CN109344799B - Article identification method, article identification device, article identification equipment, storage medium and electronic device - Google Patents
Article identification method, article identification device, article identification equipment, storage medium and electronic device Download PDFInfo
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Abstract
The embodiment of the invention provides an article identification method, an article identification device, article identification equipment, a storage medium and an electronic device, wherein the method comprises the following steps: and identifying the article which does not pass the detection on the production line, wherein when the article which does not pass the detection is the product to be detected on the production line, an alarm prompt is sent out, and when the article which does not pass the detection is not the product to be detected on the production line, the alarm prompt is not sent out. The problem of among the prior art because detection on the assembly line can't distinguish nonconforming product and interferent, cause the assembly line to report to the police unusually is solved, saved personnel's energy and time cost greatly, improved production efficiency.
Description
Technical Field
The invention relates to the technical field of intelligent equipment, in particular to an article identification method, an article identification device, article identification equipment, a storage medium and an electronic device.
Background
In the visual inspection project with the assembly line body, the object carried by the assembly line body is provided with part of interferents such as repair equipment, tool boxes and the like besides the equipment to be inspected. In the detection process, the visual detection instrument only detects whether the object meets certain characteristic requirements and sends OK and NG signals; because can't distinguish the object kind, consequently when interfering logistics visual detection instrument such as repair equipment, frock case, often can lead to reporting to the police and the phenomenon of stopping the line, can cause the waste of personnel's energy, increase unnecessary time cost, reduction in production efficiency.
In the related art, no reasonable solution is provided for the problem of abnormal alarm of the assembly line due to the fact that unqualified products and interferents cannot be distinguished through detection on the assembly line.
Disclosure of Invention
The embodiment of the invention provides an article identification method, an article identification device, article identification equipment, a storage medium and an electronic device, and at least solves the problem that in the related technology, due to the fact that detection on a production line cannot distinguish unqualified products and interferents, abnormal alarm of the production line is caused.
According to an embodiment of the present invention, there is provided an article identification method including: and identifying the article which does not pass the detection on the production line, wherein when the article which does not pass the detection is the product to be detected on the production line, an alarm prompt is sent out, and when the article which does not pass the detection is not the product to be detected on the production line, the alarm prompt is not sent out.
Preferably, before identifying an item on the production line that fails a detection, the method further comprises: performing feature detection on the articles on the production line; when the item fails the feature detection, an image of the failed detected item is captured.
Preferably, identifying an item on the production line that fails a test comprises: and identifying the images of the articles which fail to pass the detection by using a sample training model, wherein the sample training model comprises a training set, and the training set comprises an image set of unqualified products to be detected and an image set of interferents.
Preferably, identifying the image of the failed detected item using the sample training model comprises: when the article which fails to pass the detection is identified to be an unqualified product to be detected, an alarm prompt is sent out; and when the article which fails to pass the detection is identified as the interference object, no alarm prompt is sent out.
Preferably, when the article which fails to pass the detection is identified as an unqualified product to be detected, and an alarm prompt is sent, the method further comprises the following steps: and outputting detection information and stopping the operation of the pipeline.
Preferably, after the image of the article failing the detection is identified by the sample training model, the method further comprises: and storing the images of the articles which fail to pass the detection into the sample training model for training.
Preferably, storing the image of the article failed to be detected in the sample training model for training comprises: storing the images of the articles which fail to pass the detection into the training set according to the identified categories; converting the images stored in the training set into gray images, and performing edge detection processing by using an edge detection operator to extract texture features of the images; converting an original RGB color image into an HSV three-channel color model, extracting information of hue, saturation and brightness in the original RGB color image, and extracting information of a connected region of similar color values; extracting characteristic information of a color histogram of the image; and putting all the extracted characteristic information into the sample training model for training.
According to another embodiment of the present invention, there is also provided an article recognition apparatus including: the identification module is used for identifying the article which fails to pass the detection on the production line; and the alarm module is used for sending an alarm prompt when the article which does not pass the detection is the product to be detected on the assembly line, and not sending the alarm prompt when the article which does not pass the detection is not the product to be detected on the assembly line.
According to another embodiment of the present invention, there is also provided an article identification apparatus including: an identification device for identifying an item on the production line that fails a test; and the alarm device sends out an alarm prompt when the article which does not pass the detection is the product to be detected on the assembly line, and does not send out the alarm prompt when the article which does not pass the detection is not the product to be detected on the assembly line.
Preferably, the apparatus further comprises: the detection device is used for carrying out characteristic detection on the articles on the assembly line; image capture means for capturing an image of the article failed detection when the article failed the feature detection.
Preferably, the identification means is further adapted to: and identifying the images of the articles which fail to pass the detection by using a sample training model, wherein the sample training model comprises a training set, and the training set comprises an image set of unqualified products to be detected and an image set of interferents.
Preferably, the apparatus further comprises: and the training device is used for storing the image of the article which fails to pass the detection into the sample training model for training.
Preferably, the training means is further adapted to: storing the images of the articles which fail to pass the detection into the training set according to the identified categories; converting the images stored in the training set into gray images, and performing edge detection processing by using an edge detection operator to extract texture features of the images; converting an original RGB color image into an HSV three-channel color model, extracting information of hue, saturation and brightness in the original RGB color image, and extracting information of a connected region of similar color values; extracting characteristic information of a color histogram of the image; and putting all the extracted characteristic information into the sample training model for training.
According to another embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
According to another embodiment of the present invention, there is also provided an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the steps of any of the above method embodiments.
According to the embodiment of the invention, the article which does not pass the detection on the assembly line is further identified, when the article which does not pass the detection is the product to be detected on the assembly line, the alarm prompt is sent out, and when the article which does not pass the detection is not the product to be detected on the assembly line, the alarm prompt is not sent out. The problem of among the prior art because detection on the assembly line can't distinguish nonconforming product and interferent, cause the assembly line to report to the police unusually is solved, saved personnel's energy and time cost greatly, improved production efficiency.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a block diagram of a hardware configuration of a mobile terminal of an article identification method according to an embodiment of the present invention;
FIG. 2 is a flow chart of an article identification method according to an embodiment of the present invention;
FIG. 3 is yet another flow chart of an item identification method of an embodiment of the present invention;
fig. 4 is a block diagram of the structure of an article recognition apparatus according to an embodiment of the present invention;
fig. 5 is a block diagram of the structure of an item identification apparatus according to an embodiment of the present invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Example 1
The method provided by the first embodiment of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking an example of the operation on a mobile terminal, fig. 1 is a hardware structure block diagram of the mobile terminal of an article identification method according to an embodiment of the present invention. As shown in fig. 1, the mobile terminal 10 may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and optionally may also include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the data information obtaining method in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, so as to implement the above-mentioned method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal 10. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
The embodiment of the invention provides an article identification method. Fig. 2 is a flowchart of an article identification method according to an embodiment of the present invention, as shown in fig. 2, the method including:
step S201, identifying the article which does not pass the detection on the production line, wherein when the article which does not pass the detection is the product to be detected on the production line, an alarm prompt is sent out, and when the article which does not pass the detection is not the product to be detected on the production line, the alarm prompt is not sent out.
Through the method, the article which does not pass the detection on the production line is identified, wherein when the article which does not pass the detection is the product to be detected on the production line, the alarm prompt is sent out, and when the article which does not pass the detection is not the product to be detected on the production line, the alarm prompt is not sent out. The problem of among the prior art because detection on the assembly line can't distinguish nonconforming product and interferent, cause the assembly line to report to the police unusually is solved, saved personnel's energy and time cost greatly, improved production efficiency.
According to a preferred implementation manner of the embodiment of the present invention, before the step S201, the method further includes: carrying out feature detection on the articles on the assembly line; when an item fails the feature detection, an image of the item failing the detection is acquired.
According to a preferred implementation of the embodiment of the present invention, the step S201 may be implemented by: and identifying the images of the articles which fail to pass the detection by using a sample training model, wherein the sample training model comprises a training set, and the training set comprises an image set of unqualified products to be detected and an image set of interferents.
According to a preferred implementation of the embodiment of the present invention, the identification of the image of the article which fails to pass the detection by using the sample training model can be realized by the following steps: when the article which fails to pass the detection is identified to be an unqualified product to be detected, an alarm prompt is sent out, wherein after the alarm prompt is sent out, detection information can be output, and the operation of the production line is stopped; when the article which fails to pass the detection is identified as the interference object, no alarm prompt is sent out, the operation of the production line is not required to be stopped without alarming, and the production line can continue to operate normally.
According to a preferred implementation of the embodiment of the present invention, after the identifying the image of the article failing the detection by using the sample training model, the method further includes: and storing the images of the articles which do not pass the detection into a sample training model for training.
According to a preferred implementation of the embodiment of the present invention, the training method may be implemented by:
1) storing the images of the articles which do not pass the detection into a training set according to the identified categories;
2) converting the image stored in the training set into a gray image, and performing edge detection processing by using an edge detection operator to extract texture features of the image (the edge detection operator mentioned in the embodiment of the invention can be a Sobel edge detection operator or a Canny edge detection operator);
3) converting an original RGB color image into an HSV three-channel color model, extracting information of hue, saturation and brightness in the original RGB color image, and extracting information of a connected region of similar color values;
4) extracting characteristic information of a color histogram of the image;
5) and (3) putting all the characteristic information extracted in the steps 2) to 4) into a sample training model for training.
In order to better understand the technical solution in the embodiment of the present invention, specific steps of the article identification method in the embodiment of the present invention are specifically described below with reference to fig. 3. Fig. 3 is still another flowchart of an item identification method of an embodiment of the present invention. As shown in fig. 3, the method comprises the steps of:
1. and (4) enabling the object to flow to the position of the visual detection instrument, and dynamically detecting the product characteristics in real time.
2. The object passing the feature detection is marked as detection OK; the object whose feature detection does not pass needs to be subjected to an operation of distinguishing object types.
3. If the equipment is identified and distinguished to be normal detection equipment, the equipment is unqualified product and is marked as detection NG; if the identification area is other kinds of objects (namely interferents), the objects are not the equipment to be detected, and an alarm prompt is not needed.
4. And outputting the detection information. If the product is a product for detecting NG, an alarm line stop operation is carried out.
According to the technical scheme provided by the embodiment of the invention, the characteristic information such as the texture, the color histogram and the like of the image is trained in a machine learning supervision learning mode, the obtained model is used for classifying NG (failing to detect) pictures, and the NG pictures of the product to be detected and the pictures of the interferent are distinguished, so that whether the object is the product to be detected or the interferent is distinguished. Normal detection of products to be detected is not affected, non-detected products are distinguished and rejected, the occurrence of alarm phenomena of detection instruments is reduced, and frequent line stop of a production line is avoided.
Example 2
In this embodiment, an article identification apparatus is further provided, which is used to perform the steps in any of the above method embodiments, and the content that has been described is not described herein again. Fig. 4 is a block diagram showing the structure of an article recognition apparatus according to an embodiment of the present invention, as shown in fig. 4, the apparatus including: an identification module 40 for identifying an item on the production line that fails the detection; the alarm module 42 sends out an alarm prompt when the article which does not pass the detection is the product to be detected on the production line, and does not send out the alarm prompt when the article which does not pass the detection is not the product to be detected on the production line.
By the above means, the identification module 40 identifies the article on the assembly line that has not passed the detection; the alarm module 42 issues an alarm prompt when the article that fails to pass the detection is a product to be detected on the production line, and does not issue an alarm prompt when the article that fails to pass the detection is not a product to be detected on the production line. The problem of among the prior art because detection on the assembly line can't distinguish nonconforming product and interferent, cause the assembly line to report to the police unusually is solved, saved personnel's energy and time cost greatly, improved production efficiency.
The apparatus for implementing the steps in any of the above method embodiments may include various functional modules, if necessary, in the above method embodiments.
In this embodiment, an article identification device is further provided, which is configured to perform the steps in any one of the above method embodiments, and the content that has been described is not described herein again. Fig. 5 is a block diagram of a configuration of an item identification apparatus according to an embodiment of the present invention, as shown in fig. 5, the apparatus including: an identification device 50 for identifying an item on the line that fails the test; the alarm device 52 gives an alarm prompt when the article that fails to pass the detection is a product to be detected on the production line, and does not give an alarm prompt when the article that fails to pass the detection is not a product to be detected on the production line.
According to a preferred implementation of the embodiment of the invention, the apparatus further comprises: the detection device is used for carrying out characteristic detection on the articles on the assembly line; and the image acquisition device is used for acquiring the image of the article which fails the detection when the article fails the characteristic detection.
According to a preferred implementation of the embodiment of the invention, the identification means are further adapted to: and identifying the images of the articles which fail to pass the detection by using a sample training model, wherein the sample training model comprises a training set, and the training set comprises an image set of unqualified products to be detected and an image set of interferents.
According to a preferred implementation of the embodiment of the invention, the apparatus further comprises: and the training device is used for storing the images of the articles which fail to pass the detection into the sample training model for training.
According to a preferred implementation of the embodiment of the invention, the training means are further adapted to: storing the images of the articles which do not pass the detection into a training set according to the identified categories; converting the image stored in the training set into a gray image, and performing edge detection processing by using an edge detection operator to extract texture features of the image; converting an original RGB color image into an HSV three-channel color model, extracting information of hue, saturation and brightness in the original RGB color image, and extracting information of a connected region of similar color values; extracting characteristic information of a color histogram of the image; and putting all the extracted characteristic information into a sample training model for training.
According to the technical scheme provided by the embodiment of the invention, the characteristic information such as the texture, the color histogram and the like of the image is trained in a machine learning supervision learning mode, the obtained model is used for classifying NG (failing to detect) pictures, and the NG pictures of the product to be detected and the pictures of the interferent are distinguished, so that whether the object is the product to be detected or the interferent is distinguished. Normal detection of products to be detected is not affected, non-detected products are distinguished and rejected, the occurrence of alarm phenomena of detection instruments is reduced, and frequent line stop of a production line is avoided.
Example 3
Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
and S1, identifying the article which does not pass the detection on the production line, wherein when the article which does not pass the detection is the product to be detected on the production line, an alarm prompt is sent out, and when the article which does not pass the detection is not the product to be detected on the production line, the alarm prompt is not sent out.
Optionally, the storage medium is further arranged to store a computer program for performing the steps of:
carrying out feature detection on the articles on the assembly line; when an item fails the feature detection, an image of the item failing the detection is acquired.
Optionally, the storage medium is further arranged to store a computer program for performing the steps of:
and identifying the images of the articles which fail to pass the detection by using a sample training model, wherein the sample training model comprises a training set, and the training set comprises an image set of unqualified products to be detected and an image set of interferents.
Optionally, the storage medium is further arranged to store a computer program for performing the steps of:
and storing the images of the articles which do not pass the detection into a sample training model for training.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
and S1, identifying the article which does not pass the detection on the production line, wherein when the article which does not pass the detection is the product to be detected on the production line, an alarm prompt is sent out, and when the article which does not pass the detection is not the product to be detected on the production line, the alarm prompt is not sent out.
Optionally, the processor is further arranged to store a computer program for performing the steps of:
carrying out feature detection on the articles on the assembly line; when an item fails the feature detection, an image of the item failing the detection is acquired.
Optionally, the processor is further arranged to store a computer program for performing the steps of:
and identifying the images of the articles which fail to pass the detection by using a sample training model, wherein the sample training model comprises a training set, and the training set comprises an image set of unqualified products to be detected and an image set of interferents.
Optionally, the processor is further arranged to store a computer program for performing the steps of:
and storing the images of the articles which do not pass the detection into a sample training model for training.
For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and optional implementation manners, and details of this embodiment are not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. An article identification method, comprising:
identifying the article which does not pass the detection on the production line, wherein when the article which does not pass the detection is the product to be detected on the production line, an alarm prompt is sent out, and when the article which does not pass the detection is not the product to be detected on the production line, the alarm prompt is not sent out,
before identifying an item on the production line that fails a detection, the method further comprises:
performing feature detection on the articles on the production line;
capturing an image of the article that failed the detection when the article failed the feature detection,
identifying items on the assembly line that fail detection includes:
identifying the images of the articles which fail to pass the detection by using a sample training model, wherein the sample training model comprises a training set, the training set comprises an image set of unqualified products to be detected and an image set of interferents,
after identifying the image of the failed detected item using the sample training model, the method further comprises:
storing the images of the articles which fail to pass the detection into the sample training model for training,
storing the image of the article failing detection in the sample training model for training comprises:
storing the images of the articles which fail to pass the detection into the training set according to the identified categories;
converting the images stored in the training set into gray images, and performing edge detection processing by using an edge detection operator to extract texture features of the images;
converting an original RGB color image into an HSV three-channel color model, extracting information of hue, saturation and brightness in the original RGB color image, and extracting information of a connected region of similar color values;
extracting characteristic information of a color histogram of the image;
and putting all the extracted characteristic information into the sample training model for training.
2. The method of claim 1, wherein identifying the image of the undetected item using a sample training model comprises:
when the article which fails to pass the detection is identified to be an unqualified product to be detected, an alarm prompt is sent out;
and when the article which fails to pass the detection is identified as the interference object, no alarm prompt is sent out.
3. The method of claim 2, wherein after an alarm is issued when the product to be detected is identified as being unacceptable as an article failing the detection, the method further comprises:
and outputting detection information and stopping the operation of the pipeline.
4. An article identification device, comprising:
the identification module is used for identifying the article which fails to pass the detection on the production line;
the alarm module sends out an alarm prompt when the article which does not pass the detection is the product to be detected on the production line, does not send out the alarm prompt when the article which does not pass the detection is not the product to be detected on the production line,
the device is also used for carrying out characteristic detection on the articles on the assembly line before identifying the articles which do not pass the detection on the assembly line; capturing an image of the article that failed the detection when the article failed the feature detection,
the identification module is also used for identifying the images of the articles which fail to pass the detection by utilizing a sample training model, wherein the sample training model comprises a training set, the training set comprises an image set of unqualified products to be detected and an image set of interferents,
the device is also used for storing the image of the article failed to be detected into the sample training model for training after the image of the article failed to be detected is identified by using the sample training model,
the device is also used for storing the images of the articles which fail to pass the detection into the training set according to the recognized categories; converting the images stored in the training set into gray images, and performing edge detection processing by using an edge detection operator to extract texture features of the images; converting an original RGB color image into an HSV three-channel color model, extracting information of hue, saturation and brightness in the original RGB color image, and extracting information of a connected region of similar color values; extracting characteristic information of a color histogram of the image; and putting all the extracted characteristic information into the sample training model for training.
5. An article identification device, comprising:
an identification device for identifying an item on the production line that fails a test;
an alarm device which gives an alarm prompt when the article which does not pass the detection is the product to be detected on the production line, and does not give an alarm prompt when the article which does not pass the detection is not the product to be detected on the production line,
the apparatus further comprises:
the detection device is used for carrying out characteristic detection on the articles on the assembly line;
image capture means for capturing an image of said article failed said detection when said article failed said feature detection,
the identification device is further configured to:
identifying the images of the articles which fail to pass the detection by using a sample training model, wherein the sample training model comprises a training set, the training set comprises an image set of unqualified products to be detected and an image set of interferents,
the apparatus further comprises:
a training device for storing the image of the article failed to be detected in the sample training model for training,
the training device is further configured to:
storing the images of the articles which fail to pass the detection into the training set according to the identified categories;
converting the images stored in the training set into gray images, and performing edge detection processing by using an edge detection operator to extract texture features of the images;
converting an original RGB color image into an HSV three-channel color model, extracting information of hue, saturation and brightness in the original RGB color image, and extracting information of a connected region of similar color values;
extracting characteristic information of a color histogram of the image;
and putting all the extracted characteristic information into the sample training model for training.
6. A storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 3 when executed.
7. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 3.
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