WO2020151530A1 - Procédé, appareil et dispositif de comptage de vêtements par nombre de pièces - Google Patents

Procédé, appareil et dispositif de comptage de vêtements par nombre de pièces Download PDF

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Publication number
WO2020151530A1
WO2020151530A1 PCT/CN2020/071926 CN2020071926W WO2020151530A1 WO 2020151530 A1 WO2020151530 A1 WO 2020151530A1 CN 2020071926 W CN2020071926 W CN 2020071926W WO 2020151530 A1 WO2020151530 A1 WO 2020151530A1
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Prior art keywords
image data
moving object
clothing
pixel
working area
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PCT/CN2020/071926
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English (en)
Chinese (zh)
Inventor
赵永飞
龙一民
徐博文
吴剑
胡露露
张民英
神克乐
陈新
尹宁
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阿里巴巴集团控股有限公司
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Publication of WO2020151530A1 publication Critical patent/WO2020151530A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06M—COUNTING MECHANISMS; COUNTING OF OBJECTS NOT OTHERWISE PROVIDED FOR
    • G06M7/00—Counting of objects carried by a conveyor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present invention relates to the field of computer technology, in particular to a method, device and equipment for piece counting of clothing.
  • the processing process of the factory mainly includes three processes: cloth cutting, assembly line/complete, and later.
  • garment piece counting operations can be performed.
  • the specific methods for achieving piece counting operations include: using traditional intrusive bar code guns, radio frequency identification RFID technology or directly assigning specialized piece counting personnel to perform manual data entry operations .
  • the embodiment of the present invention provides a garment piece counting method, device and equipment, which are used to reduce the cost of garment piece counting, ensure the efficiency of garment piece counting, and thereby improve the management efficiency of garments in factories.
  • an embodiment of the present invention provides a garment piece counting method, including:
  • an embodiment of the present invention provides a piece counting device for clothing, which includes:
  • the acquisition module is used to acquire at least one frame of image data for quality inspection of clothing
  • Recognition module for recognizing the moving object in the image data and the working area where the moving object is located
  • the piece-counting module is used to perform piece-counting operations on the clothing after quality inspection according to the moving object and the working area.
  • an embodiment of the present invention provides an electronic device, including: a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are processed by the The garment piece counting method in the first aspect mentioned above is realized when the device is executed.
  • an embodiment of the present invention provides a computer storage medium for storing a computer program that enables the computer to implement the garment piece counting method in the first aspect when executed by a computer.
  • the moving object in the image data and the working area where the moving object is located are identified, and the quality-inspected image data is compared according to the moving object and the working area.
  • the garment piece counting operation effectively ensures the piece counting statistics of the garments undergoing quality inspection operations, and reduces the production management cost of the garment piece counting, ensures the efficiency and accuracy of the garment piece counting, and facilitates the production management of the factory.
  • the management efficiency of the factory at the same time, users can obtain production process data and understand the order production progress at any time, and finally realize efficient production and sales collaboration.
  • FIG. 1 is a flowchart of a method for counting pieces of clothing provided by an embodiment of the present invention
  • FIG. 2 is a flowchart of identifying moving objects in the image data provided by an embodiment of the present invention
  • FIG. 3 is a flowchart of identifying a moving object in each frame of the image data according to at least one frame of the image data and the background model image according to an embodiment of the present invention
  • FIG. 4 is a flowchart of determining a moving object in the image data according to the first pixel value and the second pixel value according to an embodiment of the present invention
  • FIG. 5 is a flowchart of identifying a working area where a moving object in the image data is located according to an embodiment of the present invention
  • FIG. 6 is a flowchart of establishing a statistical matrix for reflecting the frequency of movement changes of the moving object provided by an embodiment of the present invention
  • FIG. 7 is a flowchart of determining the working area where the moving object is located according to the statistical matrix according to an embodiment of the present invention.
  • FIG. 8 is a flowchart of updating the statistical matrix according to an embodiment of the present invention.
  • FIG. 9 is a flowchart of a piece counting operation performed on the garment after quality inspection according to the moving object and the work area provided by an embodiment of the present invention.
  • FIG. 10 is a schematic structural diagram of a piece counting device for clothing provided by an embodiment of the present invention.
  • Fig. 11 is a schematic structural diagram of an electronic device corresponding to the piece counting device for clothing provided by the embodiment shown in Fig. 10.
  • the words “if” and “if” as used herein can be interpreted as “when” or “when” or “in response to determination” or “in response to detection”.
  • the phrase “if determined” or “if detected (statement or event)” can be interpreted as “when determined” or “in response to determination” or “when detected (statement or event) )” or “in response to detection (statement or event)”.
  • Fig. 1 is a flowchart of a method for counting pieces of clothing provided by an embodiment of the present invention; referring to Fig. 1, this embodiment provides a method for counting pieces of clothing.
  • the main body of the method is a piece counting device.
  • the device executes the piece counting method, it can realize the piece counting statistics of the garments undergoing quality inspection operations in the subsequent process of producing and processing garments.
  • the piece counting method may include:
  • S101 Acquire at least one frame of image data for performing quality inspection operations on clothing.
  • the image data in this embodiment can be acquired in real time.
  • an image acquisition device when performing quality inspection operations on clothing, an image acquisition device can be installed at a preset position.
  • the image acquisition device can be a camera.
  • the acquisition device can acquire real-time image data for quality inspection operations on clothing.
  • the image data in this embodiment may also be acquired in non-real time.
  • the image data may be acquired by a preset image acquisition device and stored in a preset storage area, and the image data can be obtained by accessing the storage area;
  • the image data may also be actively or passively sent by the image acquisition device.
  • the image data may be stored in a preset area of the image acquisition device.
  • those skilled in the art can also choose other ways to obtain image data for quality inspection operations on clothing according to specific design requirements and application scenarios, as long as the accuracy and reliability of image data acquisition can be guaranteed. Repeat.
  • the method in this embodiment may further include: adjusting the resolution of at least one frame of image data , So that the resolution of the image data meets the preset standard.
  • the preset standard is preset, and those skilled in the art can set different resolution standards according to specific application requirements.
  • the preset standard may mean that the resolution of the image data is 320*240dpi; or, the preset The standard may mean that the resolution of the image data is 640*480dpi and so on. It should be noted that if the resolution of the image data meets the preset standard, the accuracy and reliability of the analysis and recognition of the image data can be guaranteed. Therefore, after the image data is obtained, the resolution of the image data can be obtained first.
  • the rate is greater than the preset standard, it means that the image data at this time is a large-resolution image.
  • the amount of calculated data is relatively large.
  • the image in order to ensure the quality and efficiency of image data processing, the image can be reduced
  • the resolution of the data can reduce the amount of calculation for processing the image data, thereby ensuring the real-time and reliability of the image data processing.
  • the resolution of the image data is less than the preset standard, it means that the image data at this time is a small resolution image.
  • the image data can be improved Resolution.
  • 320*240dpi is used as the preset standard resolution.
  • the resolution of the acquired image data is 1280*720dpi
  • the resolution is greater than the preset standard. Therefore, the current image data can be downsampled.
  • the resolution of image data is adjusted from 1280*720dpi to 320*240dpi, thereby reducing the amount of calculation for processing image data, ensuring the real-time and reliability of image data processing, and obtaining accurate processing results .
  • the resolution of the acquired image data is 160*120dpi
  • the resolution is less than the preset standard. Therefore, the current image data can be adjusted so that the resolution of the image data is adjusted from 160*120dpi to 320*240dpi, so It can effectively ensure the accuracy of image data recognition, so that accurate processing results can be obtained.
  • the method in this embodiment may further include: filtering and denoising processing on at least one frame of image data.
  • the acquired image data may have a lot of noise.
  • a Gaussian model can be used for at least one frame of image The data undergoes filtering and denoising processing to eliminate the noise mixed in the image and obtain clearer image data.
  • S102 Identify a moving object in the image data and a working area where the moving object is located.
  • the image data can be analyzed and processed to identify the moving object in the image data and the working area where the moving object is located.
  • the moving object refers to the staff who performs the quality inspection operation on the clothing
  • the working area where the moving object is located refers to the area where the staff performs the quality inspection operation on the clothing.
  • this embodiment does not limit the specific implementation process of recognizing moving objects and working areas.
  • Those skilled in the art can choose different implementation methods according to specific design requirements and application scenarios, for example: recognizing moving objects in image data When using the working area where the moving object is located, the contour information of all objects in the image data can be obtained first, and the contour information of all objects can be analyzed and compared with the pre-set standard contour information.
  • the standard contour information is pre-stored with the staff Corresponding contour information, it can be understood that the standard contour information can be one or more; the object corresponding to the contour information matching at least one standard contour information is determined as a moving object; then, the moving object is acquired in the image data For the time information of all areas in the, the area whose time information is greater than or equal to the preset time threshold is determined as the working area where the moving object is located.
  • the standard contour information can be one or more; the object corresponding to the contour information matching at least one standard contour information is determined as a moving object; then, the moving object is acquired in the image data For the time information of all areas in the, the area whose time information is greater than or equal to the preset time threshold is determined as the working area where the moving object is located.
  • those skilled in the art can also use other methods to identify the moving object and the working area, as long as the accuracy of acquiring the moving object and the working area can be ensured, which will not be repeated here.
  • S103 Perform a piece counting operation on the clothing after quality inspection according to the moving object and the work area.
  • the moving object and the working area After acquiring the moving object and the working area, the moving object and the working area can be analyzed and processed, and whether to perform piece counting operations on the clothing can be realized according to the analysis and processing results; specifically, refer to FIG. 9 as shown in FIG.
  • the piece counting operation of the clothing after quality inspection can include:
  • whether the moving object is located in the working area can refer to whether the position of the moving object is located in the working area.
  • the current position information of the moving object can be obtained first to determine whether the current position information is in the working area. If the current position information is in the working area, it can be determined that the moving object is located in the working area; if the current position information is not working In the area, it can be determined that the moving object is not in the work area.
  • whether the moving object is located in the working area can refer to whether the motion change range of the moving object is located in the working area.
  • the placement behavior of clothes after quality inspection can be identified and tested.
  • the moving object exceeds the working area, it means that the moving object at this time is placing the clothing after the quality inspection is completed, and then the piece-counting operation can be performed on the clothing after the quality inspection, so that the clothing after the quality inspection operation can be obtained Number of pieces.
  • the method in this embodiment may further include: if the moving object is in the working area, no piece counting operation is performed.
  • the method in this embodiment may further include:
  • the preset first area is used to place the clothing whose quality inspection results are qualified, and the first area may be located at a preset position of the work area, for example, the first area may be located on the left or right side of the work area;
  • the moving object is no longer in the working area and is located in the first area, it means that the moving object has completed the quality inspection operation of the clothing, and the quality inspection result of the clothing is qualified. Therefore, the moving object is executing the placement of qualified clothing in the first area. For the operation in one area, at this time, the qualified garment can be counted.
  • the preset second area is used to place the clothes whose quality inspection results are unqualified.
  • the second area can be located at the preset position of the working area. It should be noted that the second area and the first area have different settings. ; For example, when the first area is located on the left side of the working area, the second area can be located on the right side of the working area; when the first area is located on the right side of the working area, the second area can be located on the left side of the working area; When the object is no longer in the working area and is located in the second area, it means that the moving object has completed the quality inspection operation of the clothing, and the quality inspection result of the clothing is unqualified. Therefore, the moving object is executing the unqualified clothing placement The operation in the second area, at this time, can perform piece counting operations on unqualified garments.
  • the detection result is that the moving object is located in the work area, it means that the moving object at this time is performing quality inspection operations on the clothing, and therefore, the piece counting operation on the clothing is not performed.
  • the method in this embodiment may further include:
  • S104 Store at least one frame of image data of the moving object performing quality inspection operations on the clothing.
  • a corresponding image data can be stored for each piece of clothing, so that users can view or retrieve relevant piece counting records at any time.
  • S105 Store image data of at least one frame of qualified clothing placed on the moving object.
  • S106 Store at least one frame of image data of unqualified clothing placed on the moving object.
  • statistics can be performed on the clothes placed with different quality inspection results and quality inspection results, and the corresponding image data for each garment when statistics are stored can be stored. So that users can view or retrieve relevant records of piece counting at any time.
  • the clothing piece counting method provided in this embodiment uses at least one frame of image data for performing quality inspection operations on clothing to identify the moving object in the image data and the working area where the moving object is located, and perform quality inspection based on the moving object and the working area.
  • the piece-counting operation of the later garments effectively guarantees the piece-counting statistics of the garments undergoing quality inspection operations, and reduces the production and management cost of the garment piece-counting, ensuring the efficiency and accuracy of the garment piece-counting, and also facilitates the production management of the factory.
  • the management efficiency of the factory is improved; at the same time, users can obtain production process data and understand the order production progress at any time, and finally realize efficient production and sales collaboration.
  • Fig. 2 is a flow chart of identifying moving objects in image data provided by an embodiment of the present invention
  • Fig. 3 is a process of identifying moving objects in each frame of image data according to at least one frame of image data and a background model image provided by an embodiment of the present invention
  • Figure 4 is a flowchart of determining a moving object in image data according to the first pixel value and the second pixel value provided by an embodiment of the present invention; on the basis of the above embodiment, continue to refer to Figures 2 to 4,
  • This embodiment does not limit the specific implementation process of recognizing moving objects in image data.
  • recognizing moving objects in image data in this embodiment may include :
  • S1021 Create a background model image based on at least one frame of image data.
  • a Gaussian mixture background modeling method may be used to establish a background model image of the acquired at least one frame of image data, and the background model image includes a working environment of a moving object.
  • a background model image can be established based on all image data; alternatively, at least one background model image can be established based on at least one frame of image data, that is, a background model can be established based on part of the image data in at least one frame of image data
  • another background model image is established based on other image data in at least one frame of image data.
  • a background model image can be established based on all the image data.
  • the size of the established background model image is the same as the size of the image data.
  • a corresponding background model image can be created based on 1000 frames of image data, and the size of the created background model image is the same as the size of the image data; alternatively, it can also be based on 1000 frames of image data
  • Corresponding first background model image and second background model image wherein the size of the first background model image is the same as the size of the corresponding image data; the size of the second background model image is the same as the size of the corresponding image data the same.
  • S1022 Identify a moving object in each frame of image data according to at least one frame of image data and a background model image.
  • each frame of image data can be analyzed and compared with the background model image, so as to identify the moving object in each frame of image data.
  • identifying a moving object in each frame of image data according to at least one frame of image data and a background model image may include:
  • S10221 Obtain the first pixel value of each pixel in at least one frame of image data and the second pixel value of the same pixel in the background model image.
  • the background model image and the image data have the same size, for each pixel in the image data, there is a corresponding pixel in the background model image.
  • the first pixel value of each pixel in the image data can be obtained, and the second pixel value of the corresponding pixel in the background model image can be obtained.
  • the method in the prior art can be used to obtain The pixel value will not be explained here.
  • S10222 Determine a moving object in the image data according to the first pixel value and the second pixel value.
  • the first pixel value and the second pixel value may be analyzed and processed, so as to determine the moving object in the image data according to the analysis and processing result.
  • determining the moving object in the image data according to the first pixel value and the second pixel value may include:
  • S102221 Obtain a difference value between the first pixel value and the second pixel value.
  • the difference value is the degree of difference between the first pixel value and the second pixel value.
  • the difference value may be the difference between the first pixel value and the second pixel value, or the difference value may also be the difference between the first pixel value and the second pixel value.
  • the ratio of the second pixel value of course, those skilled in the art can also adopt other ways to reflect the difference value between the first pixel value and the second pixel value, which will not be repeated here.
  • S102222 Search for all pixels with a difference value greater than or equal to a preset pixel threshold in the image data, where all the pixels constitute a moving object in the image data.
  • the image data includes a dynamic area and a static area.
  • the dynamic area is the area where the pixels in the image data can change, that is: the dynamic area is composed of dynamic pixels; the static area is the image data
  • the area in which the pixels basically do not change, that is, the static area is composed of static pixels. It can be seen from the above that the area where the moving object is located is the dynamic area in the image data.
  • the difference between the first pixel value and the second pixel value is greater than or equal to the pixel threshold, it indicates that the pixels in the image data and the background
  • the corresponding pixels in the model image have a large difference.
  • the pixel can be determined to be a dynamic pixel, so that all the dynamic pixels in the image data can be obtained. At this time, all the dynamic pixels constitute the image data. Moving objects.
  • the pixel threshold value in this embodiment is preset. Those skilled in the art can determine the specific value range of the pixel threshold value according to specific design requirements and application scenarios. It is understood that different difference values may correspond to different values.
  • Currentground (i, j) is the first pixel value of a pixel in a certain frame of image data
  • Background (i, j) is the second pixel value of the corresponding pixel in the background model image
  • Foreground (i, j) is The difference area of the image data relative to the background model image, and the difference area is the moving object in the image data.
  • Example 2 When the difference value is the ratio of the first pixel value to the second pixel value, the corresponding pixel threshold can be TH2, and the moving object in the image data can be determined according to the following formula:
  • Currentground (i, j) is the first pixel value of a pixel in a certain frame of image data
  • Background (i, j) is the second pixel value of the corresponding pixel in the background model image
  • Foreground (i, j) is The difference area of the image data relative to the background model image, and the difference area is the moving object in the image data.
  • Recognizing the moving objects in the image data in the above manner effectively ensures the accuracy and reliability of the recognition of the moving objects in each frame of image data, thereby ensuring the accuracy of the piece-counting operation of the clothing.
  • FIG. 5 is a flowchart of identifying the working area of a moving object in image data provided by an embodiment of the present invention
  • FIG. 6 is a flowchart of establishing a statistical matrix used to reflect the frequency of motion changes of moving objects provided by an embodiment of the present invention
  • 7 is a flow chart of determining the working area of the moving object based on the statistical matrix provided by the embodiment of the present invention; on the basis of the above embodiment, continuing to refer to Figures 5 to 7, it can be seen that this embodiment is useful for identifying moving objects in image data.
  • the specific implementation process of the working area is not limited, and those skilled in the art can set according to specific design requirements.
  • the working area where the moving object in the recognition image data is located in this embodiment may include:
  • the establishment of a statistical matrix used to reflect the motion change frequency of a moving object may include:
  • S10231 Obtain a statistical value corresponding to each pixel in at least one frame of image data.
  • the statistical value can be determined based on the motion characteristics of each pixel in each frame of image data. Since each image data includes a dynamic area and a static area, the pixels in the dynamic area can be dynamic pixels. The pixel in the static area can be a static pixel, and the motion characteristic of the pixel is whether the pixel is a dynamic pixel or a static pixel; when the pixel is a dynamic pixel, it can correspond to a preset statistical value; When the pixel is a static pixel, it can correspond to another preset statistical value. After obtaining multiple statistical values, a statistical matrix can be established based on the statistical values, and the size of the established statistical matrix is the same as the size of the image data.
  • each image data is 320X240dpi. Therefore, an initial statistical matrix with a size of 320*240 can be established first, assuming that each element in the initial statistical matrix is 0, that is, : The default initial statistical value of each element is 0.
  • identifying the motion characteristics of pixel A in the first frame of image data determine that pixel A is a dynamic pixel. At this time, you can add 1 to the element corresponding to pixel A in the initial statistical matrix to obtain The statistical value corresponding to pixel A is 1.
  • identifying the motion characteristics of pixel A in the second frame of image data it is found that the pixel A is a static pixel.
  • the initial statistical matrix can be made to correspond to pixel A
  • the element of is kept unchanged.
  • the statistical value corresponding to the pixel A in the initial statistical matrix can be determined as 1.
  • the pixel B in the first frame of image data it is found that the pixel B is a dynamic pixel.
  • you can add 1 to the element corresponding to the pixel B in the initial statistical matrix to obtain The statistical value corresponding to pixel B is 1.
  • the pixel B in the statistical matrix can be The corresponding statistical value is determined 2; specifically, the statistical value in the statistical matrix satisfies the following relationship:
  • Motionground (i, j) is the preset statistical value in the statistical matrix
  • the pixels at time are static pixels.
  • Foreground(i,j) 255, it means that the pixels at this time are dynamic pixels.
  • the statistical values corresponding to the pixels in all the image data can be obtained, and the statistical matrix can be established based on the statistical values.
  • S1024 Determine the working area where the moving object is located according to the statistical matrix.
  • the statistical matrix can be used to determine the working area. Specifically, determining the working area of the moving object according to the statistical matrix can include:
  • S10241 Perform normalization processing on the statistical matrix to obtain a pixel gray value corresponding to each statistical value in the statistical matrix.
  • the gray threshold is a preset limit. This embodiment does not limit its specific numerical range. Those skilled in the art can set it arbitrarily according to specific design requirements.
  • the gray threshold can be 20, 30 Or 40 and so on.
  • the statistical matrix can be displayed as an image.
  • the gray value of each pixel in the image is 0-255.
  • the pixel gray value is greater than or equal to the gray threshold , It means that the pixel area corresponding to the gray value of the pixel changes frequently, and the pixel area corresponding to the gray value of the pixel can be determined as the working area where the moving object is located.
  • the statistical matrix is used to identify the working area where the moving object is located. Specifically, the statistical matrix is used to analyze which pixel areas in the image data change frequently and which pixel areas basically do not change. Based on the above-mentioned frequency of movement changes, it is estimated that the moving object is performing normally.
  • the scope of the work area of quality inspection specifically, the area where the movement frequency is relatively high is identified, and this area is the work area for the moving object to perform the quality inspection operation, thus effectively ensuring the accuracy and reliability of the work area recognition, and further improving The precision used by this piece counting method.
  • the numerical value of the statistical matrix cannot be increased indefinitely. If the numerical elements of the statistical matrix increase to a certain degree, it will affect the accuracy of processing the image data; and the quality of clothing is inspected on the moving objects.
  • the working area where the moving object is located is not static. It can be changed at any time according to the change of the moving object. Therefore, in order to ensure the accuracy of identifying the working area, the statistical matrix can be updated periodically .
  • the method in this embodiment may further include:
  • updating the statistical matrix may include:
  • S2011 Obtain a preset update coefficient, where the update coefficient is a positive number less than 1.
  • the update coefficient is preset, and this embodiment does not limit its specific numerical range. Those skilled in the art can set it arbitrarily according to specific application requirements, as long as it can ensure that the update coefficient meets the above requirements. That is, the update coefficient can be any value that satisfies the above conditions, for example: 0.1, 0.2, 0.3, 0.5, 0.8, etc. For ease of description, the following content is described with an update coefficient of 0.5 as an example.
  • the statistical matrix When updating the statistical matrix, the statistical matrix can be updated according to the preset period or the preset fixed number of frames, so that the overall value of the statistical matrix is updated according to the preset update coefficient, so that the update can be used
  • the latter statistical matrix recognizes the work area, realizes the adaptive estimation of the work area of the moving object, and effectively ensures the accuracy and reliability of the work area determination.
  • a camera can be installed at a preset position in the factory.
  • the camera can collect real-time image data of a moving object performing quality inspection operations on clothing.
  • the obtained image data can be obtained by Gaussian filtering. Perform filtering and denoising processing; then, use Gaussian mixture background modeling method to perform background modeling image based on the working environment of the moving object, and determine the moving object in the image data according to the established background model image.
  • the movement change frequency of the moving object the area with a relatively high movement change frequency is identified, that is, the normal working area for quality inspection of clothing.
  • the quality inspection status of the sports object can be judged, and the quality of the clothing can be judged. After the inspection is completed, the garment can be counted, and the image data for the quality inspection operation can be saved.
  • the method provided by this application embodiment can effectively reduce the cost and difficulty of digital transformation of the factory, and has the characteristics of lightweight deployment and strong reproducibility.
  • the factory can be obtained in real time without changing the original working methods of workers.
  • the real-time progress of clothing processing is synchronized to producers, platforms, and consumers, so as to achieve efficient production and sales synergy, and it is conducive to accurate matching, optimization and improvement of workers' working conditions.
  • FIG. 10 is a schematic structural diagram of a piece counting device for clothing provided by an embodiment of the present invention. referring to FIG. 10, this embodiment provides a piece counting device for clothing, which can perform the above-mentioned clothing piece counting method.
  • the piece counting device may include:
  • the obtaining module 11 is used to obtain at least one frame of image data for quality inspection of clothing
  • the recognition module 12 is used to recognize a moving object in the image data and a working area where the moving object is located;
  • the piece-counting module 13 is used to perform piece-counting operations on the garments after quality inspection according to the moving objects and the working area.
  • the recognition module 12 when the recognition module 12 recognizes a moving object in the image data, the recognition module 12 can be used to perform: establish a background model image based on at least one frame of image data; recognize each frame of image based on at least one frame of image data and the background model image The moving objects in the data.
  • the recognition module 12 when the recognition module 12 recognizes a moving object in each frame of image data based on at least one frame of image data and a background model image, the recognition module 12 may be used to perform: obtain the information of each pixel in at least one frame of image data The first pixel value and the second pixel value of the same pixel in the background model image; the moving object in the image data is determined according to the first pixel value and the second pixel value.
  • the identification module 12 may be used to perform: obtain the difference value between the first pixel value and the second pixel value; Find all pixels with a difference value greater than or equal to a preset pixel threshold in the image data, where all the pixels constitute a moving object in the image data.
  • the recognition module 12 when the recognition module 12 recognizes the working area of the moving object in the image data, the recognition module 12 can be used to perform: establish a statistical matrix for reflecting the movement frequency of the moving object, and the size of the statistical matrix and the image data The size is the same; according to the statistical matrix to determine the working area where the moving object is located.
  • the identification module 12 when the identification module 12 establishes a statistical matrix for reflecting the frequency of motion changes of the moving object, the identification module 12 can be used to perform: obtain a statistical value corresponding to each pixel in at least one frame of image data; Statistic values create a statistical matrix.
  • the identification module 12 when the identification module 12 determines the working area where the moving object is located according to the statistical matrix, the identification module 12 can be used to perform: normalize the statistical matrix to obtain the pixel gray corresponding to each statistical value in the statistical matrix. Degree value; when the pixel gray value is greater than or equal to the preset gray threshold, the pixel area corresponding to the pixel gray value is determined as the working area where the moving object is located.
  • the identification module 12 in this embodiment is also used to perform: update the statistical matrix.
  • the identification module 12 when the identification module 12 updates the statistical matrix, the identification module 12 can be used to perform: obtain a preset update coefficient, where the update coefficient is a positive number less than 1; and all statistical values included in the statistical matrix Multiply with the updated coefficients to obtain the updated value; obtain the updated statistical matrix based on the updated value.
  • the piece-counting module 13 when the piece-counting module 13 performs a piece-counting operation on the clothes after quality inspection according to the moving object and the working area, the piece-counting module 13 can be used to perform: detecting whether the moving object is located in the working area; if the moving object is not in the working area , Then perform piece counting operations on the garments after quality inspection.
  • the piece counting module 13 in this embodiment can also be used to perform: if the moving object is located in the preset first area, perform a piece counting operation on qualified clothing after quality inspection ; Or, if the moving object is located in the preset second area, perform a piece counting operation on the unqualified clothing after quality inspection.
  • the acquiring module 11 in this embodiment is further configured to adjust the resolution of the at least one frame of image data after acquiring at least one frame of image data for quality inspection of the clothing, so that the resolution of the image data meets a preset standard .
  • the acquisition module 11 in this embodiment is further configured to perform filtering and denoising processing on at least one frame of image data before identifying the moving object in the image data and the working area where the moving object is located.
  • the piece counting module 13 in this embodiment is further configured to perform: storing at least one frame of image data of a moving object performing a quality inspection operation on clothing.
  • the device shown in FIG. 10 can execute the method of the embodiment shown in FIG. 1 to FIG. 9.
  • parts that are not described in detail in this embodiment please refer to the related description of the embodiment shown in FIG. 1 to FIG. 9.
  • the implementation process and technical effects of this technical solution please refer to the description in the embodiment shown in FIG. 1 to FIG. 9, and will not be repeated here.
  • the structure of the piece counting device for clothing shown in FIG. 10 can be implemented as an electronic device, which can be various devices such as a mobile phone, a tablet computer, and a server.
  • the electronic device may include a processor 21 and a memory 22.
  • the memory 22 is used to store a program that supports the electronic device to execute the clothing piece counting method provided in the embodiments shown in FIGS. 1 to 9 above, and the processor 21 is configured to execute the program stored in the memory 22.
  • the program includes one or more computer instructions, where one or more computer instructions can implement the following steps when executed by the processor 21:
  • the garments after quality inspection are counted.
  • the processor 21 is further configured to execute all or part of the steps in the embodiments shown in FIGS. 1 to 9 above.
  • the structure of the electronic device may also include a communication interface 23 for the electronic device to communicate with other devices or a communication network.
  • an embodiment of the present invention provides a computer storage medium for storing computer software instructions used by electronic devices, which includes programs for executing the garment piece counting method in the method embodiments shown in FIGS. 1-9. .
  • the device embodiments described above are merely illustrative.
  • the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments. Those of ordinary skill in the art can understand and implement without creative work.
  • These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable equipment to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction device, and the instruction device implements A function specified in a flow or multiple flows in a flowchart and/or a block or multiple blocks in a block diagram.
  • These computer program instructions can also be loaded on a computer or other programmable equipment, so that a series of operation steps are executed on the computer or other programmable equipment to produce computer-implemented processing, so that the instructions executed on the computer or other programmable equipment provide Steps used to implement the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.
  • the computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
  • processors CPUs
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • the memory may include non-permanent memory in a computer readable medium, random access memory (RAM) and/or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer readable media.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM flash memory
  • Computer-readable media includes permanent and non-permanent, removable and non-removable media, and information storage can be realized by any method or technology.
  • the information can be computer-readable instructions, data structures, program modules, or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical storage, Magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices. According to the definition in this article, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

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Abstract

La présente invention concerne un procédé, un appareil et un dispositif de comptage de vêtements par nombre de pièces. Le procédé consiste : à acquérir au moins une trame de données d'image pour une opération d'inspection de qualité effectuée sur des vêtements ; à identifier un objet mobile dans les données d'image et une zone de travail où se trouve l'objet mobile ; et à réaliser, en fonction de l'objet mobile et de la zone de travail, une opération de comptage de pièces sur les vêtements soumis à une inspection de qualité. Au moyen de l'acquisition d'au moins une trame de données d'image pour une opération d'inspection de qualité effectuée sur des vêtements, de l'identification d'un objet mobile dans les données d'image et d'une zone de travail où l'objet mobile est situé, et de la réalisation, en fonction de l'objet mobile et de la zone de travail, d'une opération de comptage de pièces sur les vêtements soumis à une inspection de qualité, le procédé assure efficacement le comptage de pièces des vêtements soumis à l'opération d'inspection de qualité, réduit le coût de gestion de production de comptage de pièces de vêtements, garantit l'efficacité et la précision de comptage de pièces ds vêtements, et facilite également la gestion de production d'une usine et améliore l'efficacité de gestion de l'usine ; en outre, le procédé peut permettre à un utilisateur d'acquérir des données de processus de production à tout moment et de comprendre la progression de la production de commandes, ce qui permet d'obtenir finalement une planification efficace des ventes et des opérations.
PCT/CN2020/071926 2019-01-23 2020-01-14 Procédé, appareil et dispositif de comptage de vêtements par nombre de pièces WO2020151530A1 (fr)

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