CN113643401A - Live pig carcass segmentation method and system based on machine learning - Google Patents
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Abstract
The invention provides a live pig carcass segmentation method based on machine learning, which relates to the field of live pig segmentation, wherein after relevant data of bones, muscles and fat are obtained by scanning live pig carcasses by utilizing a tomography technology, segmentation paths are designed manually, and a model capable of automatically generating mechanical arm segmentation paths according to characteristic parameters of the bones, the muscles and the fat is obtained by performing machine learning through a neural network algorithm; the trained model is utilized in the live pig carcass splitting process, a mechanical arm splitting path is generated according to different live pig carcass parameters, and the mechanical arm splits the live pig carcass, so that the automation of the live pig carcass splitting is realized, the splitting efficiency is improved, and the splitting cost is reduced.
Description
Technical Field
The invention relates to a live pig carcass segmentation method, in particular to a live pig carcass segmentation method and a live pig carcass segmentation system based on machine learning.
Background
After a pig is slaughtered, the pig carcass needs to be cut, and the automatic cutting and manual cutting are generally combined in the prior art. At present, the automation degree of the division of the live pig carcass can reach 30 percent at most. The live pig carcass is manually cut, the cutting process is time-consuming and labor-consuming, standardized management cannot be achieved, and the efficiency is low; and the manual segmentation of the live pig carcasses needs to be carried out in a low-temperature environment, the number of workers willing to carry out the work is gradually reduced, and the manual wages of the work are increased year by year.
Disclosure of Invention
In view of the above technical problems, the present invention provides a live pig carcass segmentation method and system based on machine learning, which can realize automatic segmentation of live pig carcasses, reduce human involvement, improve production efficiency, and reduce production cost.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
the invention provides a machine learning-based live pig carcass segmentation method, which comprises the following steps:
1) carrying out tomography on the live pig carcass;
2) carrying out digital processing and three-dimensional modeling on the image of the tomography, and distinguishing areas such as bones, fat, muscles and the like in the three-dimensional modeling process;
3) obtaining characteristic parameters of bones, muscles and fat according to the digitally processed picture and storing the characteristic parameters into a database;
4) designing a mechanical arm segmentation path according to the bone, fat and muscle regions distinguished from the three-dimensional model, and storing mechanical arm segmentation path parameters into a database;
5) repeating steps 1) -5), and filling the database;
6) using data stored in a database, taking characteristic parameters of bones, muscles and fat as input values, taking a mechanical arm segmentation path as an output value, and performing machine learning through a neural network algorithm;
7) and performing machine learning to obtain a model capable of automatically generating mechanical arm segmentation path parameters according to the characteristic parameters of bones, muscles and fat.
The invention provides a machine learning-based live pig carcass segmentation method, preferably, the step 2) is specifically as follows: and carrying out tomography scanning on the split live pig carcass by utilizing an X-ray tomography scanning technology.
The invention provides a machine learning-based live pig carcass segmentation method, preferably, the step 3) is specifically as follows: carrying out density coloring on different tissues in a tomography image according to different densities; and carrying out digital processing and three-dimensional modeling on the image of the tomography, and distinguishing areas such as bones, fat, muscles and the like according to different density coloring in the modeling process.
The invention provides a machine learning-based live pig carcass segmentation method, preferably, the step 4) is specifically as follows: respectively calculating characteristic parameters of bones in a plurality of regions by using a reference intensity method according to the digitalized picture, the position coordinates of the bones and the inverse calculation proportion; calculating characteristic parameters of muscle and fat by the same method; and storing the characteristic parameters of the bones, the muscles and the fat into a database.
The invention provides a machine learning-based live pig carcass segmentation method, preferably, the step of performing tomography scanning on the live pig carcass is as follows: carrying out tomography scanning on the live pig carcass.
The invention provides a machine learning-based live pig carcass segmentation system, which comprises:
respectively training and learning different types of live pig carcasses by using the live pig carcass segmentation method based on machine learning to obtain mechanical arm segmentation paths of different types of live pigs under different body types;
collecting mechanical arm segmentation paths of different types of live pigs under different body types into a database;
the type and body type of the live pig are used as input values, and the mechanical arm segmentation path can be obtained.
The machine learning-based live pig carcass splitting system preferably comprises an input device; the input equipment is used for obtaining the types and body types of the live pigs.
The technical scheme has the following advantages or beneficial effects:
according to the method, after relevant data of bones, muscles and fat are obtained after a live pig carcass is scanned by utilizing a tomography technology, a segmentation path is designed manually, and a model capable of automatically generating a mechanical arm segmentation path according to characteristic parameters of the bones, the muscles and the fat is obtained after machine learning is carried out through a neural network algorithm; the trained model is utilized in the live pig carcass splitting process, a mechanical arm splitting path is generated according to different live pig carcass parameters, and the mechanical arm splits the live pig carcass, so that the automation of the live pig carcass splitting is realized, the splitting efficiency is improved, and the splitting cost is reduced.
Drawings
The invention and its features, aspects and advantages will become more apparent from reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings. Like reference symbols in the various drawings indicate like elements. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention.
Fig. 1 is a schematic flow chart of a live pig carcass splitting method based on machine learning according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the invention without making creative efforts, belong to the protection scope of the invention.
Example 1:
as shown in fig. 1, the method for splitting a live pig carcass based on machine learning provided by the invention comprises the following steps:
1) splitting a live pig carcass into halves;
2) carrying out tomography scanning on the split live pig carcass by utilizing an X-ray tomography scanning technology;
3) carrying out density coloring on different tissues in a tomography image according to different densities; carrying out digital processing and three-dimensional modeling on the image of the tomography, and distinguishing areas such as bones, fat, muscles and the like according to different density coloring in the modeling process;
4) respectively calculating characteristic parameters of bones in a plurality of regions by using a reference intensity method according to the digitalized picture, the position coordinates of the bones and the inverse calculation proportion; calculating characteristic parameters of muscle and fat by the same method; storing the characteristic parameters of the bones, the muscles and the fat into a database;
5) designing a mechanical arm segmentation path according to the bone, fat and muscle regions distinguished from the three-dimensional model, and storing mechanical arm segmentation path parameters into a database;
6) repeating steps 1) -5), and filling the database;
7) using data stored in a database, taking characteristic parameters of bones, muscles and fat as input values, taking a mechanical arm segmentation path as an output value, and performing machine learning through a neural network algorithm;
8) and performing machine learning to obtain a model capable of automatically generating mechanical arm segmentation path parameters according to the characteristic parameters of bones, muscles and fat.
According to the method, after relevant data of bones, muscles and fat are obtained after a live pig carcass is scanned by utilizing a tomography technology, a segmentation path is designed manually, and a model capable of automatically generating a mechanical arm segmentation path according to characteristic parameters of the bones, the muscles and the fat is obtained after machine learning is carried out through a neural network algorithm; the trained model is utilized in the live pig carcass splitting process, a mechanical arm splitting path is generated according to different live pig carcass parameters, and the mechanical arm splits the live pig carcass, so that the automation of the live pig carcass splitting is realized, the splitting efficiency is improved, and the splitting cost is reduced.
For the same pig type, the body type of the pig carcass is almost the same and the dividing path is also the same under the same body type, so in this embodiment, a machine learning based pig carcass dividing system is provided, which includes: the method for segmenting the live pig carcasses based on machine learning in the embodiment is used for respectively training and learning different types of live pig carcasses to obtain mechanical arm segmentation paths of different types of live pigs under different body types;
collecting mechanical arm segmentation paths of different types of live pigs under different body types into a database;
the type and body type of the live pig are used as input values, and the mechanical arm segmentation path can be obtained.
Meanwhile, the live pig carcass splitting system in the embodiment further comprises an input device; the input equipment is used for obtaining the types and body types of the live pigs. The input device in this embodiment includes a keyboard input device, but is not limited to the keyboard input device, and may also be a device capable of obtaining live pig species and live pig carcasses, such as a laser ranging instrument connected to the system.
When a user uses the live pig carcass splitting system provided by the embodiment, the scanning device does not need to be installed on the site for splitting operation, the corresponding splitting path can be obtained only by inputting the type and body type of the live pig through the input device, and the mechanical arm splits the live pig carcass according to the obtained splitting path, so that the problems existing in the process of installing the scanning device on the splitting operation site (such as complex operation site environment, easy damage of the scanning device, radiation of the scanning device, unfavorable health of workers performing operation and the like) are avoided.
The above description is only for the preferred embodiment of the present invention and is not intended to limit the scope of the present invention, and all equivalent structural changes made by using the contents of the present specification and the drawings, or any other related technical fields, are included in the scope of the present invention.
Claims (7)
1. A machine learning-based live pig carcass segmentation method is characterized by comprising the following steps:
1) carrying out tomography on the live pig carcass;
2) carrying out digital processing and three-dimensional modeling on the image of the tomography, and distinguishing areas such as bones, fat, muscles and the like in the three-dimensional modeling process;
3) obtaining characteristic parameters of bones, muscles and fat according to the digitally processed picture and storing the characteristic parameters into a database;
4) designing a mechanical arm segmentation path according to the bone, fat and muscle regions distinguished from the three-dimensional model, and storing mechanical arm segmentation path parameters into a database;
5) repeating steps 1) -5), and filling the database;
6) using data stored in a database, taking characteristic parameters of bones, muscles and fat as input values, taking a mechanical arm segmentation path as an output value, and performing machine learning through a neural network algorithm;
7) and performing machine learning to obtain a model capable of automatically generating mechanical arm segmentation path parameters according to the characteristic parameters of bones, muscles and fat.
2. The machine learning-based pig carcass splitting method according to claim 1, wherein the step 2) is specifically: and carrying out tomography scanning on the split live pig carcass by utilizing an X-ray tomography scanning technology.
3. The machine learning-based pig carcass splitting method according to claim 2, wherein the step 3) is specifically: carrying out density coloring on different tissues in a tomography image according to different densities; and carrying out digital processing and three-dimensional modeling on the image of the tomography, and distinguishing areas such as bones, fat, muscles and the like according to different density coloring in the modeling process.
4. The machine learning-based pig carcass splitting method according to claim 1, wherein the step 4) is specifically: respectively calculating characteristic parameters of bones in a plurality of regions by using a reference intensity method according to the digitalized picture, the position coordinates of the bones and the inverse calculation proportion; calculating characteristic parameters of muscle and fat by the same method; and storing the characteristic parameters of the bones, the muscles and the fat into a database.
5. The machine learning-based pig carcass splitting method according to claim 1, wherein the step of "tomoscanning the pig carcass" is specifically: carrying out tomography scanning on the live pig carcass.
6. A machine learning based live pig carcass splitting system, comprising:
respectively training and learning different types of live pig carcasses by using the live pig carcass segmentation method based on machine learning of any one of claims 1-5 to obtain mechanical arm segmentation paths of different types of live pigs under different body types;
collecting mechanical arm segmentation paths of different types of live pigs under different body types into a database;
the type and body type of the live pig are used as input values, and the mechanical arm segmentation path can be obtained.
7. The machine learning-based live pig carcass splitting system according to claim 6, comprising an input device; the input equipment is used for obtaining the types and body types of the live pigs.
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| CN116502468A (en) * | 2023-06-21 | 2023-07-28 | 查维斯机械制造(北京)有限公司 | Method and system for controlling cattle carcass segmentation robot based on machine vision |
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