CN112837302B - Method and device for monitoring state of die, industrial personal computer, storage medium and system - Google Patents
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Abstract
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 die, wherein a template image and an image to be detected of a target die are acquired, the template image is an image of the target die in a normal state, and the template image comprises an interested region containing the image of the target die; 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 subarea contained in the target area according to pixel differences existing between the target area and the region of interest; according to the area of the sensitive subarea, whether the target die is abnormal or not is determined, automatic monitoring of the die quality is realized, real-time performance and accuracy of die abnormality detection are considered, and die monitoring quality and 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 and a device for monitoring the state of a mold, an industrial personal computer, a storage medium and a system.
Background
In recent years, the industry of automobiles, buildings, household appliances, foods, medicines and the like has increasingly demanded injection molding products, the development and improvement of the level of the whole injection molding and forming technology are promoted, and the development of the injection molding industry is also driven. In the injection molding industry, molds are important molding equipment, and the quality of the molds is directly related to the final quality of products. Because the proportion of the mould in the injection processing production cost is larger, the service life of the mould directly influences the product cost, and in order to protect the mould, the production state of the mould is necessary to be monitored and detected.
The method is based on machine vision to monitor the state of the die, is an effective automatic die monitoring scheme, has strong universality, but has influence on the detection speed and the detection precision along with the increase of the number of collected templates or the change of the die stations, so that the detection speed and the detection precision are considered to be important points of research in die state monitoring.
Disclosure of Invention
The embodiment of the application provides a state monitoring method, device, industrial personal computer, storage medium and system of a die, which can give consideration to the high efficiency of the accuracy of the state monitoring of the die, and improve the production efficiency while protecting the die.
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 die; the template image is an image of the target die in a normal state, and the template image comprises an interested region containing the image of the target die;
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 pixel differences existing between the target region and the region of interest;
and determining whether the target die is abnormal or not according to the area of the sensitive subarea.
Optionally, the determining the sensitive sub-region included in the target region according to the pixel difference existing between the target region and the region of interest includes:
generating a pixel difference map based on pixel differences existing between the target region and the region of interest, wherein the pixel difference map comprises 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 map.
Optionally, 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 includes:
determining a target pixel difference point on the pixel difference map, wherein the pixel difference value of the target pixel difference point is larger than the sensitivity threshold;
and determining a sensitive subarea contained in the target area according to the position relation among target pixel differential points, wherein each pixel differential point in the sensitive subarea is the target pixel differential point.
Optionally, the determining whether the target mold has an abnormality according to the area of the sensitive subarea includes:
determining whether there is a sensitive sub-region in the target region having a single area greater than a first area threshold; if so, determining that the target die is abnormal;
if not, determining whether the total area of each sensitive subarea is greater than a second area threshold; if yes, determining that the target die is abnormal.
Optionally, before the step of 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 frame selection operation of a user on the target mold in the template image at a man-machine interaction interface;
and carrying out boundary expansion on the preselected area to obtain the interested area of the template image.
Optionally, the performing boundary expansion on the pre-selected area to obtain a region of interest of the template image includes:
if the preselected area is one, carrying out boundary expansion on the preselected area based on the minimum circumscribed rectangle of the preselected area to obtain the region of interest;
and if a plurality of the preselected areas are provided, carrying out boundary expansion on the preselected areas based on the minimum circumscribed rectangles of the plurality of the preselected areas to obtain the region of interest.
Optionally, before 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 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 die is determined to be abnormal, alarming.
In a second aspect, an embodiment of the present application provides a state monitoring device for a mold, including:
the image acquisition module is used for acquiring a template image and an image to be detected of the target die; the template image is an image of the target die in a normal state, and the template image comprises an interested region containing the image of the target die;
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 pixel differences existing between the target region and the region of interest; and determining whether the target die 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, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the method for monitoring a state of a mold according to the first aspect when executing the program.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for monitoring the condition of a mold as described in the first aspect above.
In a fifth aspect, embodiments of the present application provide a condition monitoring system for a mold, including:
the visual imaging assembly is used for collecting a template image and an image to be detected of the object to be detected;
the industrial personal computer comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the method for monitoring the state of the die according to the first aspect when executing the program.
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 variable focus lens.
Optionally, the industrial personal computer further includes:
and the man-machine interaction interface is used for information display and operation instruction acquisition.
According to the method, the device, the industrial personal computer, the storage medium and the system for monitoring the state of the die, the template image of the target die and the image to be detected are obtained, wherein the template image is an image of the target die in a normal state, and the template image comprises an interested area containing the image of the target die; 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 subarea contained in the target area according to pixel differences existing between the target area and the region of interest; according to the area of the sensitive subarea, whether the target die is abnormal or not is determined, automatic monitoring of the die quality is realized, real-time performance and accuracy of die abnormality detection are considered, and die monitoring quality and production efficiency are improved.
Drawings
Fig. 1 is a flow chart of a method for monitoring a state of a mold according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of an implementation of the method for monitoring the state of a mold according to the first embodiment of the present application in an actual scenario;
FIG. 3 is a schematic diagram of a pixel differential diagram according to a first embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a sensitive sub-region according to an embodiment of the present disclosure;
FIG. 5 is a logic diagram of anomaly detection according to an embodiment of the present application;
fig. 6 is a flow chart illustrating a preprocessing process of a template image according to a second embodiment of the present application;
fig. 7 is a schematic structural diagram of a device for monitoring the state of a mold according to a third embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of an industrial personal computer according to a fourth embodiment of the present application;
fig. 9 is a schematic structural diagram of a state monitoring system for a mold according to a sixth embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings.
The main idea of the technical scheme of the application is as follows: the embodiment of the application provides a technical scheme for monitoring the state of a die, which is characterized in that a target area of an image to be detected is determined by determining an interested area of a template image and adopting a pyramid template matching algorithm, so that the positioning recognition speed of the target area is improved, the detection speed of the die state is further improved, the abnormal state of the die is detected by determining a sensitive subarea and based on the area of the sensitive subarea, the influence of interference factors is considered, and the detection precision of the die state is improved.
Example 1
For example, fig. 1 is a schematic flow chart of a method for monitoring a state of a mold according to an embodiment of the present application, where the method of the embodiment may be performed by a device for monitoring a state of a mold according to an embodiment of the present application, 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, acquiring a template image and an image to be detected of a target die.
In the step, when the state of the target mold is required to be detected, a template image and an image to be detected of the same target mold, which are acquired by the visual imaging component, are acquired, wherein the template image is an image of the target mold in a normal state, namely, when the quality of the target mold is good and residues do not exist by the visual imaging component, the acquired image of the target mold can be processed in advance, and stored according to the identification (such as the number) of the target mold, and when the state of the mold is required to be detected, the 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 die is abnormal in state currently or not, and is a real-time image of the target die acquired and uploaded by the visual imaging assembly.
In this embodiment, the template image is processed in advance to determine the region of interest (region of interest, ROI) of the template image, and accordingly, in this step, the obtained template image includes the region of interest including the image of the target mold. It will be appreciated that since the main purpose of this embodiment is to perform state monitoring of the mold, the region of interest in this embodiment is mainly the region where the image of the target mold is located. The method has the advantages that the interested region of the template image is determined in advance, and the target region is searched based on the interested region, so that the processing speed of pictures is improved, and the detection speed of the state of the target die is improved.
In addition, in this embodiment, the timing of performing state detection on the target mold may be set in advance, for example, after each mold opening or after the product is taken out, by sending an image acquisition instruction 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 flow of the present 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 distinction, 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 distinction, the template image is called a second template image) are respectively stored, and the states of the mold at 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 flow is ensured not to be interrupted, and the production efficiency is improved.
Fig. 2 is a schematic flow chart of an implementation of the method for monitoring the state of a mold according to the first embodiment of the present application in an actual scenario. The monitor monitoring in fig. 2 is to determine whether there is an abnormality in the target mold by the technical scheme of the present embodiment. As can be seen from fig. 2, the technical solution of the present application may be applied at different stages, such as after start-up detection, after mold opening, and after product removal, to monitor the state of the mold at various stages of product production, so as to better protect the mold.
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 die opening position of the die to be not fixed, and a little deviation exists, so that the deviation exists on the imaging surface of the camera. Therefore, in order to ensure the accuracy and speed of recognition positioning, in the step, a pyramid template matching algorithm is adopted, and recognition positioning is performed on the image to be detected based on the region of interest of the template image of the target mold acquired in the step S101, so that the target region of the image to be detected is obtained. The target area is an image area on the image to be detected, which is matched with the region of interest of the template image, and correspondingly, the target area is also the area where the image of the target mold is located.
The pyramid template matching algorithm is an algorithm for superposing and using a pyramid model and the template matching algorithm, and can play a role in reducing calculation amount, specifically, when a target area is identified and positioned, in the step, the ROI of a template image is taken as a sliding window, the image to be detected is traversed by sliding from the origin of the image to be detected, the similarity between one ROI and the area covered by the ROI on the image to be detected is calculated each time by one step length, the maximum value of the similarity is determined, and the area corresponding to the maximum value of the similarity is determined as the target area. Since the pyramid algorithm is able to create multiple resolution images, the bottom of the pyramid is the high resolution image of the image to be processed and the top is the low resolution image. Thus, the higher the number of layers to the pyramid, the lower the size and resolution. By means of the fast matching of low resolution, layer-by-layer mapping, accurate positioning can be performed until the high resolution image at the bottom layer is positioned.
S103, determining a sensitive subarea contained in the target area according to pixel differences existing between the target area and the region of interest.
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 required to determine whether the target mold has a state abnormality based on the target area, where the common state abnormality includes article residue, slide block dislocation, demolding failure, and the like, and accordingly, there is a pixel difference between the acquired image and the image in the normal state. Therefore, in the step, the sensitive subarea contained in the target area is determined according to the difference between the target area and each pixel point of the interested area, so that the purpose of determining whether the target mould is abnormal or not is achieved.
In one possible implementation manner, in the step, a pixel difference map is generated based on pixel differences existing between the target region and the region of interest, where the pixel difference map 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 map.
For example, fig. 3 is a schematic diagram of a pixel difference map provided in the first embodiment of the present application, taking that pixels of the region of interest and the target region are 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 value of each pixel difference point is an absolute value of a difference value (such as a gray value) between a pixel value (such as a gray value) of a corresponding pixel point on the target region and the region of interest.
The sensitivity threshold is a value set in advance, and is used for determining whether the pixel difference between the target area corresponding to the pixel difference point and the region of interest needs to be paid attention to, and the specific value of the sensitivity threshold can be set according to the actual working condition or related experience, etc., which is not limited herein.
It will be appreciated that due to factors such as the external environment and the camera performance, there may be some pixel differences between the pictures taken at different times on the same object, which are irrelevant to the state of the mold, and these pixel differences are often within a smaller range, so in this embodiment, the influence caused by these factors is removed by setting the sensitivity threshold. Correspondingly, the sensitive sub-region is a region formed by pixel differential points with pixel differential values larger than a sensitivity threshold on the pixel differential map, and in this embodiment, the pixel differential points with pixel differential values larger than the sensitivity threshold on the pixel differential map are called target pixel differential points for convenience of distinction.
In one possible implementation, a target pixel difference point on the pixel difference map, where the pixel difference value is greater than the sensitivity threshold, is determined; and determining a sensitive subarea contained in the target area according to the position relation between the target pixel differential points.
The positional relationship between the target pixel differential points refers to the adjacent relationship between the target pixel differential points. Correspondingly, in the step, the area formed by the target pixel differential points with adjacent relation is determined as a sensitive sub-area, so that each pixel differential point in the sensitive sub-area is the target pixel differential point, namely, the pixel differential value of each pixel differential point in the sensitive sub-area is larger than the sensitivity threshold.
For example, assuming that the sensitivity threshold is 20, the value of the pixel difference point with the pixel difference value greater than 20 on the pixel difference map is replaced with 1, the value of the pixel difference point with the pixel difference value less than or equal to 20 on the pixel difference map is replaced with 0, the pixel difference map represented by 1 and 0 is obtained, and the sensitive subareas included in the target area are determined according to the pixel difference points with the adjacent relation of the pixel difference value of 1, for example, fig. 4 is a schematic diagram of the sensitive subareas provided in the first embodiment of the present application, as shown in fig. 4, gray areas in the map are the sensitive subareas, and 5 sensitive subareas in fig. 4 are respectively marked as P1, P2, P3, P4 and P5.
S104, determining whether the target die is abnormal or not according to the area of the sensitive subarea.
In the step, the area of each sensitive subarea determined in the step S103 is determined, and whether the target die is abnormal or not is determined according to the determined area of the sensitive subarea, so that the purpose of detecting the die state is achieved.
Illustratively, in this embodiment, the area of the sensitive sub-area is the number of pixel differential points included in the sensitive sub-area, and correspondingly, the areas of the sensitive sub-areas 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 schematic diagram of anomaly detection provided in the first embodiment of the present application, in this step, whether an anomaly exists in a target mold is determined by using the determining mechanism shown in fig. 5, specifically, whether a sensitive subarea with a single area greater than a first area threshold (the first area threshold in fig. 5 is denoted as S1) exists in the target area is determined first; if so, determining that the target die is abnormal; if not, further determining whether the total area of each sensitive sub-region is greater than a second area threshold (the second area threshold of FIG. 5 is denoted as S2); if so, determining that the target die is abnormal.
For example, if the first area threshold is set to 10 in advance, the second area threshold is set to 30, and the area of P2 is set to 12 and larger than the first area threshold, it can be directly determined that the target mold is abnormal, 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 performed first to determine that the areas of P1, P2, P3, P4 and P5 are smaller than the first area threshold, further, total area detection is performed to find that the total area of P1, P2, P3, P4 and P5 is 5 plus 12 plus 6 plus 3 plus 1=27 and larger than the second area threshold, and thus the abnormality of the target die is determined.
If the number of the sensitive sub-areas included in the determined target area is 0 in S103, it is directly determined that the target mold is not abnormal. If the number of the sensitive subregions contained in the target region determined in S103 is 1, in S104, only whether the area of the sensitive subregion is larger than a first area threshold is required to be determined, and if the area of the sensitive subregion is smaller than or equal to the first area threshold, the target die is determined to be normal; if the number of the sensitive subregions included in the target region determined in S103 is greater than 1, then the judgment needs to be strictly performed according to the judgment logic shown in fig. 5, and only if the area of each sensitive subregion is smaller than or equal to the first area threshold and the sum of the areas of the sensitive subregions is smaller than or equal to the second area threshold, the target die is not determined to be normal; by the judging mechanism, the accuracy of abnormality detection of the target die can be effectively ensured.
Optionally, after S104, the method of the present embodiment further includes: and if the target die is determined to be abnormal, alarming. For example, the man-machine interaction interface is controlled to flash, the alarm is controlled to give an alarm, a prompt message is controlled to be sent to user equipment (such as a mobile phone) for alarming, so that a user is prompted to perform manual intervention in time, or the injection molding machine is directly controlled to stop working, and the like, so that the protection of the target mold is realized.
In this embodiment, a template image of a target mold and an image to be detected are obtained, where the template image is an image of the target mold in a normal state, and includes a region of interest including an 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 subarea contained in the target area according to pixel differences existing between the target area and the region of interest; according to the area of the sensitive subarea, whether the target die is abnormal or not is determined, automatic monitoring of the die quality is achieved, instantaneity and accuracy of die abnormality detection are considered, the die quality can be accurately monitored, production efficiency is improved, and beneficial economic benefits and social values are achieved.
Example two
In the following, a description will be given of a process of preprocessing a template image of a target mold by a specific embodiment, and 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, where preprocessing a template image includes:
s201, determining a preselected area of the template image according to the frame selection operation of the target mold in the template image by the user in the man-machine interaction interface.
The industrial personal computer provided by the embodiment comprises a man-machine interaction interface, and a user can view the image acquired by the visual imaging component through the man-machine interaction interface and perform corresponding operation on the image. In one possible implementation manner, in this embodiment, on the basis of a frame selection operation performed on the template image by the user, a region of interest of the template image is determined, so as to ensure accuracy of the determined target region. Specifically, in the step, a preselected area of a template image is determined by identifying a frame selection operation of a target mold in the template image by a user in 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 frame selection operations of any shape, such as a circle, a rectangle, a polygon, and the like, on the template image, and meanwhile, in this embodiment, the user is also supported to select a plurality of pre-regions, so as to meet use requirements in different scenes, thereby enhancing scene adaptation capability of the technical scheme provided in this application.
It can be appreciated that in this embodiment, a prompt message, such as "please select the position of the mold" may be displayed on the man-machine interface to guide the user's operation, so as to ensure that the determined pre-selected area contains the image of the target mold. In addition, in the embodiment, selection icons with different shapes can be further arranged on the man-machine interaction interface, so that the selection icons can be selected and used when a user performs 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.
Because of aging or mechanical fluctuation of the injection molding machine, the position of the mold or the visual imaging component may slightly move, so that the allowable position deviation may exist between different pictures, and in this step, in order to better acquire the edge, the boundary expansion 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 manner may be adopted to perform a certain degree of oversized selected area, so as to accurately identify the target area in the image to be detected later.
In one possible implementation manner, if the preselected area is one, based on the minimum circumscribing rectangle (or the minimum circumscribing circle, etc.) of the preselected area, performing boundary expansion on the preselected area according to a preset filling value to obtain an interested area; if there are a plurality of pre-selected areas, based on the minimum circumscribing rectangle (or the minimum circumscribing circle, etc.) of all the pre-selected areas, carrying out boundary expansion on the pre-selected areas according to the preset filling value to obtain the interested area.
S203, performing adaptive median filtering processing on the template image.
In this step, in order to better protect relevant details in the template image, after the determination of the region of interest is completed, adaptive median filtering processing is performed on the template image.
Optionally, in the embodiment, an adaptive median filter may be set in advance, and the window size of the median filter is dynamically changed by the adaptive median filter according to a preset condition, so as to implement filtering processing on the image. The self-adaptive filter not only can filter out the salt and pepper noise with high probability, but also can better protect the details of the image, and is beneficial to improving the speed of subsequent image processing.
It can be understood that the adaptive median filtering process can also be performed on the image to be detected as required before the positioning identification is performed on the image to be detected.
In the embodiment, through the frame selection operation of the target mould in the template image according to the man-machine interaction interface of the user, the preselected area of the template image is determined, the boundary expansion is carried out on the preselected area, the interested area of the template image is obtained, the adaptive median filtering processing is carried out on the template image, the preprocessing of the template image is realized, the interested area of the template image is determined, unnecessary noise interference is reduced, the speed and the precision of subsequent image processing are improved, and the quality of abnormal detection of the mould 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, the state monitoring device 10 for a mold according to the present embodiment includes:
an image acquisition module 11 and an image processing module 12.
An image acquisition module 11, configured to acquire a template image and an image to be detected of a target mold; the template image is an image of the target die in a normal state, and the template image comprises an interested region containing the image of the target die;
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 pixel differences existing between the target region and the region of interest; and determining whether the target die 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 map based on pixel differences existing between the target region and the region of interest, wherein the pixel difference map comprises 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 map.
Optionally, the image processing module 12 is specifically configured to:
determining a target pixel difference point on the pixel difference map, wherein the pixel difference value of the target pixel difference point is larger than the sensitivity threshold;
and determining a sensitive subarea contained in the target area according to the position relation among target pixel differential points, wherein each pixel differential point in the sensitive subarea is the target pixel differential point.
Optionally, the image processing module 12 is specifically configured to:
determining whether there is a sensitive sub-region in the target region having a single area greater than a first area threshold; if so, determining that the target die is abnormal;
if not, determining whether the total area of each sensitive subarea is greater than a second area threshold; if yes, determining that the target die is abnormal.
Optionally, the image processing module 12 is further configured to:
determining a preselected area of the template image according to the frame selection operation of a user on the target mold in the template image at a man-machine interaction interface;
and carrying out boundary expansion on the preselected area to obtain the interested area of the template image.
Optionally, the image processing module 12 is specifically configured to:
if the preselected area is one, carrying out boundary expansion on the preselected area based on the minimum circumscribed rectangle of the preselected area to obtain the region of interest;
and if a plurality of the preselected areas are provided, carrying out boundary expansion on the preselected areas based on the minimum circumscribed rectangles of the plurality of the preselected areas to obtain the region of interest.
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 die is determined to be abnormal, alarming.
The state monitoring device for the mold provided by the embodiment can execute the state monitoring method for 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 the present embodiment are similar to those of the above method embodiment, and are not described here again.
Example IV
Fig. 8 is a schematic structural diagram of an industrial personal computer according to a fourth embodiment of the present application, 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 processors 22 of the industrial personal computer 20 may 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 in fig. 8, the connection is exemplified by a bus.
The memory 21 is a computer-readable storage medium that can be used to store 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 embodiment of the present application. The processor 22 executes software programs, instructions and modules stored in the memory 21 to perform various functional applications and data processing of the industrial personal computer, i.e. to implement the method for monitoring the state of the mold described above.
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, at least one application program required for functions; the storage data area may store data created according to the use of the terminal, etc. In addition, 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 remotely located relative to processor 22, which may be connected to the industrial personal computer via a grid. Examples of such grids include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Example five
A fifth embodiment of the present application also provides a computer-readable storage medium having stored thereon a computer program for performing a method of condition monitoring of a mold, when executed by a computer processor, the method comprising:
acquiring a template image and an image to be detected of a target die; the template image is an image of the target die in a normal state, and the template image comprises an interested region containing the image of the target die;
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 pixel differences existing between the target region and the region of interest;
and determining whether the target die 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 the embodiments of the present application is not limited to the method operations described above, but may also perform the 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 embodiments, it will be clear to a person skilled in the art that the present application may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art 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 (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk, or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a grid device, etc.) to perform the method described in the embodiments of the present application.
Example six
Fig. 9 is a schematic structural diagram of a state monitoring system for a mold according to a sixth embodiment of the present application, and as shown in fig. 9, a state detecting system 30 for a mold according to the present embodiment includes a visual imaging component 31 and an industrial personal computer 20 according to a fourth embodiment.
Wherein, the visual imaging component 31 is used for collecting the template image and the image to be detected of the object to be detected.
Optionally, in this embodiment, the visual imaging assembly includes: industrial cameras, imaging lenses, infrared light sources, and infrared filters, the specific cases of the industrial cameras, imaging lenses, infrared light sources, and infrared filters used in the embodiments of the present application will be described below:
(1) Industrial camera
In order to better cope with application scenes, in the embodiment, industrial cameras with various resolutions, such as 100 ten thousand pixel resolution, 300 ten thousand pixel resolution, 500 ten thousand pixel resolution and the like, can be provided, so that the cameras with corresponding resolutions can be selected according to detection objects and precision requirements in different application scenes, and the scene adaptability of the system is improved.
The data transmission interface of the industrial camera in this embodiment is a USB 3.0, which not only can perform data transmission, but also can supply power to the industrial camera at the same time, without 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-shaped interface which is used as a universal interface, so that the lens can be replaced conveniently.
Alternatively, the chip of the industrial camera used in the present embodiment is a black-and-white photosensitive chip. The reason for the selection is as follows: firstly, in an application scene of mold monitoring, special requirements on colors are not required; secondly, because of the camera 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 imaging effect of the black-and-white photosensitive chip is better; finally, in the image processing process, gray information is obtained by the black-and-white industrial camera, and can be directly processed.
(2) Imaging lens
Because the lens directly determines the size and the definition of the field of view, whether the lens is properly selected directly determines whether the visual imaging module can acquire images with higher imaging quality. In an actual application scene, the fixed focus lens is not suitable for due to the special nature, the irregularity and the size factors of different products and dies. Therefore, in this system, a variable focal length lens is selected, and in this embodiment, the focal length of the variable focal length lens ranges from 12 mm to 50mm, and the detection object can be made to be in a proper size and brightness in cooperation with the adjustment of the aperture and the sharpness.
(3) Infrared light source and infrared filter
In order to avoid the influence of external illumination on subsequent image processing, infrared light source illumination is adopted, 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 a monitoring system on the environment is improved.
Optionally, the industrial personal computer 20 further includes:
and the man-machine interaction interface is used for information display and operation instruction acquisition. Illustratively, the human-machine interaction interface includes the following functions:
monitoring state display: and displaying the current working state in real time.
And (3) production state display: counting the number of production tests after the last zero clearing.
Camera setting: the user can set the camera exposure time, the camera gain, the product photographing delay, the product rechecking delay, the mold cavity photographing delay, the template rechecking delay and the like on the human-computer interaction interface so as to realize the setting of the camera.
Parameter setting: the method mainly comprises the steps of setting detection parameters, including detection times, detection time delay, administrator password setting, time setting, screen calibration and the like.
Template sampling: the upper left corner status bar of the man-machine interaction interface can display the signals for starting sampling and waiting for closing and opening the safety door, and can automatically sample.
Setting a template: and (5) manually photographing to select templates, drawing a monitoring range, adding and deleting the number of the templates, and the like.
Sensitivity: the sensitivity, area control and detection verification functions of the detection area are set. The sensitivity and the area can be set for each region, and the defect detection range can be directly determined. The detection and verification can bypass the signal, and the state of the die is monitored manually.
Logging: recording operation logs and viewing normal/abnormal pictures, and mainly viewing alarm pictures and alarm positions, analyzing alarm reasons and the like.
And (3) signal display: the current signal transmission condition and the next signal transmission are displayed.
It should be noted that, in the embodiment of the state monitoring device of the mold, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present application.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. Those skilled in the art will appreciate that the present application is not limited to the particular embodiments described herein, but is capable of numerous obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the present application. Therefore, while the present application has been described in connection with the above embodiments, the present application is not limited to the above embodiments, but may include many other equivalent embodiments without departing from the spirit of the present application, the scope of which is defined by the scope of the appended claims.
Claims (16)
1. A method for monitoring the condition of a mold, comprising:
acquiring a template image and an image to be detected of a target die; the template image is an image of the target die in a normal state, and the template image comprises an interested region containing the image of the target die;
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, wherein the method comprises the following steps: searching a target area of the image to be detected based on the region of interest by determining the region of interest of the template image in advance, taking the region of interest of the template image as a sliding window, starting sliding from the origin of the image to be detected, traversing the image to be detected, sliding one step length each time, calculating the similarity between the region of interest and the region of the image to be detected covered by the region of interest, determining the maximum value of the similarity, and determining the region corresponding to the maximum value of the similarity as the target area of the image to be detected;
determining a sensitive sub-region contained in the target region according to pixel differences existing between the target region and the region of interest;
and determining whether the target die is abnormal or not according to the area of the sensitive subarea.
2. The method according to claim 1, wherein the determining the sensitive sub-region contained in the target region based on the pixel difference existing between the target region and the region of interest comprises:
Generating a pixel difference map based on pixel differences existing between the target region and the region of interest, wherein the pixel difference map comprises 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 map.
3. The method according to claim 2, wherein said determining a sensitive sub-region contained in the target region from pixel difference values and sensitivity thresholds in the pixel difference map comprises:
determining a target pixel difference point on the pixel difference map, wherein the pixel difference value of the target pixel difference point is larger than the sensitivity threshold;
and determining a sensitive subarea contained in the target area according to the position relation among target pixel differential points, wherein each pixel differential point in the sensitive subarea is the 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 sensitive sub-region in the target region having a single area greater than a first area threshold; if so, determining that the target die is abnormal;
If not, determining whether the total area of each sensitive subarea is greater than a second area threshold; if yes, determining that the target die is abnormal.
5. The method according to any one of claims 1 to 4, wherein before the capturing of 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 frame selection operation of a user on the target mold in the template image at a man-machine interaction interface;
and carrying out boundary expansion on the preselected area to obtain the interested area of the template image.
6. The method of claim 5, wherein said boundary expanding said preselected region to obtain a region of interest of said template image comprises:
if the preselected area is one, carrying out boundary expansion on the preselected area based on the minimum circumscribed rectangle of the preselected area to obtain the region of interest;
and if a plurality of the preselected areas are provided, carrying out boundary expansion on the preselected areas based on the minimum circumscribed rectangles of the plurality of the preselected areas to obtain the region of interest.
7. The method according to any one of claims 1-4, wherein the identifying and positioning the image to be detected by using a pyramid template matching algorithm according to the region of interest of the template image, and before obtaining 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 die is determined to be abnormal, alarming.
9. A condition monitoring device for a mold, comprising:
the image acquisition module is used for acquiring a template image and an image to be detected of the target die; the template image is an image of the target die in a normal state, and the template image comprises an interested region containing the image of the target die;
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 pixel differences existing between the target region and the region of interest; determining whether the target die is abnormal or not according to the area of the sensitive subarea;
the identifying and positioning the image to be detected by adopting a pyramid template matching algorithm according to the interested region of the template image to obtain a target region of the image to be detected, comprising:
Searching a target area of the image to be detected based on the region of interest by determining the region of interest of the template image in advance, sliding the region of interest of the template image from the origin of the image to be detected by taking the region of interest of the template image as a sliding window, traversing the image to be detected, sliding one step length at a time, calculating the similarity between the region of interest and the region of the image to be detected covered by the region of interest, determining the maximum value of the similarity, and determining the region corresponding to the maximum value of the similarity as the target area of the image to be detected.
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 condition monitoring of a mould according to any one of claims 1-8 when executing the program.
11. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a method for monitoring the condition of a mould according to any one of claims 1-8.
12. A condition monitoring system for a mold, comprising:
The visual imaging assembly is used for collecting a template image and an image to be detected of the object 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 for monitoring the condition of a mould according to any one of claims 1 to 8 when executing the program.
13. The system of claim 12, wherein the visual imaging assembly comprises: industrial cameras, imaging lenses, infrared light sources, and infrared filters.
14. The system of claim 13, wherein the chip employed 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 man-machine interaction interface is used for information display and operation instruction acquisition.
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| 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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| 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 |
| CN113362308A (en) * | 2021-06-08 | 2021-09-07 | 深圳市华汉伟业科技有限公司 | Method and device for detecting burrs of object edge and storage medium |
| CN113609897A (en) * | 2021-06-23 | 2021-11-05 | 阿里巴巴新加坡控股有限公司 | Defect detection method and defect detection system |
| 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 |
| 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 |
| 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 |
| CN115953397B (en) * | 2023-03-13 | 2023-06-02 | 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 |
| 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 |
| 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 |
| 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 |
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 |
|---|---|---|---|---|
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-
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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 |
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