix is used to adjust the meat rabbit posture to the preset standard posture to ensure that the accuracy of the meat rabbit body proportions and feature point positions is maintained during the transformation process. The feature point matching technology is used to check whether the transformed feature points match the feature points of the standard posture and verify whether the transformed posture meets the requirements of the standard posture. If not, the posture needs to be re-estimated and the meat rabbit image adjusted to the standard posture is output for use by the subsequent weight measurement module and body size measurement module;

[0069] The body size measurement module reconstructs 3D point clouds based on the matched feature point pairs in the image data in combination with 3D reconstruction technology, measures the body size data of meat rabbits, extracts meat rabbit images captured from different perspectives, ensures that the images contain sufficient feature information for matching, applies the SIFT algorithm to each image, extracts the feature points (i.e., key points) in the image and their descriptors, uses the distance metric (Euclidean distance) between the descriptors to match the feature point pairs in different images, calculates the camera's intrinsic parameters (focal length, optical center) and extrinsic parameters (rotation matrix, translation vector) through the known 3D world coordinate points and the corresponding image coordinate points, and uses the matched feature point pairs and the known camera intrinsic parameters to estimate the camera's pose, i.e., the camera's position in space, through the PnP optimization algorithm. and posture, and according to the matched feature point pairs and the camera's posture, the coordinates of the points in the three-dimensional space are calculated using the triangulation principle, thereby reconstructing a sparse three-dimensional point cloud, and filtering the reconstructed three-dimensional point cloud to remove noise and redundant points caused by matching errors and illumination changes. According to the geometric features of the point cloud, the point cloud is segmented into different parts, and the point cloud part representing the meat rabbit is extracted from the segmented point cloud. In the extracted meat rabbit point cloud data, the geometric features of body length, body height, and body width are identified. Body length refers to the straight-line distance from the head to the tail, body height refers to the distance from the ground to the highest point, and body width refers to the lateral distance at the widest point. According to the identified geometric features, the corresponding dimensions are measured in the point cloud data, and the body size data of the meat rabbit is calculated;

[0070] Furthermore, the calculation formula of body length is:

[0071]

[0072] Among them, L is the body length, N is the number of feature points in the point cloud used to calculate the body length, and p i is the coordinate of the i-th feature point in the point cloud, q i For p i The corresponding points represent the corresponding points from the head to the tail, d(p i ,q i ) is point p i and q i The Euclidean distance between i The coordinates of (x ip ,y ip , z ip ), q i The coordinates of (x iq ,y iq , z iq );

[0073] The formula for calculating body height is:

[0074] H=max(z i )-min(z i );

[0075] Where H is the height, z i is the z coordinate value of all points in the point cloud, that is, the vertical height of the ground, max(z i ) is the maximum value of the z coordinate of all points in the point cloud, min(z i ) is the minimum value of the z coordinate of all points in the point cloud;

[0076] The formula for calculating body width is:

[0077]

[0078] Where W is the body width, M is the number of feature points in the point cloud used to calculate the body width, and w j is the vertical distance from the jth feature point in the point cloud to the body length line. As the rabbit grows, the values of L, H, and W usually increase, reflecting the growth and development of the rabbit;

[0079] The weight measurement module uses the measured body size data and corrected posture of the meat rabbit, combined with a weight prediction model built using machine learning technology, to determine the weight of the meat rabbit according to its body characteristics;

[0080] The data calibration and verification module calculates the weight and body size matching coefficient by comparing it with the set standard value, calibrates and verifies the measurement results, and analyzes the degree of deviation between the automated measurement data and the standard value;

[0081] The user interaction module includes a display screen and buttons, which are used to display captured images, measurement results and user-set related human-computer interaction parameters.

[0082] The second embodiment, based on the first embodiment, see Figure 3 As shown, in the weight measurement module, the process of measuring the weight of the meat rabbit includes:

[0083] Receive the body size data of the meat rabbit output from the body size measurement module and the posture image processed by the posture correction module, and extract the body shape features related to weight from the corrected posture image, including body size, proportion, and shape. Among them, the body size includes body length, body width, and body height, which are obtained by measuring the outline of the meat rabbit in the image or the distance between key points. The proportion includes the head-to-body ratio and the ratio of the limbs to the body, which is obtained by calculating the ratio of related dimensions. The shape includes the outline shape of the body and the degree of curvature of the limbs, which are obtained through image processing technology such as edge detection and contour extraction. Pre-train the weight prediction model and deploy the weight prediction model in the production environment. Input the extracted body shape features into the pre-trained In the weight prediction model, the predicted weight range is output. A large amount of body feature data and corresponding weight data from experimental measurements, medical records, and health monitoring are obtained, covering meat rabbits of different body shapes, ages, and gender factors. The data is cleaned, duplicates, missing or outliers are removed, and normalized or standardized. The data is divided into training set and validation set. The training set data is combined with the neural network model to train the weight prediction model. The model parameters are adjusted through the optimization algorithm. During the training process, the model loss function value and accuracy index are monitored to evaluate the training effect of the model. The trained model is evaluated using the validation set data to calculate the prediction error, mean square error, root mean square error, R 2 Score index: Based on the evaluation results, determine whether the prediction performance of the model meets the requirements. Based on the evaluation results, optimize the model, calculate the weight measurement index based on the predicted weight range and the body characteristics of the meat rabbit, and preset the weight measurement standard of the meat rabbit. Compare it with the calculated weight measurement index to evaluate the growth and development status and health level of the meat rabbit and obtain the weight measurement evaluation results;

[0084] Furthermore, the calculation formula of the body mass index is:

[0085]

[0086] Where I is the body weight index, W is the body width, and W baseW is the reference body width of meat rabbit based on age and body size, std is the standard deviation of body width, which is used to adjust the sensitivity of the exponential function, L is the body length, L base is the reference length of meat rabbit based on age and body shape, H is the height, H base is the benchmark height of a meat rabbit based on its age and body shape. When the actual body width, length, and height of a meat rabbit are close to or equal to the benchmark values, I is close to 1, indicating that the meat rabbit's weight is within a healthy range. When the actual body width of a meat rabbit is lower than the benchmark width, or the length or height is lower than the benchmark value, the I value will decrease, indicating that the meat rabbit may be thin.

[0087] In the data calibration and verification module, the analysis process of automatically determining the degree of deviation between the data and the standard value includes:

[0088] According to the breeding standards, feeding management requirements and industry standards of meat rabbits, standard values of weight and body size (body length, body height, body width) are set, and the weight and body size data output by the automated measurement system are collected to ensure the uniformity of the data format, including date, time, individual identification, weight, body length, body height, body width fields, to facilitate subsequent processing and analysis. The automated measurement data are compared with the set standard values, and the difference between each measurement value (weight, body length, body height, body width) and the standard value is checked one by one. The deviation of each measurement value, that is, the difference between the measurement value and the standard value, is calculated. According to the size of the deviation, the weight and body size matching coefficient is calculated. Analyze the degree of proximity between the automated measurement data and the standard value, analyze the degree of deviation between the automated measurement data and the standard value, and determine whether the data is within the acceptable error range. If the deviation is large, further investigation is required to find the cause, including measurement equipment failure, data input error, or feeding management problems. Based on the analysis results of the deviation degree and weight and body size matching coefficient, calibrate and adjust the automated measurement system, output the calibration and verification results, and generate a measurement report. The report includes detailed data comparison, weight and body size matching coefficient, and calibrated data. Then, the calibration verification results and measurement report are fed back to relevant personnel.

[0089] Furthermore, the calculation formula for the weight and body size matching coefficient is:

[0090]

[0091] Among them, P is the weight and size matching coefficient, X k is the kth data point (weight, length, height, width) measured automatically, Y k is the kth standard value corresponding to the automated determination, n is the number of determination values, |X k -Y k| is the absolute difference between the kth measured value and its standard value. The value of P ranges from 0 to 1. The closer the value is to 1, the closer the automated measurement data is to the standard value and the higher the matching degree is. The closer the value of P is to 0, the greater the deviation between the automated measurement data and the standard value is and the lower the matching degree is.

[0092] Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without making creative efforts should fall within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described and explained in the present invention shall be implemented in accordance with conventional means in the field unless otherwise specified or limited.

Claims

1. A machine vision-based automated measurement system for rabbit weight and body size, comprising an automated measurement platform, characterized in that: The automated measurement platform is communicatively connected to an image capture module, a rabbit feature extraction module, a posture transformation module, a body size measurement module, a weight measurement module, a data calibration and verification module, and a user interaction module, wherein electrical signals are connected between the modules; The image capture module is used to capture image data of the meat rabbit from different angles and pre-process the image data; The rabbit feature extraction module extracts the body features of the rabbit from the image data of the multi-view image set using edge detection and texture analysis technology, and matches feature point pairs to form a body feature sequence; The posture transformation module transforms different postures of the meat rabbit based on the feature data of the body feature sequence, and adjusts the posture of the meat rabbit to a standard posture; The body size measurement module measures the body size data of the meat rabbit by reconstructing a 3D point cloud based on the matched feature point pairs in the image data in combination with the 3D reconstruction technology; The weight measurement module measures the weight of the rabbit based on the measured body size data and the corrected posture, combined with a weight prediction model constructed using machine learning technology, according to the body characteristics of the rabbit. In the weight measurement module, the process of measuring the weight of the rabbit includes: receiving the body size data of the meat rabbit output from the body size measurement module and the posture image processed by the posture correction module, and extracting body shape features related to weight from the corrected posture image, including body size, proportion, and shape, wherein the body size includes body length, body width, and body height; Pre-train a weight prediction model and deploy it in production. Input the extracted body shape features into the pre-trained weight prediction model to output the predicted weight range. Calculate the weight measurement index based on the predicted weight range and the body characteristics of the meat rabbit, preset the weight measurement standard for the meat rabbit, compare it with the calculated weight measurement index, evaluate the growth and development status and health level of the meat rabbit, and obtain the weight measurement evaluation result; The calculation formula of the body mass index is: Where I is the body weight index, W is the body width, and W base W is the reference body width of meat rabbit based on age and body size, std is the standard deviation of body width, L is body length, L base is the reference length of meat rabbit based on age and body shape, H is the height, H base The standard height of meat rabbits based on age and size; The data calibration and verification module calculates the weight and body measurement matching coefficient by comparing with the set standard value, and analyzes the degree of deviation between the automated measurement data and the standard value; The user interaction module includes a display screen and buttons, which are used to display captured images, measurement results and human-computer interaction parameters related to user settings.

2. The machine vision-based automated rabbit weight and body measurement system according to claim 1, characterized in that: In the image capture module, the process of capturing the rabbit image data includes: Configure cameras with high-resolution and wide-angle lenses at multiple angles, initialize image capture parameters such as resolution, frame rate, and exposure, and preset the capture angle and position of the rabbit image; The meat rabbit is placed in a capture area, and the configured camera is adjusted according to a preset capture angle and position to capture image data of the meat rabbit from multiple perspectives, where the captured images cover the entire body of the meat rabbit, including the side, front, and back; Time synchronization of images captured by different cameras and pre-processing of captured image data, including denoising, contrast enhancement, and brightness adjustment; The pre-processed image data are classified and sorted according to different capture angles, and the classified image data are integrated to generate a multi-view image set containing multiple perspectives of meat rabbits.

3. The machine vision-based automated rabbit weight and body measurement system according to claim 2, characterized in that: In the meat rabbit feature extraction module, the process of obtaining the body feature sequence includes: receiving image data from a multi-view image set, including full-body images of the side, front, and back of a meat rabbit; Apply the Sobel operator edge detection technology to the image data, calculate the gradient amplitude of the image to highlight the edge, and use the results of the Sobel operator to identify the edge features of the rabbit's body contour and joint position in the image; The texture features in the image are analyzed using the texture analysis technology of gray-level co-occurrence matrix. The texture feature parameters of contrast, energy, entropy and correlation are extracted from the gray-level co-occurrence matrix to analyze the skin texture and hair texture features of meat rabbits. Match the feature points extracted from multi-view images, generate a feature descriptor for each feature point, and use the SIFT feature point matching algorithm to match the feature point pairs in images from different viewpoints to determine the feature point pairs of the rabbit's body contour and joint positions; The matched feature point pairs are connected in series to form a body feature sequence, which includes the contour line of the body outline and the key points of the joint positions.

4. The machine vision-based automated rabbit weight and body measurement system according to claim 3, characterized in that: In the posture transformation module, the process of adjusting the rabbit posture includes: The body feature sequence outputted from the meat rabbit body feature extraction module is received, a standard posture of the meat rabbit is preset, and computer vision technology is applied to estimate the posture and position of the meat rabbit using key point information in the body feature sequence, wherein the standard posture of the meat rabbit is a standing posture with four limbs; Applying image transformation techniques such as affine transformation and perspective transformation to the original image, performing preliminary posture adjustment on the image of the meat rabbit, and preliminarily adjusting the posture of the meat rabbit to a state close to the standard posture; Based on the image transformation, the geometric correction technology is applied to calculate the perspective transformation matrix, which is then applied to the image to further adjust the rabbit's posture and correct the geometric distortion caused by the difference in viewing angle to obtain the rabbit image in a standard posture. Perform posture transformation and use the calculated transformation matrix to adjust the rabbit's posture to the preset standard posture. Use feature point matching technology to check whether the transformed feature points match the feature points of the standard posture and verify whether the transformed posture meets the requirements of the standard posture. If not, re-estimate the posture and output the rabbit image after adjusting to the standard posture.

5. The machine vision-based automated rabbit weight and body measurement system according to claim 4, characterized in that: In the body size measurement module, the measurement process of the rabbit body size data includes: Extract rabbit images captured from different perspectives, apply the SIFT algorithm to each image, extract the feature points and their descriptors in the image, and use the distance metric between the descriptors to match the feature point pairs in different images; The camera's intrinsic and extrinsic parameters are calculated using the known 3D world coordinate points and the corresponding image coordinate points. The camera's pose, i.e., its position and attitude in space, is estimated using the PnP optimization algorithm using the matched feature point pairs and the known camera intrinsic parameters. Based on the matched feature point pairs and the camera's position, the coordinates of the points in 3D space are calculated using the principle of triangulation, thereby reconstructing a sparse 3D point cloud. The reconstructed 3D point cloud is then filtered and segmented into different parts based on its geometric features. The part of the point cloud representing the meat rabbit is extracted from the segmented point cloud. In the extracted meat rabbit point cloud data, the geometric features of body length, body height, and body width are identified. Body length refers to the straight-line distance from the head to the tail, body height refers to the distance from the ground to the highest point, and body width refers to the horizontal distance at the widest point. Based on the identified geometric features, the corresponding dimensions are measured in the point cloud data and the body size data of the meat rabbit is calculated.

6. The machine vision-based automated rabbit weight and body measurement system according to claim 5, characterized in that: The calculation formula of the body length is: Among them, L is the body length, N is the number of feature points in the point cloud used to calculate the body length, and p i is the coordinate of the i-th feature point in the point cloud, q i For p i The corresponding points represent the corresponding points from the head to the tail, d(p i ,q i ) is point p i and q i The Euclidean distance between i The coordinates are q i The coordinates are The calculation formula of the body height is: H=max(z i )-min(z i ); Where H is the height, z i is the z coordinate value of all points in the point cloud, that is, the vertical height of the ground, max(z i ) is the maximum value of the z coordinate of all points in the point cloud, min(z i ) is the minimum value of the z coordinate of all points in the point cloud; The calculation formula of the body width is: Where W is the body width, M is the number of feature points in the point cloud used to calculate the body width, and w j is the vertical distance from the jth feature point in the point cloud to the body length line.

7. The machine vision-based automated rabbit weight and body measurement system according to claim 6, characterized in that: In the data calibration and verification module, the analysis process of automatically determining the degree of deviation between the data and the standard value includes: According to the breeding standards, feeding and management requirements and industry standards of meat rabbits, standard values for weight and body size are set, and weight and body size data output by the automated measurement system are collected, including date, time, individual identification, weight, length, height and width fields; Compare the automated measurement data with the set standard value, check the difference between each measured value and the standard value one by one, and calculate the deviation of each measured value, that is, the difference between the measured value and the standard value; According to the deviation, calculate the weight and body size matching coefficient, analyze the closeness of the automated measurement data to the standard value, analyze the deviation of the automated measurement data from the standard value, and determine whether the data is within the acceptable error range; Based on the analysis results of the degree of deviation and the weight and body size matching coefficient, the automated measurement system is calibrated and adjusted, and the calibration and verification results are output, and a measurement report is generated. The calibration verification results and measurement report are then fed back to relevant personnel.

8. The machine vision-based automated rabbit weight and size measurement system according to claim 7, characterized in that: The calculation formula of the weight and body size matching coefficient is: Among them, P is the weight and size matching coefficient, X k is the kth data point measured automatically, Y k is the kth standard value corresponding to the automated determination, n is the number of determination values, |X k -Y k | is the absolute difference between the kth measured value and its standard value.

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