CN110827245A - Method and equipment for detecting screen display disconnection - Google Patents
Method and equipment for detecting screen display disconnection Download PDFInfo
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- CN110827245A CN110827245A CN201911032867.4A CN201911032867A CN110827245A CN 110827245 A CN110827245 A CN 110827245A CN 201911032867 A CN201911032867 A CN 201911032867A CN 110827245 A CN110827245 A CN 110827245A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30121—CRT, LCD or plasma display
Abstract
The method comprises the steps of obtaining information of a target point in broken lines by utilizing a convolutional neural network; performing area extension according to the information of the target point to obtain a target extension diagram; and judging the target extension diagram, and determining the position of the broken line according to the judgment result. Therefore, whether the screen generates display disconnection or not is effectively and automatically detected, and the disconnection position of the screen is accurately determined.
Description
Technical Field
The application relates to the field of screen detection, in particular to a method and equipment for detecting screen display disconnection.
Background
The problems of screen display of the intelligent device include display broken lines, dead spots and the like, in order to check whether screen display is normal or not, the existing broken line detection mode generally uses a classification method to obtain broken line position information, consumes a large amount of computing resources, generates a redundant calculation process, is difficult to obtain an accurate broken line position, and cannot automatically obtain a point on the broken line.
Disclosure of Invention
An object of the present application is to provide a method and an apparatus for detecting a screen display disconnection, which solve the problems of low efficiency of detecting the screen display disconnection, difficulty in automatically detecting a disconnection position, and inaccurate judgment of the disconnection position in the prior art.
According to an aspect of the present application, there is provided a method of detecting a disconnection of a screen display, the method including:
acquiring information of a target point in a broken line by using a convolutional neural network;
performing area extension according to the information of the target point to obtain a target extension diagram;
and judging the target extension diagram, and determining the position of the broken line according to the judgment result.
Further, the acquiring information of the target point in the broken line by using the convolutional neural network includes:
acquiring a pixel value of a target point in a broken line by using a convolutional neural network;
and acquiring coordinate information of the target point in the broken line by using the convolutional neural network.
Further, the acquiring pixel values of target points in the broken line by using the convolutional neural network includes:
acquiring pixel values of a plurality of pixel points in a broken line by using a convolutional neural network;
and determining the pixel value of the target point according to the number of the plurality of pixel points and the pixel values of the plurality of pixel points.
Further, the acquiring coordinate information of the target point in the broken line by using the convolutional neural network includes:
acquiring coordinate information corresponding to a plurality of pixel points in the broken line by using a convolutional neural network;
determining the number of the plurality of pixel points and calculating the accumulated sum of the coordinate information corresponding to the plurality of pixel points;
and determining the coordinate information of the target point according to the ratio of the accumulated sum of the coordinate information corresponding to the plurality of pixel points to the number of the plurality of pixel points.
Further, the performing area extension according to the information of the target point to obtain a target extension map includes:
determining a first adjacent pixel according to the coordinate information of the target point, and acquiring a pixel value of the first adjacent pixel;
and judging whether the difference value between the pixel value of the target point and the pixel value of the first adjacent pixel is smaller than a first preset threshold value, if so, determining a target extension diagram according to the first adjacent pixel and the target point.
Further, the determining the target extension diagram and determining the position of the broken line according to the determination result includes:
judging whether the target extension diagram is allowed to be extended by the area, if so, continuing the area extension of the target extension diagram; if not, determining the position of the broken line according to the target extension diagram.
Further, whether the target extension diagram is allowed to be extended by the area is judged, and if yes, the area extension is continued on the target extension diagram; if not, determining the position of the broken line according to the target extension diagram, wherein the step of determining the position of the broken line comprises the following steps:
determining the pixel average value of all pixel points when the target extension diagram extends to the current last horizontal position in the horizontal direction;
acquiring the pixel value of the adjacent pixel at the current last horizontal position, judging whether the target extension diagram is allowed to be extended by the region according to the pixel value of the adjacent pixel at the current last horizontal position and the pixel average value, and if so, continuing to extend the region of the target extension diagram; and if not, taking the target extension diagram as the position of the broken line.
Further, whether the target extension diagram is allowed to be extended by the area is judged, and if yes, the area extension is continued on the target extension diagram; if not, determining the position of the broken line according to the target extension diagram, wherein the step of determining the position of the broken line comprises the following steps:
determining the pixel average value of all pixel points when the target extension diagram extends to the current last vertical position in the vertical direction;
acquiring the pixel value of the adjacent pixel at the current last vertical position, judging whether the target extension diagram is allowed to be extended by the region according to the pixel value of the adjacent pixel at the current last vertical position and the pixel average value, and if so, continuing to extend the region of the target extension diagram; and if not, taking the target extension diagram as the position of the broken line.
Further, the convolutional neural network is a segmented neural network u-net network.
According to another aspect of the present application, there is also provided a computer readable medium having stored thereon computer readable instructions executable by a processor to implement the aforementioned method of detecting a screen display disconnection.
According to still another aspect of the present application, there is also provided an apparatus for detecting a disconnection of a screen display, wherein the apparatus includes:
one or more processors; and
a memory storing computer readable instructions that, when executed, cause the processor to perform the operations of the foregoing one method of detecting screen display disconnection.
Compared with the prior art, the method and the device have the advantages that the information of the target point in the broken line is obtained by utilizing the convolutional neural network; performing area extension according to the information of the target point to obtain a target extension diagram; and judging the target extension diagram, and determining the position of the broken line according to the judgment result. Therefore, whether the screen generates display disconnection or not is effectively and automatically detected, and the disconnection position of the screen is accurately determined.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
fig. 1 is a flow chart illustrating a method for detecting a screen display disconnection according to an aspect of the present application.
The same or similar reference numbers in the drawings identify the same or similar elements.
Detailed Description
The present application is described in further detail below with reference to the attached figures.
In a typical configuration of the present application, the terminal, the device serving the network, and the trusted party each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.
Fig. 1 is a schematic flow chart illustrating a method for detecting a screen display disconnection according to an aspect of the present application, where the method includes: S11-S13, wherein, in the step S11, the information of the target point in the broken line is obtained by utilizing the convolutional neural network; step S12, performing area extension according to the information of the target point to obtain a target extension diagram; step S13, determining the target extension diagram, and determining the position of the broken wire according to the determination result. Therefore, whether the screen generates display disconnection or not is effectively and automatically detected, and the disconnection position of the screen is accurately determined.
Specifically, in step S11, information of a target point in a broken line is acquired using a convolutional neural network. Here, the trained convolutional neural network is used to detect the broken line, and information of a plurality of points in the broken line, such as position coordinate information and corresponding pixel values corresponding to the plurality of points, can be obtained, where the trained convolutional neural network is obtained by training using the marked broken line data; and then, obtaining the information of the target point through calculation according to the information of the plurality of points, wherein the information of the target point includes, but is not limited to, position coordinate information of the target point and a pixel value of the target point. The process of acquiring the target point information is simple, convenient and quick according to the convolutional neural network, the obtained result is visual, a complex check flow is not needed, and the accuracy is high.
In step S12, a region is extended according to the information of the target point, and a target extension map is obtained. Here, the extension in the horizontal direction and the vertical direction is performed according to the position coordinate information of the target point and the pixel value of the target point, and preferably, the area extension is completed by capturing pixel points whose surrounding neighboring pixel values do not exceed a certain preset threshold, and the target extension graph is determined in combination with the target point.
In step S13, the target extension diagram is determined, and the position of the broken line is specified based on the determination result. Here, whether the obtained target extension diagram can be further extended is judged, and if yes, the target extension diagram is further extended; if not, the target extension diagram is the broken line, and the position coordinate information of the broken line is obtained according to the position coordinate information corresponding to the target extension diagram.
Preferably, in step S11, a convolutional neural network is used to obtain the pixel value of the target point in the broken line; and acquiring coordinate information of the target point in the broken line by using the convolutional neural network. Here, the information of the target point in the broken line includes, but is not limited to, pixel values and coordinate information, and the information of the target point in the broken line can be accurately acquired through the convolutional neural network.
Preferably, in step S11, the convolutional neural network is used to obtain pixel values of a plurality of pixel points in the broken line; and determining the pixel value of the target point according to the number of the plurality of pixel points and the pixel values of the plurality of pixel points. The ratio of the sum of the pixel values of the plurality of pixel points to the number of the plurality of pixel points is used to determine the pixel value of the target point.
Preferably, in step S11, coordinate information corresponding to a plurality of pixel points in the broken line is acquired by using a convolutional neural network; determining the number of the plurality of pixel points and calculating the accumulated sum of the coordinate information corresponding to the plurality of pixel points; and determining the coordinate information of the target point according to the ratio of the accumulated sum of the coordinate information corresponding to the plurality of pixel points to the number of the plurality of pixel points. The convolutional neural network can be used for automatically and accurately acquiring the coordinate information of a plurality of pixel points in the broken line so as to accurately acquire the coordinate information of the target point. It should be noted that the above manner of determining the coordinate information of the target point is only an example, and other manners may also be included, for example, the coordinate information of the target point may also be determined by the sum of the coordinate information corresponding to a plurality of pixel points and the weighted average of the number of the plurality of pixel points.
Preferably, in step S12, a first neighboring pixel is determined according to the coordinate information of the target point, and a pixel value of the first neighboring pixel is obtained; and judging whether the difference value between the pixel value of the target point and the pixel value of the first adjacent pixel is smaller than a first preset threshold value, if so, determining a target extension diagram according to the first adjacent pixel and the target point. Capturing surrounding directly adjacent pixels by using the coordinate information of the target point to obtain a plurality of first adjacent pixels, and obtaining pixel values of the first adjacent pixels. Then, setting a first preset threshold, and when the absolute value of the difference between the pixel value of the target point and the pixel value of the first adjacent pixel is smaller than the first preset threshold, adding the first adjacent pixel to the target point in a preset manner to form a target extension diagram, preferably, the preset manner includes but is not limited to: and marking the first adjacent pixel and the target point in a preset mode, and determining the target extension diagram according to the marked pixel points in the preset mode.
Preferably, in step S13, it is determined whether the target extension map is allowed to be extended by an area, and if so, the area extension is continued for the target extension map; if not, determining the position of the broken line according to the target extension diagram. The region extension includes extension in a horizontal direction and extension in a vertical direction, when the target extension diagram cannot continue the region extension, the target extension diagram is a broken line, and the position of the broken line is determined according to the position of the target extension diagram.
Determining the pixel average value of all pixel points when the horizontal direction of the target extension diagram extends to the current last horizontal position; acquiring the pixel value of the adjacent pixel at the current last horizontal position, judging whether the target extension diagram is allowed to be extended by the region according to the pixel value of the adjacent pixel at the current last horizontal position and the pixel average value, and if so, continuing to extend the region of the target extension diagram; and if not, taking the target extension diagram as the position of the broken line. Here, the target extension map is extended horizontally to reach a current last horizontal position, and pixel values of neighboring pixels at the current last horizontal position are compared with the pixel average value, where the neighboring pixels at the current last horizontal position are not in the target extension map. Preferably, a second preset threshold is set, when the difference value between the pixel value of the adjacent pixel at the current last horizontal position and the pixel average value is smaller than the second preset threshold, the target extension map is allowed to be extended regionally, the adjacent pixel at the current last horizontal position is included in the target extension map, and the target extension map is continued to be extended horizontally; when the difference value between the pixel value of the pixel at the current last horizontal position and the pixel average value is greater than or equal to the second preset threshold value, the target extension diagram is not allowed to extend to the current last horizontal position, and the horizontal position of the target extension diagram is the horizontal position of the broken line, so that the horizontal position of the broken line is accurately confirmed.
Determining the pixel average value of all pixel points when the target extension diagram extends to the current last vertical position in the vertical direction; acquiring the pixel value of the adjacent pixel at the current last vertical position, judging whether the target extension diagram is allowed to be extended by the region according to the pixel value of the adjacent pixel at the current last vertical position and the pixel average value, and if so, continuing to extend the region of the target extension diagram; and if not, taking the target extension diagram as the position of the broken line. Here, the target extension map is vertically extended to reach a current last vertical position, and pixel values of neighboring pixels at the current last vertical position are compared with the pixel average value, wherein the neighboring pixels at the current last vertical position are not in the target extension map. Preferably, a third preset threshold is set, when the difference value between the pixel value of the adjacent pixel at the current last vertical position and the pixel average value is smaller than the third preset threshold, the target extension map is allowed to be extended regionally, the adjacent pixel at the current last vertical position is included in the target extension map, and the target extension map continues to be extended vertically; when the difference value between the pixel value of the adjacent pixel at the current last vertical position and the pixel average value is greater than or equal to the third preset threshold value, the target extension diagram is not allowed to extend to the current last vertical position, and the vertical position of the target extension diagram is the vertical position of the broken line, so as to accurately confirm the vertical position of the broken line.
Preferably, the convolutional neural network is a segmented neural network u-net network. In the preferred embodiment, the broken line region and the normal display region of the broken line picture can be displayed through a manual marking screen, the marked picture is used for training and dividing the neural network u-net network to obtain a broken line detection model, the broken line detection model is used for detecting the picture to be detected, when the broken line is detected, a plurality of pixel points in the broken line are obtained, and the target point can be accurately obtained after calculation through the mode.
In addition, the embodiment of the present application further provides a computer readable medium, on which computer readable instructions are stored, where the computer readable instructions are executable by a processor to implement the foregoing method for detecting a broken line of a screen display.
According to still another aspect of the present application, there is also provided an apparatus for detecting a disconnection of a screen display, wherein the apparatus includes:
one or more processors; and
a memory storing computer readable instructions that, when executed, cause the processor to perform the operations of the foregoing one method of detecting screen display disconnection.
For example, the computer readable instructions, when executed, cause the one or more processors to: acquiring information of a target point in a broken line by using a convolutional neural network; performing area extension according to the information of the target point to obtain a target extension diagram; and judging the target extension diagram, and determining the position of the broken line according to the judgment result.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, implemented using Application Specific Integrated Circuits (ASICs), general purpose computers or any other similar hardware devices. In one embodiment, the software programs of the present application may be executed by a processor to implement the steps or functions described above. Likewise, the software programs (including associated data structures) of the present application may be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Additionally, some of the steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
In addition, some of the present application may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or techniques in accordance with the present application through the operation of the computer. Program instructions which invoke the methods of the present application may be stored on a fixed or removable recording medium and/or transmitted via a data stream on a broadcast or other signal-bearing medium and/or stored within a working memory of a computer device operating in accordance with the program instructions. An embodiment according to the present application comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform a method and/or a solution according to the aforementioned embodiments of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the apparatus claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Claims (11)
1. A method of detecting a screen display disconnection, wherein the method comprises:
acquiring information of a target point in a broken line by using a convolutional neural network;
performing area extension according to the information of the target point to obtain a target extension diagram;
and judging the target extension diagram, and determining the position of the broken line according to the judgment result.
2. The method of claim 1, wherein the obtaining information of the target point in the broken line using the convolutional neural network comprises:
acquiring a pixel value of a target point in a broken line by using a convolutional neural network;
and acquiring coordinate information of the target point in the broken line by using the convolutional neural network.
3. The method of claim 2, wherein the obtaining pixel values of target points in a broken line using a convolutional neural network comprises:
acquiring pixel values of a plurality of pixel points in a broken line by using a convolutional neural network;
and determining the pixel value of the target point according to the number of the plurality of pixel points and the pixel values of the plurality of pixel points.
4. The method of claim 2, wherein the obtaining coordinate information of the target point in the broken line by using the convolutional neural network comprises:
acquiring coordinate information corresponding to a plurality of pixel points in the broken line by using a convolutional neural network;
determining the number of the plurality of pixel points and calculating the accumulated sum of the coordinate information corresponding to the plurality of pixel points;
and determining the coordinate information of the target point according to the ratio of the accumulated sum of the coordinate information corresponding to the plurality of pixel points to the number of the plurality of pixel points.
5. The method according to claim 2, wherein the performing region extension according to the information of the target point to obtain a target extension map comprises:
determining a first adjacent pixel according to the coordinate information of the target point, and acquiring a pixel value of the first adjacent pixel;
and judging whether the difference value between the pixel value of the target point and the pixel value of the first adjacent pixel is smaller than a first preset threshold value, if so, determining a target extension diagram according to the first adjacent pixel and the target point.
6. The method of claim 1, wherein the determining the target extension diagram and determining the position of the broken line according to the determination result comprise:
judging whether the target extension diagram is allowed to be extended by the area, if so, continuing the area extension of the target extension diagram; if not, determining the position of the broken line according to the target extension diagram.
7. The method according to claim 6, wherein, whether the target extension map is allowed to be extended by the region is determined, if yes, the region extension is continued on the target extension map; if not, determining the position of the broken line according to the target extension diagram, wherein the step of determining the position of the broken line comprises the following steps:
determining the pixel average value of all pixel points when the target extension diagram extends to the current last horizontal position in the horizontal direction;
acquiring the pixel value of the adjacent pixel at the current last horizontal position, judging whether the target extension diagram is allowed to be extended by the region according to the pixel value of the adjacent pixel at the current last horizontal position and the pixel average value, and if so, continuing to extend the region of the target extension diagram; and if not, taking the target extension diagram as the position of the broken line.
8. The method according to claim 6, wherein, whether the target extension map is allowed to be extended by the region is determined, if yes, the region extension is continued on the target extension map; if not, determining the position of the broken line according to the target extension diagram, wherein the step of determining the position of the broken line comprises the following steps:
determining the pixel average value of all pixel points when the target extension diagram extends to the current last vertical position in the vertical direction;
acquiring the pixel value of the adjacent pixel at the current last vertical position, judging whether the target extension diagram is allowed to be extended by the region according to the pixel value of the adjacent pixel at the current last vertical position and the pixel average value, and if so, continuing to extend the region of the target extension diagram; and if not, taking the target extension diagram as the position of the broken line.
9. The method of claim 1, wherein the convolutional neural network is a partitioned neural network u-net network.
10. An apparatus for detecting a disconnection of a screen display, wherein the apparatus comprises:
one or more processors; and
a memory storing computer readable instructions that, when executed, cause the processor to perform the operations of the method of any of claims 1 to 9.
11. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of any one of claims 1 to 9.
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| PCT/CN2020/120879 WO2021082922A1 (en) | 2019-10-28 | 2020-10-14 | Method and device for detecting screen display disconnection |
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| WO2021082922A1 (en) * | 2019-10-28 | 2021-05-06 | 上海悦易网络信息技术有限公司 | Method and device for detecting screen display disconnection |
| US11798250B2 (en) | 2019-02-18 | 2023-10-24 | Ecoatm, Llc | Neural network based physical condition evaluation of electronic devices, and associated systems and methods |
| US11843206B2 (en) | 2019-02-12 | 2023-12-12 | Ecoatm, Llc | Connector carrier for electronic device kiosk |
| US11922467B2 (en) | 2020-08-17 | 2024-03-05 | ecoATM, Inc. | Evaluating an electronic device using optical character recognition |
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| WO2021082922A1 (en) | 2021-05-06 |
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