CN112837302A - Mold state monitoring method and device, industrial personal computer, storage medium and system - Google Patents
Mold state monitoring method and device, industrial personal computer, storage medium and system Download PDFInfo
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Abstract
The embodiment of the application provides a method and a device for monitoring the state of a mold, an industrial personal computer, a storage medium and a system, wherein a template image and an image to be detected of a target mold are obtained, the template image is an image of the target mold in a normal state, and the template image comprises an interested area containing the image of the target mold; according to the interesting region of the template image, identifying and positioning the image to be detected by adopting a pyramid template matching algorithm to obtain a target region of the image to be detected; determining a sensitive subarea contained in the target area according to the pixel difference between the target area and the interested area; and determining whether the target mold is abnormal or not according to the area of the sensitive subarea, so that the automatic monitoring of the mold quality is realized, the real-time performance and the accuracy of the mold abnormality detection are considered, and the mold monitoring quality and the production efficiency are improved.
Description
Technical Field
The embodiment of the application relates to the technical field of mold protection, in particular to a method, a device, an industrial personal computer, a storage medium and a system for monitoring the state of a mold.
Background
In recent years, the increasing demand for injection molded articles in industries such as automobiles, buildings, household appliances, food, medicines and the like has promoted the development and improvement of the whole injection molding and forming technology level, and has also driven the development of the injection molding industry. In the injection molding industry, a mold is important molding equipment, and the quality of the mold is directly related to the final quality of a product. Because the mold occupies a large proportion in the injection molding production cost, the service life of the mold directly affects the product cost, and monitoring and detecting the production state of the mold are necessary for protecting the mold.
The state monitoring is carried out to the mould based on machine vision, is a comparatively effectual automatic mould monitoring scheme, and this scheme possesses stronger commonality, but along with the increase of gathering template quantity or the change of mould station, has the influence to the precision that detects speed and detection, consequently, how to compromise and detect speed and detection precision and become the focus of research in the mould state monitoring.
Disclosure of Invention
The embodiment of the application provides a method, a device, an industrial personal computer, a storage medium and a system for monitoring the state of a mold, which can give consideration to the high efficiency of the accuracy of the monitoring of the state of the mold, and improve the production efficiency while protecting the mold.
In a first aspect, an embodiment of the present application provides a method for monitoring a state of a mold, including:
acquiring a template image and an image to be detected of a target mold; the template image is an image of the target mold in a normal state, and the template image comprises an interested area containing the image of the target mold;
identifying and positioning the image to be detected by adopting a pyramid template matching algorithm according to the region of interest of the template image to obtain a target region of the image to be detected;
determining a sensitive sub-region contained in the target region according to the pixel difference between the target region and the region of interest;
and determining whether the target mold is abnormal or not according to the area of the sensitive subarea.
Optionally, the determining a sensitive sub-region included in the target region according to a pixel difference existing between the target region and the region of interest includes:
generating a pixel difference image based on the pixel difference between the target area and the interested area, wherein the pixel difference image comprises pixel difference values of corresponding pixel points in the target area and the interested area;
and determining a sensitive subarea contained in the target area according to the pixel difference value and the sensitivity threshold value in the pixel difference image.
Optionally, the determining a sensitive sub-region included in the target region according to the pixel difference value and the sensitivity threshold in the pixel difference map includes:
determining a target pixel difference point on the pixel difference image, wherein the pixel difference value is larger than the sensitivity threshold value;
and determining a sensitive subarea contained in the target area according to the position relation between the target pixel differential points, wherein each pixel differential point in the sensitive subarea is a target pixel differential point.
Optionally, the determining whether the target mold has an abnormality according to the area of the sensitive sub-region includes:
determining whether there is a single sensitive subregion in the target region that has an area greater than a first area threshold; if so, determining that the target mold is abnormal;
if not, determining whether the total area of each sensitive subarea is larger than a second area threshold value; and if so, determining that the target mold is abnormal.
Optionally, before acquiring the template image and the image to be detected of the target mold, the method includes:
determining a preselected area of the template image according to the framing operation of a user on the target mold in the template image on a human-computer interaction interface;
and carrying out boundary expansion on the preselected area to obtain an interested area of the template image.
Optionally, the performing boundary expansion on the preselected region to obtain the region of interest of the template image includes:
if the number of the preselected areas is one, performing boundary expansion on the preselected areas based on the minimum circumscribed rectangle of the preselected areas to obtain the interested areas;
and if the number of the preselected areas is multiple, performing boundary expansion on the preselected areas based on the minimum circumscribed rectangles of the preselected areas to obtain the interested areas.
Optionally, before the image to be detected is identified and located by using a pyramid template matching algorithm according to the region of interest of the template image to obtain the target region of the image to be detected, the method further includes:
and carrying out self-adaptive median filtering processing on the template image and the image to be detected.
Optionally, the method further comprises:
and if the target mold is determined to be abnormal, alarming.
In a second aspect, an embodiment of the present application provides a device for monitoring a state of a mold, including:
the image acquisition module is used for acquiring a template image and an image to be detected of the target mold; the template image is an image of the target mold in a normal state, and the template image comprises an interested area containing the image of the target mold;
the image processing module is used for identifying and positioning the image to be detected by adopting a pyramid template matching algorithm according to the region of interest of the template image to obtain a target region of the image to be detected; determining a sensitive sub-region contained in the target region according to the pixel difference between the target region and the region of interest; and determining whether the target mold is abnormal or not according to the area of the sensitive subarea.
In a third aspect, an embodiment of the present application provides an industrial personal computer, which includes a memory, a processor, and a computer program that is stored in the memory and is executable on the processor, and when the processor executes the computer program, the method for monitoring the state of the mold according to the first aspect is implemented.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the method for monitoring the state of a mold according to the first aspect.
In a fifth aspect, an embodiment of the present application provides a system for monitoring a state of a mold, including:
the visual imaging assembly is used for acquiring a template image and an image to be detected of an article to be detected;
the industrial personal computer comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, and the processor executes the program to realize the state monitoring method of the mold according to the first aspect.
Optionally, the visual imaging assembly comprises: industrial cameras, imaging lenses, infrared light sources and infrared filters.
Optionally, the chip adopted by the industrial camera is a black and white photosensitive chip.
Optionally, the imaging lens is a zoom lens.
Optionally, the industrial personal computer further comprises:
and the human-computer interaction interface is used for displaying information and acquiring an operation instruction.
According to the method, the device, the industrial personal computer, the storage medium and the system for monitoring the state of the mold, the template image and the image to be detected of the target mold are obtained, the template image is the image of the target mold in a normal state, and the template image comprises the region of interest containing the image of the target mold; according to the interesting region of the template image, identifying and positioning the image to be detected by adopting a pyramid template matching algorithm to obtain a target region of the image to be detected; determining a sensitive subarea contained in the target area according to the pixel difference between the target area and the interested area; and determining whether the target mold is abnormal or not according to the area of the sensitive subarea, so that the automatic monitoring of the mold quality is realized, the real-time performance and the accuracy of the mold abnormality detection are considered, and the mold monitoring quality and the production efficiency are improved.
Drawings
Fig. 1 is a schematic flowchart of a method for monitoring a state of a mold according to an embodiment of the present disclosure;
fig. 2 is a schematic implementation flow diagram of a method for monitoring a state of a mold in an actual scene according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a pixel difference map according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a sensitive sub-region provided in an embodiment of the present application;
FIG. 5 is a logic diagram of anomaly detection provided in accordance with an embodiment of the present invention;
fig. 6 is a schematic flowchart of a template image preprocessing process provided in the second embodiment of the present application;
fig. 7 is a schematic structural diagram of a state monitoring device for a mold according to a third embodiment of the present application;
fig. 8 is a schematic structural diagram of an industrial personal computer provided in the fourth embodiment of the present application;
fig. 9 is a schematic structural diagram of a system for monitoring a state of a mold according to a sixth embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
The main ideas of the technical scheme are as follows: the embodiment of the application provides a technical scheme for monitoring the state of a mold, which comprises the steps of determining an interested area of a template image, determining a target area of an image to be detected by adopting a pyramid template matching algorithm, improving the positioning and identifying speed of the target area, further improving the detection speed of the state of the mold, and performing abnormal state detection on the mold by determining a sensitive subarea and based on the area of the sensitive subarea, so that the influence of interference factors is considered, and the detection precision of the state of the mold is improved.
Example one
Fig. 1 is a schematic flow chart of a method for monitoring a state of a mold according to an embodiment of the present disclosure, where the method according to the present disclosure may be executed by a device for monitoring a state of a mold according to an embodiment of the present disclosure, and the device may be implemented by software and/or hardware and may be integrated in an industrial personal computer. As shown in fig. 1, the method for monitoring the state of the mold according to the present embodiment includes:
s101, obtaining a template image and an image to be detected of the target mold.
In this step, when the state of the target mold needs to be detected, a template image and an image to be detected of the same target mold, which are acquired by the visual imaging assembly, are obtained, wherein the template image is an image of the target mold in a normal state, that is, when the quality of the target mold is good and no residue exists by the visual imaging assembly, the acquired image of the target mold can be processed in advance and stored according to an identifier (such as a number) of the target mold, and when the state of the mold needs to be monitored, a corresponding template image is extracted from a database storing the template image. The image to be detected is an image used for determining whether the mold has abnormal state at present, and is a real-time image of the target mold acquired and uploaded by the visual imaging assembly.
It should be noted that, in order to improve the speed of positioning and identifying the target mold in the image to be detected, in this embodiment, the template image is processed in advance, and a region of interest (ROI) of the template image is determined, and accordingly, in this step, the acquired template image includes the region of interest including the image of the target mold. It is to be understood that, since the main purpose of the present embodiment is to perform state monitoring on the mold, the region of interest in the present embodiment is mainly the region where the image of the target mold is located. The interesting area of the template image is determined in advance, and the target area is searched based on the interesting area, so that the processing speed of the image is improved, and the detection speed of the state of the target mold is improved.
In addition, in this embodiment, a timing for performing state detection on the target mold may be set in advance, for example, after the mold is opened or the product is taken out each time, an image acquisition instruction is sent to the visual imaging component, so that the visual imaging component acquires an image to be detected of the target mold, thereby triggering the mold state detection process of this application. It can be understood that the template images for monitoring the state of the mold at different times may be different, for example, the template image used for detecting the state after the mold is opened (for convenience of distinguishing, the template image is called a first template image) and the template image used for detecting the state after the product is taken out (for convenience of distinguishing, the template image is called a second template image) are respectively stored, and the states of the mold in different production stages are respectively detected according to the first template image and the second template image, so that the state of the mold can be better monitored, the quality of the mold is ensured, the production process is not interrupted, and the production efficiency is improved.
Fig. 2 is a schematic implementation flow diagram of a method for monitoring a state of a mold in an actual scene according to an embodiment of the present disclosure. The monitor monitoring in fig. 2 means that whether there is an abnormality in the target mold is determined by the technical solution of the present embodiment. As can be seen from FIG. 2, the technical scheme of the application can be applied to different stages such as after starting detection, after die opening and after product taking out, and the state monitoring of the die at each stage of product production is realized, so that the die is protected better.
S102, identifying and positioning the image to be detected by adopting a pyramid template matching algorithm according to the region of interest of the template image to obtain a target region of the image to be detected.
In actual working conditions, the aging of the injection molding machine causes the mold opening position of the mold to be unfixed, and a slight deviation exists, so that the deviation exists on the imaging surface of the camera. Therefore, in order to ensure the accuracy and speed of the identification and positioning, in this step, a pyramid template matching algorithm is adopted, and based on the region of interest of the template image of the target mold obtained in S101, the image to be detected is identified and positioned, so as to obtain the target region of the image to be detected. The target area is an image area matched with the interested area of the template image on the image to be detected, and correspondingly, the target area is also the area where the image of the target mold is located.
Specifically, when a target region is identified and positioned, in the step, an ROI of a template image is used as a sliding window, sliding is started from an original point of an image to be detected, the image to be detected is traversed, a step length is slid every time, the similarity between the ROI and a region covered by the ROI on the image to be detected is calculated, the maximum value of the similarity is determined, and a region corresponding to the maximum value of the similarity is determined as the target region. Since the pyramid algorithm is capable of creating images of multiple resolutions, the bottom of the pyramid is the high resolution image of the image to be processed, while the top is the low resolution image. Thus, the higher the number of layers into the pyramid, the lower the size and resolution. And the accurate positioning can be carried out by fast matching of low resolution, layer-by-layer mapping and positioning until the high resolution image at the bottommost layer is obtained.
S103, determining a sensitive sub-area contained in the target area according to the pixel difference between the target area and the interested area.
In this embodiment, the identification and positioning of the target area is the first step of performing state detection on the target mold, and then it is necessary to determine whether the target mold has a state abnormality based on the target area, for the mold, common state abnormalities include article residue, slide misalignment, poor mold release, and the like, and accordingly, a pixel difference exists between the acquired image and the image in a normal state. Therefore, in the step, the sensitive subarea contained in the target area is determined according to the difference between the pixel points of the target area and the interesting area, so that the purpose of determining whether the target mould is abnormal is achieved.
In a possible implementation manner, in this step, a pixel difference image is generated based on a pixel difference between the target region and the region of interest, and the pixel difference image includes pixel difference values of corresponding pixel points in the target region and the region of interest; and determining a sensitive subarea contained in the target area according to the pixel difference value and the sensitivity threshold value in the pixel difference image.
Exemplarily, fig. 3 is a schematic diagram of a pixel difference map provided in an embodiment of the present application, taking that pixels of an interested area and a target area are both 10 × 10 as an example, as shown in fig. 3, each point on the pixel difference map is called a pixel difference point, and on the pixel difference map, a numerical value of each pixel difference point is an absolute value of a difference between pixel values (such as gray values) of corresponding pixels on the target area and the interested area, that is, a pixel difference value.
The sensitivity threshold is a preset value, and is used to determine whether the pixel difference between the target region and the region of interest corresponding to the pixel difference point needs to be paid particular attention, and the specific value of the sensitivity threshold may be set according to an actual working condition or related experience, which is not limited here.
It can be understood that due to factors such as external environment and camera performance, there may be some pixel differences in the pictures acquired at different times for the same object, regardless of the state of the mold, and these pixel differences are often within a small range, so in this embodiment, the influence caused by these factors is removed by setting the sensitivity threshold. Correspondingly, the sensitive subregion is a region formed by pixel difference points of which the pixel difference values are greater than the sensitivity threshold value on the pixel difference image, and in order to facilitate the distinction, in this embodiment, the pixel difference points of which the pixel difference values are greater than the sensitivity threshold value on the pixel difference image are called target pixel difference points.
In one possible implementation, a target pixel difference point, of which the pixel difference value is greater than a sensitivity threshold, is determined on the pixel difference image; and determining a sensitive subarea contained in the target area according to the position relation between the target pixel differential points.
The position relationship between the target pixel differential points refers to the adjacent relationship between the target pixel differential points. Correspondingly, in this step, the region formed by the target pixel differential points having the adjacent relationship is determined as a sensitive subregion, and thus, it can be seen that each pixel differential point in the sensitive subregion is a target pixel differential point, that is, the pixel difference value of each pixel differential point in the sensitive subregion is greater than the sensitivity threshold.
Exemplarily, assuming that the sensitivity threshold is 20, replacing a numerical value of a pixel difference point, whose pixel difference value is greater than 20, on the pixel difference map with 1, and replacing a numerical value of a pixel difference point, whose pixel difference value is less than or equal to 20, on the pixel difference map with 0, to obtain a pixel difference map represented by 1 and 0, and determining a sensitive subregion included in the target region according to the pixel difference point, whose pixel difference value is 1, which has an adjacent relationship, fig. 4 is a schematic diagram of the sensitive subregion provided in this application, for example, as shown in fig. 4, a gray region in the map is a sensitive subregion, and 5 sensitive subregions in fig. 4 are respectively denoted as P1, P2, P3, P4, and P5.
And S104, determining whether the target mold is abnormal or not according to the area of the sensitive subarea.
In this step, the area of each sensitive subregion determined in S103 is determined, and whether the target mold is abnormal or not is determined according to the determined area of the sensitive subregion, thereby achieving the purpose of detecting the mold state.
Illustratively, in the present embodiment, the area of the sensitive sub-region is the number of pixel difference points included in the sensitive sub-region, and accordingly, the areas of the sensitive sub-regions P1, P2, P3, P4 and P5 in fig. 4 are 5, 12, 6, 3 and 1, respectively.
In a possible implementation manner, fig. 5 is a logic diagram of anomaly detection provided in an embodiment of the present application, in this step, it is determined whether an anomaly exists in a target mold through the determination mechanism shown in fig. 5, specifically, it is first determined whether a single sensitive sub-region having an area greater than a first area threshold (the first area threshold is denoted as S1 in fig. 5) exists in the target region; if so, determining that the target mold is abnormal; if not, further determining whether the total area of the sensitive sub-regions is greater than a second area threshold (fig. 5 second area threshold is denoted as S2); if so, determining that the target mold has an abnormality.
For example, if the first area threshold is set to 10 and the second area threshold is set to 30 in advance, since the area of P2 is 12, which is larger than the first area threshold, it can be directly determined that there is an abnormality in the target mold, and the total area detection is not required. If the preset first area threshold is 15 and the second area threshold is 25, single area detection is firstly carried out, the areas of P1, P2, P3, P4 and P5 are all determined to be smaller than the first area threshold, further, total area detection is carried out, the total areas of P1, P2, P3, P4 and P5 are found to be 5 + 12 + 6 + 3 + 1-27 and larger than the second area threshold, and therefore the target mold is determined to be abnormal.
If the number of sensitive subregions included in the target region determined in step S103 is 0, it is directly determined that there is no abnormality in the target mold. If the number of the sensitive subregions included in the target region determined in the S103 is 1, only whether the area of the sensitive subregion is larger than a first area threshold value or not needs to be judged in the S104, and if the area of the sensitive subregion is smaller than or equal to the first area threshold value, the target mold is determined to be normal; if the number of the sensitive sub-regions included in the target region determined in S103 is greater than 1, strictly performing determination according to the determination logic shown in fig. 5, and determining that the target mold is normal only when the area of each sensitive sub-region is less than or equal to the first area threshold and the sum of the areas of the sensitive sub-regions is less than or equal to the second area threshold; through the judgment mechanism, the accuracy of anomaly detection on the target die can be effectively ensured.
Optionally, after S104, the method of this embodiment further includes: and if the target mold is determined to be abnormal, alarming. For example, the control of the flashing of the human-computer interaction interface, the control of the alarm for alarming, the control of the sending of a prompt message to user equipment (such as a mobile phone) and the like is carried out to prompt a user to carry out manual intervention in time, or the direct control of the stop of the injection molding machine and the like, thereby realizing the protection of the target mold.
In the embodiment, a template image and an image to be detected of a target mold are obtained, wherein the template image is an image of the target mold in a normal state, and the template image comprises an interested area containing the image of the target mold; according to the interesting region of the template image, identifying and positioning the image to be detected by adopting a pyramid template matching algorithm to obtain a target region of the image to be detected; determining a sensitive subarea contained in a target area according to the pixel difference between the target area and the interesting area; according to the area of the sensitive subarea, whether the target mold is abnormal or not is determined, the automatic monitoring of the mold quality is realized, the real-time performance and the accuracy of the mold abnormality detection are considered, the mold quality can be accurately monitored, the production efficiency is improved, and the beneficial economic benefit and the social value are achieved.
Example two
The following will describe a process of preprocessing a template image of a target mold by a specific embodiment, and exemplarily, fig. 6 is a schematic flow chart of a process of preprocessing a template image provided in a second embodiment of the present application, as shown in fig. 6, in this embodiment, the preprocessing of the template image includes:
s201, determining a preselected area of the template image according to the framing operation of a user on the target mold in the template image in the human-computer interaction interface.
The industrial personal computer provided by the embodiment comprises a human-computer interaction interface, and a user can check the image acquired by the visual imaging assembly through the human-computer interaction interface and perform corresponding operation on the image. In a possible implementation manner, in this embodiment, on the basis of the frame selection operation performed on the template image by the user, the region of interest of the template image is determined, so as to ensure the accuracy of the determined target region. Specifically, in this step, a preselected area of the template image is determined by recognizing a frame selection operation performed by a user on a target mold in the template image at a human-computer interaction interface.
It should be noted that, in order to adapt to different shapes of the mold, in this embodiment, a user is supported to perform a framing operation of any shape on the template image, such as a circle, a rectangle, a polygon, and the like, and at the same time, in this embodiment, the user is also supported to select a plurality of pre-regions to meet the use requirements in different scenes, so that the scene adaptability of the technical scheme provided by the present application is enhanced.
It can be understood that, in this embodiment, the prompt message, such as "please frame the position of the selected mold", may be displayed on the human-computer interaction interface to guide the user's operation, so as to ensure that the image of the target mold is included in the determined preselected area. In addition, in the embodiment, selection icons in different shapes can be arranged on the human-computer interaction interface so that a user can select and use the selection icons when performing frame selection operation, and the convenience of operation is improved.
S202, carrying out boundary expansion on the preselected area to obtain an interested area of the template image.
In this step, in order to better obtain the edge, the boundary extension is performed on the preselected area obtained in S201, so as to obtain the region of interest of the template image.
Optionally, in this step, a fixed value filling mode may be adopted to excessively enlarge the selected area to a certain extent, so as to accurately identify the target area in the image to be detected in the subsequent step.
In a possible implementation manner, if there is one preselected region, based on the minimum circumscribed rectangle (or minimum circumscribed circle, etc.) of the preselected region, performing boundary expansion on the preselected region according to a preset filling value to obtain an interested region; if the number of the preselected areas is multiple, based on the minimum circumscribed rectangle (or the minimum circumscribed circle and the like) of all the preselected areas, carrying out boundary expansion on the preselected areas according to a preset filling value to obtain the interested areas.
And S203, performing adaptive median filtering processing on the template image.
In this step, in order to better protect the relevant details in the template image, after the determination of the region of interest is completed, the template image is subjected to adaptive median filtering.
Optionally, in an embodiment, an adaptive median filter may be set in advance, and the filtering process on the image may be implemented by dynamically changing the window size of the median filter through the adaptive median filter according to a preset condition. The adaptive filter can filter salt and pepper noise with high probability, can better protect the details of the image, and is beneficial to improving the speed of subsequent image processing.
It can be understood that before the image to be detected is subjected to positioning identification, the image to be detected may also be subjected to adaptive median filtering processing as required.
In the embodiment, the pre-selection area of the template image is determined through the frame selection operation of the target mold in the template image by the user at the human-computer interaction interface, the boundary expansion is carried out on the pre-selection area to obtain the region of interest of the template image, and the adaptive median filtering processing is carried out on the template image, so that the preprocessing of the template image is realized, the region of interest of the template image is determined, the unnecessary noise interference is reduced, the speed and the precision of the subsequent image processing are improved, and the quality of the abnormal detection of the mold is improved.
EXAMPLE III
Fig. 7 is a schematic structural diagram of a state monitoring device for a mold according to a third embodiment of the present application, and as shown in fig. 7, a state monitoring device 10 for a mold according to the third embodiment of the present application includes:
an image acquisition module 11 and an image processing module 12.
The image acquisition module 11 is used for acquiring a template image and an image to be detected of a target mold; the template image is an image of the target mold in a normal state, and the template image comprises an interested area containing the image of the target mold;
the image processing module 12 is configured to identify and locate the image to be detected by using a pyramid template matching algorithm according to the region of interest of the template image, so as to obtain a target region of the image to be detected; determining a sensitive sub-region contained in the target region according to the pixel difference between the target region and the region of interest; and determining whether the target mold is abnormal or not according to the area of the sensitive subarea.
Optionally, the image processing module 12 is specifically configured to:
generating a pixel difference image based on the pixel difference between the target area and the interested area, wherein the pixel difference image comprises pixel difference values of corresponding pixel points in the target area and the interested area;
and determining a sensitive subarea contained in the target area according to the pixel difference value and the sensitivity threshold value in the pixel difference image.
Optionally, the image processing module 12 is specifically configured to:
determining a target pixel difference point on the pixel difference image, wherein the pixel difference value is larger than the sensitivity threshold value;
and determining a sensitive subarea contained in the target area according to the position relation between the target pixel differential points, wherein each pixel differential point in the sensitive subarea is a target pixel differential point.
Optionally, the image processing module 12 is specifically configured to:
determining whether there is a single sensitive subregion in the target region that has an area greater than a first area threshold; if so, determining that the target mold is abnormal;
if not, determining whether the total area of each sensitive subarea is larger than a second area threshold value; and if so, determining that the target mold is abnormal.
Optionally, the image processing module 12 is further configured to:
determining a preselected area of the template image according to the framing operation of a user on the target mold in the template image on a human-computer interaction interface;
and carrying out boundary expansion on the preselected area to obtain an interested area of the template image.
Optionally, the image processing module 12 is specifically configured to:
if the number of the preselected areas is one, performing boundary expansion on the preselected areas based on the minimum circumscribed rectangle of the preselected areas to obtain the interested areas;
and if the number of the preselected areas is multiple, performing boundary expansion on the preselected areas based on the minimum circumscribed rectangles of the preselected areas to obtain the interested areas.
Optionally, the image processing module 12 is further configured to:
and carrying out self-adaptive median filtering processing on the template image and the image to be detected.
Optionally, the image processing module 12 is further configured to:
and if the target mold is determined to be abnormal, alarming.
The device for monitoring the state of the mold, provided by the embodiment, can execute the method for monitoring the state of the mold, provided by the embodiment of the method, and has the corresponding functional modules and beneficial effects of the execution method. The implementation principle and technical effect of this embodiment are similar to those of the above method embodiments, and are not described in detail here.
Example four
Fig. 8 is a schematic structural diagram of an industrial personal computer provided in a fourth embodiment of the present application, and as shown in fig. 8, the industrial personal computer 20 includes a memory 21, a processor 22, and a computer program stored in the memory and capable of running on the processor; the number of the processors 22 of the industrial personal computer 20 can be one or more, and one processor 22 is taken as an example in fig. 8; the processor 22 and the memory 21 in the industrial personal computer 20 may be connected by a bus or other means, and fig. 8 illustrates the connection by the bus as an example.
The memory 21 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the image acquisition module 11 and the image processing module 12 in the embodiments of the present application. The processor 22 executes software programs, instructions and modules stored in the memory 21, thereby applying various functions of the industrial personal computer and processing data, that is, implementing the above-described method for monitoring the state of the mold.
The memory 21 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 21 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 21 may further include memory located remotely from processor 22, which may be connected to an industrial personal computer through a grid. Examples of such a mesh include, but are not limited to, the internet, an intranet, a local area network, a mobile communications network, and combinations thereof.
EXAMPLE five
A fifth embodiment of the present application further provides a computer-readable storage medium having stored thereon a computer program which, when executed by a computer processor, is configured to perform a method of monitoring a condition of a mold, the method comprising:
acquiring a template image and an image to be detected of a target mold; the template image is an image of the target mold in a normal state, and the template image comprises an interested area containing the image of the target mold;
identifying and positioning the image to be detected by adopting a pyramid template matching algorithm according to the region of interest of the template image to obtain a target region of the image to be detected;
determining a sensitive sub-region contained in the target region according to the pixel difference between the target region and the region of interest;
and determining whether the target mold is abnormal or not according to the area of the sensitive subarea.
Of course, the computer program of the computer-readable storage medium provided in this embodiment of the present application is not limited to the method operations described above, and may also perform related operations in the method for monitoring the state of the mold provided in any embodiment of the present application.
From the above description of the embodiments, it is obvious for those skilled in the art that the present application can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a grid device) to execute the methods described in the embodiments of the present application.
EXAMPLE six
Fig. 9 is a schematic structural diagram of a state monitoring system of a mold according to a sixth embodiment of the present application, and as shown in fig. 9, a state detection system 30 of a mold according to the present embodiment includes a visual imaging assembly 31 and an industrial personal computer 20 according to a fourth embodiment.
The visual imaging component 31 is used for acquiring a template image of an article to be detected and an image to be detected.
Optionally, in this embodiment, the visual imaging assembly includes: the specific situations of the industrial camera, the imaging lens, the infrared light source and the infrared filter used in the embodiment of the present application are described below:
(1) industrial camera
In order to better cope with application scenarios, in the embodiment, industrial cameras with multiple resolutions, such as 100 ten thousand pixel resolutions, 300 ten thousand pixel resolutions, 500 ten thousand pixel resolutions, and the like, may be provided, so as to select a camera with a corresponding resolution according to a detection object and an accuracy requirement in different application scenarios, thereby improving the scene adaptability of the system.
Exemplarily, the data transmission interface of the industrial camera in this embodiment is USB 3.0, and the interface can not only perform data transmission, but also supply power to the industrial camera at the same time, does not need an additional power supply, and has the characteristics of simple use, high transmission speed, good compatibility, and the like. The lens interface at the front end of the camera is a C port and is used as a universal interface, so that the lens can be conveniently replaced.
Optionally, the chip of the industrial camera used in this embodiment is a black and white photosensitive chip. The reason for the selection is as follows: firstly, in the application scene of mold monitoring, no special requirements on color are needed; secondly, because of the cameras with the same resolution, the black-and-white photosensitive chip is higher than the color photosensitive chip in precision, and especially for image edge detection, the black-and-white photosensitive chip has better imaging effect; finally, in the image processing process, the black and white industrial camera obtains gray information which can be directly processed.
(2) Imaging lens
Because the camera lens directly determines the size and the definition of the visual field, whether the camera lens is suitable for directly determining whether the visual imaging module can acquire images with higher imaging quality or not is determined. In practical application scenarios, due to the specificity, irregularity and size factors of different products and molds, the fixed-focus lens is not suitable. Therefore, the system selects a zoom lens, exemplarily, the focal length of the zoom lens in the embodiment is 12-50mm, and the detection object can be in a proper size and brightness in cooperation with the adjustment of the aperture and the definition.
(3) Infrared light source and infrared filter
In order to avoid the influence of external illumination on subsequent image processing, an infrared light source is adopted for illumination, and an infrared filter is added at the front end of the camera to ensure that infrared light enters the camera, so that the anti-interference performance of the monitoring system on the environment is improved.
Optionally, the industrial personal computer 20 further comprises:
and the human-computer interaction interface is used for displaying information and acquiring an operation instruction. Illustratively, the human-computer interaction interface includes the following functions:
and (3) displaying a monitoring state: and displaying the current working state in real time.
And (3) displaying the production state: and counting the number of production detections after the last zero clearing.
Camera setting: the user can set the exposure time of the camera, the gain of the camera, the photographing delay of the product, the rechecking delay of the product, the photographing delay of the die cavity, the rechecking delay of the template and the like on the human-computer interaction interface so as to set the camera.
Setting parameters: the method mainly comprises the steps of setting detection parameters, including detection times, detection delay, administrator password setting, time setting, screen calibration and the like.
Sampling a template: and the upper left corner status bar of the human-computer interaction interface can display the start sampling, and can perform automatic sampling after waiting for the signals of closing the safety door and completing the mold opening.
Setting a template: and manually shooting to select the template, drawing a monitoring range, adding and deleting the number of the templates and the like.
Sensitivity: and setting the sensitivity, area control and detection verification functions of the detection area. Wherein the sensitivity and area can be set for each region, directly determining the range of defect detection. The detection and verification can bypass signals, and the state of the die is manually monitored.
Logging: and recording an operation log and checking normal/abnormal pictures, and mainly used for checking alarm pictures and alarm positions, analyzing alarm reasons and the like.
Signal display: and displaying the current signal transmission condition and the next signal transmission.
It should be noted that, in the embodiment of the device for monitoring the state of the mold, the units and modules included in the device are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, specific names of the functional units are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the application.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.
Claims (16)
1. A method of monitoring a condition of a mold, comprising:
acquiring a template image and an image to be detected of a target mold; the template image is an image of the target mold in a normal state, and the template image comprises an interested area containing the image of the target mold;
identifying and positioning the image to be detected by adopting a pyramid template matching algorithm according to the region of interest of the template image to obtain a target region of the image to be detected;
determining a sensitive sub-region contained in the target region according to the pixel difference between the target region and the region of interest;
and determining whether the target mold is abnormal or not according to the area of the sensitive subarea.
2. The method according to claim 1, wherein the determining a sensitive sub-region contained in the target region according to the pixel difference existing between the target region and the region of interest comprises:
generating a pixel difference image based on the pixel difference between the target area and the interested area, wherein the pixel difference image comprises pixel difference values of corresponding pixel points in the target area and the interested area;
and determining a sensitive subarea contained in the target area according to the pixel difference value and the sensitivity threshold value in the pixel difference image.
3. The method of claim 2, wherein the determining the sensitive sub-region included in the target region according to the pixel difference value and the sensitivity threshold in the pixel difference map comprises:
determining a target pixel difference point on the pixel difference image, wherein the pixel difference value is larger than the sensitivity threshold value;
and determining a sensitive subarea contained in the target area according to the position relation between the target pixel differential points, wherein each pixel differential point in the sensitive subarea is a target pixel differential point.
4. The method of claim 1, wherein determining whether the target mold is abnormal based on the area of the sensitive subregion comprises:
determining whether there is a single sensitive subregion in the target region that has an area greater than a first area threshold; if so, determining that the target mold is abnormal;
if not, determining whether the total area of each sensitive subarea is larger than a second area threshold value; and if so, determining that the target mold is abnormal.
5. The method according to any one of claims 1-4, wherein prior to acquiring the template image and the image to be detected of the target mold, the method comprises:
determining a preselected area of the template image according to the framing operation of a user on the target mold in the template image on a human-computer interaction interface;
and carrying out boundary expansion on the preselected area to obtain an interested area of the template image.
6. The method of claim 5, wherein said boundary-extending the preselected region to obtain a region of interest of the template image comprises:
if the number of the preselected areas is one, performing boundary expansion on the preselected areas based on the minimum circumscribed rectangle of the preselected areas to obtain the interested areas;
and if the number of the preselected areas is multiple, performing boundary expansion on the preselected areas based on the minimum circumscribed rectangles of the preselected areas to obtain the interested areas.
7. The method according to any one of claims 1 to 4, wherein before the image to be detected is identified and located by using a pyramid template matching algorithm according to the region of interest of the template image to obtain the target region of the image to be detected, the method further comprises:
and carrying out self-adaptive median filtering processing on the template image and the image to be detected.
8. The method according to any one of claims 1-4, further comprising:
and if the target mold is determined to be abnormal, alarming.
9. A state monitoring device of a mold, characterized by comprising:
the image acquisition module is used for acquiring a template image and an image to be detected of the target mold; the template image is an image of the target mold in a normal state, and the template image comprises an interested area containing the image of the target mold;
the image processing module is used for identifying and positioning the image to be detected by adopting a pyramid template matching algorithm according to the region of interest of the template image to obtain a target region of the image to be detected; determining a sensitive sub-region contained in the target region according to the pixel difference between the target region and the region of interest; and determining whether the target mold is abnormal or not according to the area of the sensitive subarea.
10. An industrial personal computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of monitoring the condition of a mold according to any one of claims 1 to 8 when executing the program.
11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of monitoring the condition of a mold according to any one of claims 1 to 8.
12. A system for monitoring the condition of a mold, comprising:
the visual imaging assembly is used for acquiring a template image and an image to be detected of an article to be detected;
an industrial personal computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of monitoring the condition of a mold according to any one of claims 1 to 8 when executing the program.
13. The system of claim 12, wherein the visual imaging component comprises: industrial cameras, imaging lenses, infrared light sources and infrared filters.
14. The system of claim 13, wherein the chip used by the industrial camera is a black and white light sensitive chip.
15. The system of claim 13, wherein the imaging lens is a variable focus lens.
16. The system of claim 12, wherein the industrial personal computer further comprises:
and the human-computer interaction interface is used for displaying information and acquiring an operation instruction.
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| CN202110178510.8A CN112837302B (en) | 2021-02-09 | 2021-02-09 | Method and device for monitoring state of die, industrial personal computer, storage medium and system |
| PCT/CN2021/097967 WO2022170702A1 (en) | 2021-02-09 | 2021-06-02 | Mold state monitoring method, apparatus and system, and industrial personal computer and storage medium |
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| CN202110178510.8A CN112837302B (en) | 2021-02-09 | 2021-02-09 | Method and device for monitoring state of die, industrial personal computer, storage medium and system |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113469974A (en) * | 2021-07-05 | 2021-10-01 | 天津市三特电子有限公司 | Method and system for monitoring grate plate state of pellet chain grate |
| CN113592831A (en) * | 2021-08-05 | 2021-11-02 | 北京方正印捷数码技术有限公司 | Method and device for detecting printing error and storage medium |
| CN114299026A (en) * | 2021-12-29 | 2022-04-08 | 广东利元亨智能装备股份有限公司 | Detection method, detection device, electronic equipment and readable storage medium |
| WO2022170702A1 (en) * | 2021-02-09 | 2022-08-18 | 广东拓斯达科技股份有限公司 | Mold state monitoring method, apparatus and system, and industrial personal computer and storage medium |
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| CN116935079A (en) * | 2023-09-07 | 2023-10-24 | 深圳金三立视频科技股份有限公司 | Linear switch state monitoring method and terminal based on vision |
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Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116309602B (en) * | 2023-05-24 | 2023-08-04 | 济南章力机械有限公司 | Numerical control drilling and milling machine working state detection method based on machine vision |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9349076B1 (en) * | 2013-12-20 | 2016-05-24 | Amazon Technologies, Inc. | Template-based target object detection in an image |
| CN107336417A (en) * | 2017-06-13 | 2017-11-10 | 上海斐讯数据通信技术有限公司 | A kind of mold protecting method and system based on machine vision |
| CN110426395A (en) * | 2019-07-02 | 2019-11-08 | 广州大学 | A kind of solar energy EL cell silicon chip surface inspecting method and device |
| CN111474184A (en) * | 2020-04-17 | 2020-07-31 | 河海大学常州校区 | AOI character defect detection method and device based on industrial machine vision |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112837302B (en) * | 2021-02-09 | 2024-02-13 | 广东拓斯达科技股份有限公司 | Method and device for monitoring state of die, industrial personal computer, storage medium and system |
-
2021
- 2021-02-09 CN CN202110178510.8A patent/CN112837302B/en active Active
- 2021-06-02 WO PCT/CN2021/097967 patent/WO2022170702A1/en unknown
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9349076B1 (en) * | 2013-12-20 | 2016-05-24 | Amazon Technologies, Inc. | Template-based target object detection in an image |
| CN107336417A (en) * | 2017-06-13 | 2017-11-10 | 上海斐讯数据通信技术有限公司 | A kind of mold protecting method and system based on machine vision |
| CN110426395A (en) * | 2019-07-02 | 2019-11-08 | 广州大学 | A kind of solar energy EL cell silicon chip surface inspecting method and device |
| CN111474184A (en) * | 2020-04-17 | 2020-07-31 | 河海大学常州校区 | AOI character defect detection method and device based on industrial machine vision |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN113469974B (en) * | 2021-07-05 | 2022-12-02 | 天津市三特电子有限公司 | Method and system for monitoring state of grate plate of pellet grate |
| CN113469974A (en) * | 2021-07-05 | 2021-10-01 | 天津市三特电子有限公司 | Method and system for monitoring grate plate state of pellet chain grate |
| CN113592831A (en) * | 2021-08-05 | 2021-11-02 | 北京方正印捷数码技术有限公司 | Method and device for detecting printing error and storage medium |
| CN113592831B (en) * | 2021-08-05 | 2024-03-19 | 北京方正印捷数码技术有限公司 | Printing error detection method, device and storage medium |
| CN114299026A (en) * | 2021-12-29 | 2022-04-08 | 广东利元亨智能装备股份有限公司 | Detection method, detection device, electronic equipment and readable storage medium |
| CN115049713A (en) * | 2022-08-11 | 2022-09-13 | 武汉中导光电设备有限公司 | Image registration method, device, equipment and readable storage medium |
| CN115049713B (en) * | 2022-08-11 | 2022-11-25 | 武汉中导光电设备有限公司 | Image registration method, device, equipment and readable storage medium |
| CN115953397A (en) * | 2023-03-13 | 2023-04-11 | 山东金帝精密机械科技股份有限公司 | Method and equipment for monitoring process preparation flow of conical bearing retainer |
| CN116935079A (en) * | 2023-09-07 | 2023-10-24 | 深圳金三立视频科技股份有限公司 | Linear switch state monitoring method and terminal based on vision |
| CN116935079B (en) * | 2023-09-07 | 2024-02-20 | 深圳金三立视频科技股份有限公司 | Linear switch state monitoring method and terminal based on vision |
| CN117095000A (en) * | 2023-10-19 | 2023-11-21 | 杭州和利时自动化有限公司 | Equipment detection method and device |
| CN117095000B (en) * | 2023-10-19 | 2024-01-26 | 杭州和利时自动化有限公司 | Equipment detection method and device |
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| CN112837302B (en) | 2024-02-13 |
| WO2022170702A1 (en) | 2022-08-18 |
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