CN111871864A - Sorting device and method - Google Patents

Sorting device and method Download PDF

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Publication number
CN111871864A
CN111871864A CN202010783312.XA CN202010783312A CN111871864A CN 111871864 A CN111871864 A CN 111871864A CN 202010783312 A CN202010783312 A CN 202010783312A CN 111871864 A CN111871864 A CN 111871864A
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China
Prior art keywords
preset
image
sorting
module
time
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Pending
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CN202010783312.XA
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Chinese (zh)
Inventor
张占军
李斌
崔云鹏
樊红杰
秦延龙
贺成柱
吴晓彤
牛靖乾
程涛
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Liaoning Ruihua Industrial Group High And New Technology Co ltd
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Liaoning Ruihua Industrial Group High And New Technology Co ltd
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Priority to CN202010783312.XA priority Critical patent/CN111871864A/en
Publication of CN111871864A publication Critical patent/CN111871864A/en
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34—Sorting according to other particular properties
    • B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
    • B07C5/3422—Sorting according to other particular properties according to optical properties, e.g. colour using video scanning devices, e.g. TV-cameras
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/36—Sorting apparatus characterised by the means used for distribution
    • B07C5/361—Processing or control devices therefor, e.g. escort memory
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/36—Sorting apparatus characterised by the means used for distribution
    • B07C5/361—Processing or control devices therefor, e.g. escort memory
    • B07C5/362—Separating or distributor mechanisms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
    • B07C2501/0063—Using robots

Abstract

The present disclosure relates to a sorting device and method, the device includes an image acquisition module, an identification control module and a sorting module, wherein: the image acquisition module is connected with the identification control module and used for acquiring the image of the preset area and sending the image to the identification control module; the identification control module is connected with the sorting module and is used for: the image input image classification positioning model is identified, the position of an object in the image and the class of the object are determined, when the class of the object comprises a preset class, control information is generated according to the position of the preset class object in the image and preset sorting parameters, and the control information is sent to a sorting module; and the sorting module is used for executing the sorting action aiming at the preset class object according to the control information. The sorting device of the embodiment of the disclosure can improve the recognition efficiency and the detection precision, and reduce the cost of recognition and sorting.

Description

Sorting device and method
Technical Field
The disclosure relates to the technical field of automatic control, in particular to a sorting device and a sorting method.
Background
Coal resources are widely applied in industry and life, and almost all the coal resources are not used for heating in a small house as compared with thermal power generation. However, a large amount of coal gangue can be mixed in the coal mining process, the combustion utilization rate of the coal gangue is low, and the industrial production can be seriously influenced and even the environment can be polluted by mixing the coal gangue in the industrial coal, so that the identification and sorting of the coal gangue from the coal is an important link of the coal production.
At present, coal gangue is identified and sorted from coal mainly in a manual mode, but the environment of manual operation is very severe, and a large amount of dust seriously influences the health of sorting personnel; secondly, the manual operation has low efficiency, high cost, high labor intensity, great inaccuracy and artificial subjectivity.
Disclosure of Invention
In view of this, the present disclosure provides a sorting device, including an image acquisition module, an identification control module and a sorting module, wherein:
the image acquisition module is connected with the identification control module and used for acquiring an image of a preset area and sending the image to the identification control module;
the identification control module is connected with the sorting module and is used for:
the image input image classification positioning model is identified, the position of an object in the image and the class of the object are determined,
when the object type comprises a preset type, generating control information according to the position of the preset type object in the image and preset sorting parameters, and sending the control information to the sorting module;
and the sorting module is used for executing sorting action aiming at the preset class object according to the control information.
In a possible implementation, the sorting module comprises a conveying unit for conveying the objects and an execution unit for executing a sorting action for the objects of the preset category.
In a possible implementation manner, the preset sorting parameters include a conveying speed of the conveying unit, a moving speed of the execution unit, and a first distance between the execution unit and the conveying unit;
the identification control module generates control information according to the position of the preset category object in the image and preset sorting parameters, and the control information comprises the following steps:
determining a first time according to a position of the preset category object in the image, a size of the preset area, a second distance between the execution unit and the preset area, and a transmission speed of the transmission unit,
wherein the preset region comprises a region corresponding to the size of the image in the transmission unit, and the first time comprises a time from the image acquisition module starting to acquire the image of the preset region to the preset target position of the preset class object;
determining a second time according to the first distance, the position of the preset category object in the image and the moving speed of the execution unit, wherein the second time comprises the time from the execution unit starting to move to the time when the execution unit contacts the preset category object;
and generating the control information according to the first time and the second time.
In one possible implementation, the apparatus further includes a model training module, and the model training module is configured to:
and training the image classification positioning model according to a preset training image so that the image classification positioning model identifies the position of an object in the training image and the class of the object.
In one possible implementation, the image classification and positioning model includes a neural network model constructed based on a YOLOV3 algorithm or a support vector machine algorithm.
According to an aspect of the present disclosure, there is provided a sorting method applied to a sorting apparatus as described above, the apparatus including an image acquisition module and a sorting module, the method including:
acquiring an image of a preset area acquired by an image acquisition module;
the image input image classification positioning model is identified, the position of an object in the image and the class of the object are determined,
when the object type comprises a preset type, generating control information according to the position of the preset type object in the image and preset sorting parameters, so that the sorting module executes a sorting action aiming at the preset type object.
In a possible implementation, the sorting module comprises a conveying unit for conveying the objects and an execution unit for executing a sorting action for the objects of the preset category.
In a possible implementation manner, the preset sorting parameters include a conveying speed of the conveying unit, a moving speed of the execution unit, and a first distance between the execution unit and the conveying unit;
the generating of the control information according to the position of the preset category object in the image and the preset sorting parameter includes:
determining a first time according to a position of the preset category object in the image, a size of the preset area, a second distance between the execution unit and the preset area, and a transmission speed of the transmission unit,
the preset region comprises a region corresponding to the size of the image in the transmission unit, and the first time comprises the time from the image acquisition module acquiring the image of the preset region to the preset class object reaching a preset target position;
determining a second time according to the first distance, the position of the preset category object in the image and the moving speed of the execution unit, wherein the second time comprises the time from the movement start of the execution unit to the contact of the execution unit with the preset category object;
and generating the control information according to the first time and the second time.
In one possible implementation, the method further includes:
and training the image classification positioning model according to a preset training image so that the image classification positioning model identifies the position of an object in the training image and the class of the object.
In one possible implementation, the image classification and positioning model includes a neural network model constructed based on a YOLOV3 algorithm or a support vector machine algorithm.
The sorting device of the embodiment of the disclosure can determine the position of an object and the category of the object in an acquired image of a preset area, when the category of the object comprises the preset category, control information is generated according to the position of the preset category object in the image and preset sorting parameters, and a sorting module executes sorting action aiming at the preset category object according to the control information.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a block diagram of a sorting apparatus according to an embodiment of the present disclosure.
Fig. 2 shows a schematic structural diagram of a sorting apparatus according to an embodiment of the present disclosure.
Fig. 3 shows a flow diagram of a sorting method according to an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
In practical application, more coal gangue is mixed in the coal mining process, and the coal gangue has low utilization value for production and life, so the coal gangue needs to be identified and sorted from the coal. Compared with coal, the coal gangue is relatively small in quantity, so that the coal gangue can be operated only, the workload of identification and sorting is reduced, and the efficiency is improved.
Fig. 1 shows a block diagram of a sorting apparatus according to an embodiment of the present disclosure. As shown in fig. 1, the apparatus includes an image acquisition module 11, a recognition control module 12, and a sorting module 13, wherein:
the image acquisition module 11 is connected with the recognition control module 12, and is configured to acquire an image of a preset area and send the image to the recognition control module 12;
the identification control module 12 is connected to the sorting module 13, and is configured to:
the image input image classification positioning model is identified, the position of an object in the image and the class of the object are determined,
when the object type includes a preset type, generating control information according to the position of the preset type object in the image and a preset sorting parameter, and sending the control information to the sorting module 13;
the sorting module 13 is configured to execute a sorting action for the preset category object according to the control information.
Compared with the existing manual identification sorting method, the sorting device provided by the embodiment of the disclosure has the advantages that the identification efficiency and the detection precision are improved, and the identification sorting cost can be reduced.
In a possible implementation manner, the image acquisition module 11 may acquire an image, and the identification control module 12 may perform corresponding processing on the image to identify gangue from coal, thereby improving the identification efficiency. Illustratively, the image capturing module 11 may include a camera, a video camera, and the like for capturing images, and the type of the image capturing module 11 is not limited in the embodiments of the present disclosure.
In one possible implementation, the recognition control module 12 may recognize the image input image classification positioning model and determine the position of the object in the image and the category of the object.
In the embodiment of the present disclosure, the categories of the object may include at least two categories: coal and coal gangue, wherein the coal gangue can be a preset class object, and the class of the object is not limited in the embodiment of the disclosure. Illustratively, the recognition control module 12 may include a computer, a mobile device, a tablet computer, or the like for image processing, and the embodiment of the present disclosure does not limit the type of the recognition control module 12.
In a possible implementation, the sorting module 13 may comprise a conveying unit for conveying the objects and an execution unit for executing a sorting action for objects of a preset category. For example, the conveying unit may include a conveyor belt or other device for conveying the object, and the execution unit may include a robot arm or other device for executing the sorting action.
In one possible implementation, the preset area includes an area corresponding to a size of the image in the transfer unit. For example, different types of image capturing devices may correspond to different image sizes, taking the image size as w × h as an example, where w may represent the length of the image, h may represent the width of the image, and the preset region may be a region on the transfer unit that includes the transfer object and has the same size as the image.
In one possible implementation, the preset sorting parameters include a conveying speed of the conveying unit, a moving speed of the execution unit, and a first distance between the execution unit and the conveying unit. The first distance between the execution unit and the transmission unit is the vertical distance between the execution unit and the transmission unit.
In one possible implementation, the apparatus further includes a model training module, and the model training module is configured to:
and training the image classification positioning model according to a preset training image so that the image classification positioning model identifies the position of an object in the training image and the class of the object.
For example, the image classification and positioning model may be trained according to a preset training image until a value of a loss function of the image classification and positioning model is lower than a preset threshold, so that the image classification and positioning model identifies a position of an object in the training image and a class of the object. The specific value of the preset threshold value can be set according to the user requirement, and the specific value of the preset threshold value is not limited in the embodiment of the disclosure.
In one possible implementation, the image classification and positioning model includes a neural network model constructed based on a YOLOV3 algorithm or a support vector machine algorithm.
Illustratively, the YOLOV3 algorithm is capable of generating candidate boxes in an image through a single network structure and predicting the class and location of objects in the image based on the candidate boxes. An SVM (Support Vector Machine) algorithm is capable of distinguishing the category of an object based on the color of the object in an image by recognizing the color of the object in the image, and determining the position of the object based on the color position of the object in the image. The embodiment of the present disclosure does not limit the type of the image classification and positioning model.
In a possible implementation manner, the generating, by the recognition control module 12, control information according to the position of the preset category object in the image and a preset sorting parameter includes:
determining a first time according to the position of the preset category object in the image, the size of the preset area, a second distance between the execution unit and the preset area and the transmission speed of the transmission unit;
determining a second time according to the first distance, the position of the preset class object in the image and the moving speed of the execution unit;
and generating the control information according to the first time and the second time.
Fig. 2 shows a schematic structural diagram of a sorting apparatus according to an embodiment of the present disclosure. Illustratively, the coordinate axis shown in fig. 2 is constructed, the position of the preset category object in the image is (x, y), the size of the preset area is w × h, wherein w represents the length of the preset area, h represents the width of the preset area, the second distance between the execution unit and the preset area is s, the conveying speed of the conveying unit is v1The moving speed of the execution unit is v2The example that the first distance between the execution unit and the transmission unit is L illustrates a process in which the recognition control module generates the control information according to the position of the preset category object in the image and the preset sorting parameter:
the first time may be determined according to a method of the following formula (1) according to a position of the preset category object in the image, a size of the preset area, a second distance between the execution unit and the preset area, and a conveying speed of the conveying unit:
formula (1):
t1＝(s+w-x)/v1
wherein, t1Representing a first time, s representing a second distance, w representing a length of a preset region, x representing an abscissa position of a preset class object in the image, v1Indicating the conveying speed of the conveying unit.
In a possible implementation manner, the first time includes a time from when the image acquisition module starts to acquire the image of the preset area to when the preset class object reaches the preset target position. Illustratively, the preset target position may include a position in the transfer unit perpendicular to the execution unit.
The second time may be determined according to the first distance, the position of the preset category object in the image, and the moving speed of the execution unit in the following formula (2):
formula (2):
t2＝(L+y)/v2
wherein, t2Representing a second time, L representing a first distance, y representing a vertical coordinate position of the preset class object in the image, v2Indicating the speed of movement of the execution unit.
In a possible implementation manner, the second time includes a time from when the execution unit starts to move to when the execution unit contacts the preset class object.
In one possible implementation, the control information includes a time difference between the first time and the second time, and a position of the preset category object in the transmission unit, wherein the time difference between the first time and the second time may be determined according to the following formula (3):
formula (3):
T＝t1-t2
where T represents a time difference between the first time and the second time.
The position of the preset category object in the transfer unit may include (s + w-x, y).
In one possible implementation, the performing, by the sorting module according to the control information, a sorting action for the preset category object includes:
and the identification control module sends the control information to the sorting module, and after T time, an execution unit of the sorting module sorts the preset class object which is away from the execution unit by (y + L) from the conveying unit.
The sorting device of the embodiment can determine the position of an object and the category of the object in an acquired image of a preset area, when the category of the object comprises the preset category, control information is generated according to the position of the preset category object in the image and preset sorting parameters, and a sorting module executes sorting action aiming at the preset category object according to the control information.
Fig. 3 shows a flow diagram of a sorting method according to an embodiment of the present disclosure. As shown in fig. 3, the sorting method may be applied to the sorting apparatus according to the embodiment of fig. 1, the apparatus includes an image acquisition module and a sorting module, and the method includes:
step S301, acquiring an image of a preset area acquired by an image acquisition module;
step S302, the image input image classification positioning model is identified, the position of the object in the image and the class of the object are determined,
step S303, when the category of the object includes a preset category, generating control information according to a position of the preset category object in the image and a preset sorting parameter.
In one possible implementation, the sorting module can perform a sorting action for the preset category objects according to the control information.
In a possible implementation, the sorting module comprises a conveying unit for conveying the objects and an execution unit for executing a sorting action for the objects of the preset category.
In a possible implementation manner, the preset sorting parameters include a conveying speed of the conveying unit, a moving speed of the execution unit, and a first distance between the execution unit and the conveying unit;
the generating of the control information according to the position of the preset category object in the image and the preset sorting parameter includes:
determining a first time according to a position of the preset category object in the image, a size of the preset area, a second distance between the execution unit and the preset area, and a transmission speed of the transmission unit,
the preset region comprises a region corresponding to the size of the image in the transmission unit, and the first time comprises the time from the image acquisition module acquiring the image of the preset region to the preset class object reaching a preset target position;
determining a second time according to the first distance, the position of the preset category object in the image and the moving speed of the execution unit, wherein the second time comprises the time from the movement start of the execution unit to the contact of the execution unit with the preset category object;
and generating the control information according to the first time and the second time.
In one possible implementation, the method further includes:
and training the image classification positioning model according to a preset training image so that the image classification positioning model identifies the position of an object in the training image and the class of the object.
In one possible implementation, the image classification and positioning model includes a neural network model constructed based on a YOLOV3 algorithm or a support vector machine algorithm.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (10)

1. The utility model provides a sorting device which characterized in that, includes image acquisition module, discernment control module and letter sorting module, wherein:
the image acquisition module is connected with the identification control module and used for acquiring an image of a preset area and sending the image to the identification control module;
the identification control module is connected with the sorting module and is used for:
the image input image classification positioning model is identified, the position of an object in the image and the class of the object are determined,
when the object type comprises a preset type, generating control information according to the position of the preset type object in the image and preset sorting parameters, and sending the control information to the sorting module;
and the sorting module is used for executing sorting action aiming at the preset class object according to the control information.
2. The apparatus according to claim 1, characterized in that the sorting module comprises a conveying unit for conveying the objects and an execution unit for performing a sorting action for the objects of the preset category.
3. The apparatus according to claim 2, wherein the preset sorting parameters comprise a conveying speed of the conveying unit, a moving speed of the execution unit, a first distance of the execution unit from the conveying unit;
the identification control module generates control information according to the position of the preset category object in the image and preset sorting parameters, and the control information comprises the following steps:
determining a first time according to a position of the preset category object in the image, a size of the preset area, a second distance between the execution unit and the preset area, and a transmission speed of the transmission unit,
wherein the preset region comprises a region corresponding to the size of the image in the transmission unit, and the first time comprises a time from the image acquisition module starting to acquire the image of the preset region to the preset target position of the preset class object;
determining a second time according to the first distance, the position of the preset category object in the image and the moving speed of the execution unit, wherein the second time comprises the time from the execution unit starting to move to the time when the execution unit contacts the preset category object;
and generating the control information according to the first time and the second time.
4. The apparatus of claim 1, further comprising a model training module to:
and training the image classification positioning model according to a preset training image so that the image classification positioning model identifies the position of an object in the training image and the class of the object.
5. The apparatus of claim 4, wherein the image classification localization model comprises a neural network model constructed based on a Yolov3 algorithm or a support vector machine algorithm.
6. A sorting method, characterized in that it is applied to a sorting device according to any one of claims 1 to 5, said device comprising an image acquisition module and a sorting module, said method comprising:
acquiring an image of a preset area acquired by an image acquisition module;
the image input image classification positioning model is identified, the position of an object in the image and the class of the object are determined,
when the object type comprises a preset type, generating control information according to the position of the preset type object in the image and preset sorting parameters, so that the sorting module executes a sorting action aiming at the preset type object.
7. The method according to claim 6, characterized in that the sorting module comprises a transfer unit for transferring the objects and an execution unit for performing a sorting action for the objects of the preset category.
8. The method according to claim 7, characterized in that the preset sorting parameters comprise the conveying speed of the conveying unit, the moving speed of the execution unit, the first distance of the execution unit from the conveying unit;
the generating of the control information according to the position of the preset category object in the image and the preset sorting parameter includes:
determining a first time according to a position of the preset category object in the image, a size of the preset area, a second distance between the execution unit and the preset area, and a transmission speed of the transmission unit,
the preset region comprises a region corresponding to the size of the image in the transmission unit, and the first time comprises the time from the image acquisition module acquiring the image of the preset region to the preset class object reaching a preset target position;
determining a second time according to the first distance, the position of the preset category object in the image and the moving speed of the execution unit, wherein the second time comprises the time from the movement start of the execution unit to the contact of the execution unit with the preset category object;
and generating the control information according to the first time and the second time.
9. The method of claim 6, further comprising:
and training the image classification positioning model according to a preset training image so that the image classification positioning model identifies the position of an object in the training image and the class of the object.
10. The method of claim 9, wherein the image classification localization model comprises a neural network model constructed based on a YOLOV3 algorithm or a support vector machine algorithm.
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CN113369173A (en) * 2021-06-29 2021-09-10 上海希翎智能科技有限公司 Full-automatic plastic bottle color sorting equipment
CN114871120A (en) * 2022-05-26 2022-08-09 江苏省徐州医药高等职业学校 Medicine determining and sorting method and device based on image data processing

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Application publication date: 20201103