Device Screen Repair Feature Detection Method and Device

Abstract

The present invention relates to a method and device for detecting the repair characteristics of a device screen. After obtaining the flash image of the intelligent device to be tested, the exposure point area of the flash image is extracted, and whether there are repair characteristics on the device screen of the intelligent device to be tested is judged according to the gradient direction distribution and gradient direction of the exposure point area. Based on this, the interference of the lighting environment of the flash image is reduced through the environmental setting of the flash image. At the same time, through the image optimization method for the flash image, it is convenient to accurately and quickly determine whether there are repair characteristics in the screen area of the intelligent device to be tested.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic products, and particularly to a method and device for detecting the repair characteristics of a device screen. Background Art

[0002] With the development of electronic product technology, various intelligent devices emerge in an endless stream, such as smart phones, laptops, and tablet computers. At present, with the rapid development of economy and technology, the popularization and replacement speed of intelligent devices are also getting faster and faster. Taking smart phones as an example, the arrival of the 5G era has accelerated the replacement of smart phones. During the iteration process of intelligent devices, effective recycling is one of the effective means to utilize the residual value of intelligent devices, which can reduce chemical pollution to the environment and reduce waste.

[0003] During the recycling process of mobile phones, the overall loss degree of the device screen has a great impact on the recycling valuation of intelligent devices. Among them, when the device screen is severely damaged, it is usually necessary to repair the device screen. However, the repaired device screen has a great impact on the recycling valuation of intelligent devices. Therefore, during the recycling process, it is necessary to detect whether the device screen of the intelligent device has repair characteristics to determine the repair status. However, the traditional method is to collect the image of the device screen by the recycled device, but the lighting state during the collection process will bring a lot of interference to the image, affecting the accuracy of the repair feature detection.

[0004] In summary, it can be seen that the traditional method for detecting the repair characteristics of a device screen still has the above deficiencies. Summary of the Invention

[0005] Based on this, in view of the deficiencies still existing in the traditional method for detecting the repair characteristics of a device screen, it is necessary to provide a method and device for detecting the repair characteristics of a device screen.

[0006] A method for detecting the repair characteristics of a device screen includes the steps of:

[0007] Obtaining a flash image of the intelligent device to be tested; wherein, the flash image is a captured image when the intelligent device to be tested is irradiated by a flash;

[0008] Extracting the exposure point area of the flash image; wherein, the exposure point area includes the exposure points where the intelligent device to be tested is irradiated by the flash;

[0009] Judging whether there are repair characteristics on the device screen of the intelligent device to be tested according to the gradient direction distribution and gradient direction of the exposure point area.

[0010] The aforementioned device screen repair feature detection method, after acquiring a flash image of the smart device under test, extracts the exposure point area of the flash image and, based on the gradient distribution and direction of the exposure point area, determines whether the device screen under test exhibits repair features. Based on this, the flash image's ambient lighting environment is optimized to reduce interference. Furthermore, image optimization methods for the flash image facilitate accurate and rapid determination of the presence of repair features on the screen area of the smart device under test.

[0011] In one embodiment, the process of extracting the exposure point area of the flash image includes the steps of:

[0012] Perform grayscale transformation on the flash image to generate a corresponding grayscale image;

[0013] Binarize the grayscale image to obtain a binary image;

[0014] Extract the edges of the binary image using edge detection algorithm;

[0015] The edge exposure point area is filtered out through the circle detection algorithm.

[0016] In one embodiment, before binarizing the grayscale image to obtain the binarized image, the process further includes the following steps:

[0017] Remove white noise interference from grayscale images.

[0018] In one embodiment, a process for determining whether a screen of a smart device to be tested has a maintenance feature based on a gradient direction distribution and a gradient direction of an exposure point area includes the following steps:

[0019] Calculate the gradient of the exposure point area;

[0020] Use the gradient to calculate the gradient direction and magnitude of each pixel in the exposure point area;

[0021] Calculate the gradient direction distribution of the exposure point area based on the gradient direction and amplitude;

[0022] The gradient direction distribution is sorted in reverse order according to the gradient direction, and whether the device screen of the smart device to be tested has a maintenance feature is determined according to the angle of the gradient direction after the reverse order.

[0023] In one embodiment, the process of sorting the gradient direction distribution in reverse order according to the gradient direction, and determining whether the device screen of the smart device under test has a maintenance feature based on the angle of the gradient direction after the reverse order, includes the steps of:

[0024] Based on the first n angles of the forward gradient direction distribution after reverse sorting, determine whether there are repair features on the device screen of the intelligent device to be tested; where n is a positive integer.

[0025] In one embodiment, n is 3.

[0026] In one embodiment, the process of determining whether there are repair features on the device screen of the intelligent device to be tested based on the first n angles of the forward gradient direction distribution after reverse sorting includes the steps of:

[0027] When the first n angles are target angles, it is determined that there are repair features on the device screen of the intelligent device to be tested, otherwise it is determined that there are no repair features.

[0028] A device screen repair feature detection device includes:

[0029] An image acquisition module for acquiring a flash image of the intelligent device to be tested; where the flash image is a captured image when the intelligent device to be tested is illuminated by a flash.

[0030] A region extraction module for extracting the exposure point region of the flash image; where the exposure point region includes the exposure points where the intelligent device to be tested is illuminated by the flash.

[0031] A feature detection module for determining whether there are repair features on the device screen of the intelligent device to be tested according to the gradient direction distribution and gradient direction of the exposure point region.

[0032] After the above device screen repair feature detection device acquires the flash image of the intelligent device to be tested, it extracts the exposure point region of the flash image, and determines whether there are repair features on the device screen of the intelligent device to be tested according to the gradient direction distribution and gradient direction of the exposure point region. Based on this, through the environmental setting of the flash image, the interference of the lighting environment of the flash image is reduced. At the same time, through the image optimization method for the flash image, it is convenient to accurately and quickly determine whether there are repair features in the screen area of the intelligent device to be tested.

[0033] A computer storage medium stores computer instructions thereon, and when the computer instructions are executed by a processor, the device screen repair feature detection method of any of the above embodiments is implemented.

[0034] After the above computer storage medium acquires the flash image of the intelligent device to be tested, it extracts the exposure point region of the flash image, and determines whether there are repair features on the device screen of the intelligent device to be tested according to the gradient direction distribution and gradient direction of the exposure point region. Based on this, through the environmental setting of the flash image, the interference of the lighting environment of the flash image is reduced. At the same time, through the image optimization method for the flash image, it is convenient to accurately and quickly determine whether there are repair features in the screen area of the intelligent device to be tested.

[0035] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the device screen repair feature detection method of any of the above embodiments.

[0036] For the above computer device, after obtaining the flash image of the intelligent device to be tested, the exposure point area of the flash image is extracted, and based on the gradient direction distribution and gradient direction of the exposure point area, it is determined whether there are repair features on the device screen of the intelligent device to be tested. Based on this, by setting the environment of the flash image, the interference of the lighting environment of the flash image is reduced. At the same time, through the image optimization method for the flash image, it is convenient to accurately and quickly determine whether there are repair features in the screen area of the intelligent device to be tested. Description of the Drawings

[0037] Figure 1 It is a flowchart of the device screen repair feature detection method according to an embodiment;

[0038] Figure 2 It is a flowchart of the device screen repair feature detection method according to another embodiment;

[0039] Figure 3 It is a flowchart of the device screen repair feature detection method according to yet another embodiment;

[0040] Figure 4 It is a module structure diagram of the device screen repair feature detection device according to an embodiment;

[0041] Figure 5 It is a schematic diagram of the internal structure of a computer according to an embodiment. Detailed Embodiments

[0042] In order to better understand the purpose, technical solution and technical effect of the present invention, the present invention will be further explained below in conjunction with the drawings and embodiments. At the same time, it is stated that the embodiments described below are only used to explain the present invention and are not used to limit the present invention.

[0043] Figure 1 It is a structural diagram of the acquisition device, as Figure 1 shown, the acquisition device includes: left light source 1 and right light source 11; left acrylic light-transmitting sheet 2 and right acrylic light-transmitting sheet 10 for generating a soft light environment; power supply socket 3; power switch 4; touch screen 5; flash 6; three-in-one data cable 7; camera 8; front metal light guide bar 9 and rear metal light guide bar 13 for enhancing the brightness on both the front and rear sides; the placement board 12 for the intelligent device to be tested, generally green.

[0044] As Figure 1As shown, the intelligent device to be tested is placed in the placement board 12, and the camera 8 performs image shooting on the intelligent device to be tested. Different from the long-term stable illumination during shooting in the traditional recycling process, the flash 6 provides instant strong flash illumination in the camera 8, and cooperates with the camera 8 to collect the flash image.

[0045] Based on this, Figure 2 It is a flowchart of a method for detecting device screen repair features in an embodiment, as Figure 2 shown, a method for detecting device screen repair features in an embodiment includes steps S100 to step S102:

[0046] S100, obtaining a flash image of the intelligent device to be tested; wherein, the flash image is a captured image when the intelligent device to be tested is irradiated by the flash;

[0047] S101, extracting the exposure point area of the flash image; wherein, the exposure point area includes the exposure points where the intelligent device to be tested is irradiated by the flash;

[0048] S102, judging whether there are repair features on the device screen of the intelligent device to be tested according to the gradient direction distribution and gradient direction of the exposure point area.

[0049] The execution subject of step S100 obtains the flash image and performs the next image processing.

[0050] In step S101, the exposure point area of the flash image is extracted, irrelevant image information is removed, the subsequent image processing calculation amount is reduced, and the accuracy of repair feature detection is improved. Among them, if there are repair features on the device screen, it indicates that the device screen of the corresponding intelligent device to be tested has been repaired.

[0051] In one embodiment, the exposure point area of the flash image can be extracted by a pre-established image cropping method.

[0052] In one embodiment, Figure 3 It is a flowchart of a method for detecting device screen repair features in another embodiment, as Figure 3 shown, the process of extracting the exposure point area of the flash image in step S101 includes steps S200 to step S204:

[0053] S200, performing gray-scale transformation on the flash image to generate a corresponding gray-scale image;

[0054] S201, removing the white noise interference of the gray-scale image;

[0055] S202, performing binaryzation processing on the gray-scale image to obtain a binary image;

[0056] S203. Extract the edges of the binary image through an edge detection algorithm;

[0057] S204. Screen out the exposed point area of the edge through a circle detection algorithm.

[0058] Among them, step S201 is an optional step.

[0059] Among them, perform gray-scale transformation on the flash image I to generate the corresponding gray-scale image G1, as shown in the following formula:

[0060] G1(j,i) = 0.1140 * I b (j,i) + 0.5870 * I g (j,i) + 0.2989 * I r (j,i)

[0061] Among them, to remove the white noise interference of the gray-scale image G1, it can be performed through Gaussian blur processing, as shown in the following formula:

[0062]

[0063] Among them, G2 represents the gray-scale image G1 after Gaussian blur processing.

[0064] Among them, in the above formula, j = 1, 2...H, i = 1, 2...W, j and i respectively represent the coordinate values in the horizontal direction and the vertical direction relative to the origin in the upper left corner of the gray-scale image G1, H and W respectively represent the height and width of X, w represents the length of the rectangular window, which is set to 3, and A represents the amplitude of the corresponding rectangular window, which is set to 16. The rectangular window represents the template corresponding to Gaussian filtering:

[0065]

[0066] In one embodiment, the edge detection algorithm can select the Canny edge detection operator. Perform binary processing on the gray-scale image (G1 or G2) to obtain the binary image B, and extract the edge E of the binary image B through the Canny edge detection operator, as shown in the following formula:

[0067]

[0068] As a preferred implementation method, the thresholds of the Canny edge detection operator are respectively set to 30 and 60 to improve the detection accuracy of the smart devices to be tested such as smart phones.

[0069] In one embodiment, the circle detection algorithm can select the Hough circle detection method. Screen out the exposed point area X in the edge E through the Hough circle detection method.

[0070] In one embodiment, such as Figure 3As shown, the process of determining whether there are repair features on the device screen of the intelligent device to be tested according to the gradient direction distribution and gradient direction of the exposure point area in step S102 includes steps S300 to S303:

[0071] S300, calculate the gradient of the exposure point area;

[0072] S301, use the gradient to calculate the gradient direction and amplitude of each pixel point in the exposure point area;

[0073] S302, calculate the gradient direction distribution of the exposure point area according to the gradient direction and amplitude;

[0074] S303, reverse-order sort the gradient direction distribution according to the gradient direction, and determine whether there are repair features on the device screen of the intelligent device to be tested according to the angles of the gradient directions after reverse-order sorting.

[0075] Among them, the gradient G of the exposure point area is calculated as follows:

[0076] G = |G x | + |G y |

[0077]

[0078]

[0079] Use the gradient G to calculate the gradient direction θ and amplitude M of each pixel point in the exposure point area X as follows:

[0080]

[0081]

[0082] Use the gradient direction θ and amplitude M to calculate the gradient direction distribution H of the exposure point area X as follows:

[0083] H(θ) = ∑M(j,i), θ ∈ [0, 360]

[0084] In one embodiment, the process of reverse-order sorting the gradient direction distribution according to the gradient direction and determining whether there are repair features on the device screen of the intelligent device to be tested according to the angles of the gradient directions after reverse-order sorting includes the steps:

[0085] Determine whether there are repair features on the device screen of the intelligent device to be tested according to the first n angles of the gradient direction distribution after reverse-order sorting; where n is a positive integer.

[0086] In one embodiment, n is 3.

[0087] In one embodiment, the process of determining whether there is a repair feature on the device screen of the intelligent device to be tested according to the first n angles of the forward gradient direction distribution after reverse sorting includes the steps:

[0088] When the first n angles are target angles, it is determined that there is a repair feature on the device screen of the intelligent device to be tested; otherwise, it is determined that there is no repair feature.

[0089] In one embodiment, the target angle is 60° or 120°.

[0090] For the device screen repair feature detection method in any of the above embodiments, after obtaining the flash image of the intelligent device to be tested, the exposure point area of the flash image is extracted, and whether there is a repair feature on the device screen of the intelligent device to be tested is determined according to the gradient direction distribution and gradient direction of the exposure point area. Based on this, through the environmental setting of the flash image, the interference of the lighting environment of the flash image is reduced. At the same time, through the image optimization method for the flash image, it is convenient to accurately and quickly determine whether there is a repair feature in the screen area of the intelligent device to be tested.

[0091] An embodiment of the present invention further provides a device screen repair feature detection device.

[0092] Figure 4 For the module structure diagram of the device screen repair feature detection device in one embodiment, as Figure 4 shown, the device screen repair feature detection device in one embodiment includes:

[0093] An image acquisition module 100, configured to acquire a flash image of the intelligent device to be tested; wherein, the flash image is a captured image when the intelligent device to be tested is irradiated by a flash;

[0094] A region extraction module 101, configured to extract the exposure point area of the flash image; wherein, the exposure point area includes the exposure points where the intelligent device to be tested is irradiated by the flash;

[0095] A feature detection module 102, configured to determine whether there is a repair feature on the device screen of the intelligent device to be tested according to the gradient direction distribution and gradient direction of the exposure point area.

[0096] For the above device screen repair feature detection device, after obtaining the flash image of the intelligent device to be tested, the exposure point area of the flash image is extracted, and whether there is a repair feature on the device screen of the intelligent device to be tested is determined according to the gradient direction distribution and gradient direction of the exposure point area. Based on this, through the environmental setting of the flash image, the interference of the lighting environment of the flash image is reduced. At the same time, through the image optimization method for the flash image, it is convenient to accurately and quickly determine whether there is a repair feature in the screen area of the intelligent device to be tested.

[0097] An embodiment of the present invention further provides a computer storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the device screen repair feature detection method of any of the above embodiments is implemented.

[0098] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0099] Alternatively, if the above integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present invention, in essence, or the part that contributes to the related technology can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a terminal, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present invention. And the aforementioned storage medium includes: various media such as a removable storage device, RAM, ROM, a magnetic disk, or an optical disc that can store program codes.

[0100] Corresponding to the above computer storage medium, in one embodiment, a computer device is further provided. The computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements any one of the device screen repair feature detection methods in the above embodiments.

[0101] The computer device may be a terminal, and its internal structure diagram may be as Figure 5 shown. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements a method for detecting the repair characteristics of a device screen. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device may be a touch layer covering the display screen, or may be a button, a trackball, or a touchpad provided on the housing of the computer device, or may also be an external keyboard, a touchpad, or a mouse, etc.

[0102] For the above computer device, after obtaining the flash image of the intelligent device to be tested, the exposure point area of the flash image is extracted, and based on the gradient direction distribution and gradient direction of the exposure point area, it is determined whether there are repair characteristics on the device screen of the intelligent device to be tested. Based on this, through the environmental setting of the flash image, the interference of the lighting environment of the flash image is reduced. At the same time, through the image optimization method for the flash image, it is convenient to accurately and quickly determine whether there are repair characteristics in the screen area of the intelligent device to be tested.

[0103] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0104] The above embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A method for detecting the characteristics of device screen repair, characterized in that, Including the steps: Obtain the flash image of the intelligent device to be tested; wherein, the flash image is the captured image when the intelligent device to be tested is irradiated by the flash; Extract the exposure point area of the flash image; wherein, the exposure point area includes the exposure points where the intelligent device to be tested is irradiated by the flash; Judge whether there are repair features on the device screen of the intelligent device to be tested according to the gradient direction distribution and gradient direction of the exposure point area; The process of judging whether there are repair features on the device screen of the intelligent device to be tested according to the gradient direction distribution and gradient direction of the exposure point area includes the steps: Calculate the gradient of the exposure point area; Use the gradient to calculate the gradient direction and amplitude of each pixel point in the exposure point area; Calculate the gradient direction distribution of the exposure point area according to the gradient direction and the amplitude; Reverse-order sort the gradient direction distribution according to the gradient direction, and judge whether there are repair features on the device screen of the intelligent device to be tested according to the angles of the gradient direction after reverse-order sorting.

2. The method for detecting the device screen repair feature according to claim 1, wherein The process of extracting the exposure point area of the flash image includes the steps: Perform gray-scale transformation on the flash image to generate a corresponding gray-scale image; Perform binarization processing on the gray-scale image to obtain a binarized image; Extract the edges of the binarized image through an edge detection algorithm; Screen out the exposure point area of the edges through a circle detection algorithm.

3. The method for detecting the device screen repair feature according to claim 2, wherein Before the process of performing binarization processing on the gray-scale image to obtain a binarized image, it further includes the steps: Remove the white noise interference of the gray-scale image.

4. The method for detecting the characteristics of device screen repair according to claim 1, wherein, The process of reverse-order sorting the gradient direction distribution according to the gradient direction and judging whether there are repair features on the device screen of the intelligent device to be tested according to the angles of the gradient direction after reverse-order sorting includes the steps: Judge whether there are repair features on the device screen of the intelligent device to be tested according to the first n angles of the gradient direction distribution after reverse-order sorting; wherein, n is a positive integer.

5. The method for detecting the device screen repair characteristics according to claim 4, wherein The n is 3.

6. The method for detecting the characteristics of the device screen repair according to claim 4, wherein The process of judging whether there are repair features on the device screen of the intelligent device to be tested according to the first n angles of the gradient direction distribution after reverse-order sorting includes the steps: When the first n angles are target angles, it is determined that there are repair features on the device screen of the intelligent device to be tested, otherwise it is determined that there are no repair features.

7. A device screen repair feature detection device, characterized in that Including: An image acquisition module, configured to obtain the flash image of the intelligent device to be tested; wherein, the flash image is the captured image when the intelligent device to be tested is irradiated by the flash; A region extraction module, configured to extract the exposure point area of the flash image; wherein, the exposure point area includes the exposure points where the intelligent device to be tested is irradiated by the flash; A feature detection module, configured to judge whether there are repair features on the device screen of the intelligent device to be tested according to the gradient direction distribution and gradient direction of the exposure point area; The process of judging whether there are repair features on the device screen of the intelligent device to be tested according to the gradient direction distribution and gradient direction of the exposure point area includes the steps: Calculate the gradient of the exposure point area; Calculate the gradient direction and magnitude of each pixel point in the exposure point area by using the gradient; Calculate the gradient direction distribution of the exposure point area according to the gradient direction and the magnitude; Reverse-sort the gradient direction distribution according to the gradient direction, and judge whether there is a repair feature on the device screen of the to-be-tested intelligent device according to the angles of the gradient directions after the reverse sorting.

8. A computer storage medium having computer instructions stored thereon, characterized in that, When the computer instructions are executed by a processor, the method for detecting the repair feature of the device screen according to any one of claims 1 to 6 is implemented.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the method for detecting the repair feature of the device screen according to any one of claims 1 to 6 is implemented.

Images