Detection methods and detection devices

Abstract

This invention relates to a detection method. The detection method includes the steps of: acquiring an image, wherein the image contains a plurality of pixels; comparing the gray value of a first pixel in the image with the gray value of a reference region; and determining whether the first pixel is abnormal based on the difference between the gray value of the first pixel and the reference gray values.

Description

Technical Field

[0001] This invention relates to a detection method and a detection device, particularly a detection method and a detection device for a display module. Background Technology

[0002] Display module products must undergo quality inspection before shipment, checking for defects such as dot defects, line defects, or uneven brightness (mura). Currently, these inspections must be performed manually. Human labor is often inefficient and can lead to poor inspection quality due to various factors, such as fatigue and lack of attention to detail. Although some automated machines are equipped with cameras, the captured images still require manual analysis, thus failing to resolve the aforementioned problems.

[0003] In view of this, there is a need to develop an improved detection method and detection device to solve the aforementioned problems. Summary of the Invention

[0004] The present invention provides a detection method comprising the steps of: acquiring an image, wherein the image contains multiple pixels; and comparing the gray value of a first pixel of the image with the reference gray value of a reference region, and determining whether the first pixel is abnormal based on the difference between the gray value of the first pixel and the reference gray value.

[0005] The present invention also provides a detection device, comprising: an image acquisition device and a region grayscale comparison module. The image acquisition device is used to acquire an image, wherein the image contains multiple pixels; the region grayscale comparison module is used to compare the grayscale value of a first pixel of the image with a reference grayscale value in a reference region of the image, and to determine whether the first pixel is abnormal based on the difference between the grayscale value of the first pixel and the reference grayscale value.

[0006] The contents, advantages and novel features of the present invention will then be described in detail with reference to the accompanying drawings. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of the architecture of a detection device according to an embodiment of the present invention;

[0008] Figure 2 This is a flowchart of the detection method according to an embodiment of the present invention;

[0009] Figure 3 This is a schematic diagram illustrating the application of a detection device according to an embodiment of the present invention;

[0010] Figure 4 This is a detailed schematic diagram of a detection device according to an embodiment of the present invention;

[0011] Figure 5 This is an embodiment of the present invention. Figure 2Detailed flowchart of step S14;

[0012] Figure 6(A) is an embodiment of the present invention. Figure 2 Detailed flowchart of step S15;

[0013] Figure 6(B) is a schematic diagram of the first pixel and the reference area in Figure 6(A);

[0014] Figure 7(A) shows another embodiment of the present invention. Figure 2 Detailed flowchart of step S15;

[0015] Figure 7(B) is a schematic diagram of the first pixel and the reference area in Figure 7(A);

[0016] Figure 8 This is another embodiment of the present invention. Figure 2 The detailed flowchart of step S15.

[0017] [Explanation of Labels in the Attached Image]

[0018] 1. Detection device

[0019] 10 Image processing device

[0020] 11 microprocessors

[0021] 12 Exposure Detection Module

[0022] 13 Image Filtering Module

[0023] 14 Image Enhancement Module

[0024] 15. Area Grayscale Comparison Module

[0025] 20 Image acquisition devices

[0026] 22 lenses

[0027] 24 Optical sensing elements

[0028] 30 storage modules

[0029] 32 Transmission Module

[0030] 40 External devices

[0031] 100 testing machines

[0032] 110 Load-bearing equipment

[0033] 120 Detection Platform

[0034] 124 bar light sources

[0035] 130 material handling equipment

[0036] 140 Transfer Equipment

[0037] 150 Cleaning Equipment

[0038] 160 positioning device

[0039] 500 LCD display module

[0040] 501 Backlight Module

[0041] 600 images

[0042] 610 First pixel

[0043] 620 Reference Area

[0044] 630 reference pixels

[0045] 610 pixels

[0046] 620′ Reference Area

[0047] 621 First Reference Area

[0048] 622 Second Reference Area

[0049] 631 First reference pixel

[0050] 632 Second Reference Pixels

[0051] 640 detection area

[0052] 642 First Edge

[0053] 644 Second Edge

[0054] Display area A

[0055] B Surrounding Area

[0056] Steps S11 to S16

[0057] Steps S141~S143

[0058] Steps S151~S159 Detailed Implementation

[0059] The following describes the implementation of the present invention through specific embodiments. The present invention can also be implemented or applied through other different embodiments, and various details in this specification can be modified and changed in various ways to accommodate different viewpoints and applications without departing from the spirit of the present invention.

[0060] Furthermore, the use of ordinal numbers such as "first" and "second" in the specification and the request to modify the elements at the top of the specification and the request does not itself contain or represent any ordinal number of the requested element, nor does it represent the order of one requested element with another requested element, or the order of manufacturing process. The use of these ordinal numbers is only to enable a request element with a certain name to be clearly distinguished from another request element with the same name.

[0061] Furthermore, descriptions such as "when..." or "...when" in this invention refer to "at present, before, or after," and are not limited to simultaneous occurrences; this is explained in advance. Descriptions such as "set on..." in this invention indicate the corresponding positional relationship between two elements, and do not limit the two elements to direct or indirect contact; this is explained in advance. Additionally, the term "connection" in this invention, unless otherwise emphasized, includes both direct and indirect connections. Furthermore, when this invention describes multiple functions (or elements), the use of the word "or" between multiple functions (or elements) indicates that the functions (or elements) can exist independently, but does not exclude the possibility of multiple functions (or elements) existing simultaneously.

[0062] Considering the measurement issues and errors associated with specific quantities (i.e., limitations of the measurement system), descriptions such as "approximately..." and "substantially..." in this document may include the stated numerical value and the acceptable range of deviation that a person skilled in the art could determine. For example, "approximately..." may mean within one or more standard deviations, or within ±20%, ±15%, ±10%, ±5%, or ±3% of the standard value. It should be noted that due to process deviations or instabilities, descriptions such as "same" in this document also include the meaning of "approximately".

[0063] Furthermore, the "grayscale value" mentioned in this invention is between 0 and 255 grayscale, where 0 grayscale is the minimum grayscale value and has the darkest color, such as black; while 225 grayscale is the maximum grayscale value and has the lightest color, such as white. Additionally, the "pixel" mentioned in this invention refers to a pixel unit in image resolution, not the pixels forming a display array in a display panel; this is clarified in advance.

[0064] Furthermore, the effectiveness of the scope of protection of this invention can be demonstrated at least through the components or operating mechanisms of the product, but the methods of demonstration are not limited thereto. Additionally, if software execution steps are involved, demonstration can be demonstrated at least through reverse engineering or based on the instructions in the program code, but the methods of demonstration are not limited thereto.

[0065] Figure 1 This is a schematic diagram of the architecture of a detection device 1 according to an embodiment of the present invention. Figure 2This is a flowchart of the detection method according to an embodiment of the present invention, wherein the detection method can be executed by detection device 1. Figure 1 and Figure 2 As shown, the detection device 1 includes at least an image acquisition device 20 and a region grayscale comparison module 15. The detection method includes at least step S11: acquiring an image, wherein the image contains multiple pixels; wherein step S11 can be performed by the image acquisition device 20. In addition, the detection method also includes step S15: comparing the grayscale value of a first pixel of the image with a reference grayscale value in a reference region, and determining whether the first pixel is abnormal based on the difference between the grayscale value of the first pixel and the reference grayscale value; wherein step S15 can be performed by the region grayscale comparison module 15.

[0066] The detection device 1 may include more components, and the detection method may include more steps. In one embodiment, the detection device 1 includes an exposure detection module 12, and the detection method includes step S12: comparing a first exposure value of an image with a preset brightness value, and adjusting the brightness of the image according to the difference between the first exposure value and the preset brightness value, wherein step S12 can be performed by the exposure detection module 12. In one embodiment, the detection device 1 may include an image filtering module 13, and the detection method includes step S13: comparing a grayscale value to be detected of a pixel in the image with an average grayscale value of a plurality of neighboring pixels (hereinafter referred to as a first average grayscale value), and filtering the pixel to be detected when the difference between the grayscale value to be detected and the first average grayscale value reaches a first preset condition, wherein step S13 can be performed by the image filtering module 13. In one embodiment, the detection device 1 includes an image enhancement module 14, and the detection method includes step S14: enhancing the texture characteristics of the image, wherein step S14 can be performed by the image enhancement module 14. In one embodiment, the detection device 1 includes a storage module 30 and a transmission module 32, and the detection method includes step S16: when the first pixel is determined to be abnormal, a prompt message is transmitted to an external device 40, wherein step S16 can be executed through the storage module 30 and the transmission module 32. It should be noted that, as long as it is reasonable, steps S11 to S16 can be selectively executed or not executed as needed, and the order between steps can also be changed, the details of the steps can also be changed, and the number of components of the detection device 1 can also be increased or decreased as needed; the present invention is not limited thereto.

[0067] Next, the details of the detection device 1 will be described. The detection device 1 may be, for example, an inspection machine used to inspect an image of an object to be tested, such as identifying abnormal parts in the image and analyzing the type of abnormality. For instance, when the object to be tested is a display module, the detection device 1 may be an inspection machine for display modules, used to perform quality inspection on the display module and identify abnormal parts and their types. Details of this will be described in more detail in later paragraphs. The detection device 1 may be an automated device, or it may be a partially manual and partially automated device. Furthermore, the detection device 1 may include an image processing device 10 for processing and analyzing the image. In one embodiment, the image processing device 10 may be a microcontroller, which may include a microprocessor 11. In this case, the image processing device 10 may be integrated into the detection device 1 (e.g., the image processing device 10 may be the microcontroller of the inspection machine itself). In another embodiment, the image processing device 10 may also be an electronic device having a microprocessor 11, such as, but not limited to, a computer, a laptop, a smartphone, a tablet computer, a wearable device, etc. In this case, the image processing device 10 may be located outside the detection device 1 and transmit data to the detection device 1 via wired or wireless transmission, and is not limited thereto.

[0068] In one embodiment, the exposure detection module 12, image filtering module 13, image enhancement module 14, or area grayscale comparison module 15 can be functional modules of the image processing device 10, and can be implemented by the microprocessor 11 executing software. For example, the image processing device 10 can install software (or a computer program product), wherein the software contains multiple program instructions, and the microprocessor 11 can execute these program instructions to implement the functions of the exposure detection module 12, image filtering module 13, image enhancement module 14, or area grayscale comparison module 15, that is, the microprocessor 11 can execute steps S12 to S15, but is not limited thereto. These program instructions can be written in any programming language, and there are no restrictions on how they are written, as long as they enable the microprocessor 11 to execute steps S12 to S15. In addition, the software can be stored in a non-transitory computer-readable medium, such as an optical disc, USB flash drive, hard disk, memory, external hard disk, or network server, etc., and is not limited thereto. In another embodiment, the instructions for performing functions such as the exposure detection module 12, the image filtering module 13, the image enhancement module 14, or the grayscale comparison module 15 of a region can also be directly embedded in the hardware of the image processing device 10 via firmware, for example, embedded in the microcontroller or microprocessor 11, but are not limited thereto.

[0069] Image acquisition device 20 is used to acquire an image of the object under test. Data transmission between image processing device 10 and image acquisition device 20 can be wired or wireless, and there can be a data storage device between them, such as a memory, temporary storage device, USB flash drive, external hard drive, etc., and it is not limited to these. In one embodiment, image acquisition device 20 can be a camera device used to capture images. In one embodiment, image acquisition device 20 may include an optical sensing element, which may be, for example, a charge-coupled device (CCD), complementary metal-oxide-semiconductor (CMOS), etc., and is not limited to these. In one embodiment, image acquisition device 20 is suitable for acquiring images with a resolution of 20 megapixels or higher; for example, the resolution of the image captured by image acquisition device 20 can reach 20 megapixels or higher (meaning 20 megapixels ≤ image resolution). In one embodiment, the resolution of the image captured by image acquisition device 20 is between 20 megapixels and 100 megapixels (meaning 20 megapixels ≤ image resolution ≤ 100 megapixels). In one embodiment, the image acquired by the image acquisition device 20 has a resolution of 29 megapixels, but this is not a limitation. In another embodiment, the image acquisition device 20 may also be a data transmission interface, such as a USB interface or various transmission interfaces, to acquire images from an external device (e.g., an external camera device). In other words, the detection device 1 itself may not have a camera device. The present invention is not limited thereto.

[0070] In one embodiment, the storage module 30 can store the analysis results of the image processing device 10 (e.g., whether the first pixel is abnormal or the type of abnormality of the first pixel). In one embodiment, the storage module 30 can be integrated into the image processing device 10, for example, the storage module 30 can be the memory or hard disk of the image processing device 10 itself, but the storage module 30 can also be independent of the image processing device 10, for example, the storage module 30 can be the memory or hard disk of other devices, external hard disks, USB flash drives, or cloud storage space, and is not limited thereto. In one embodiment, the transmission module 32 can transmit the analysis results of the image processing device 10 to an external device 40. The transmission module 32 can perform wired or wireless transmission. The transmission module 32 can be a transceiver, antenna, wired network card, wireless network card, etc., and is not limited thereto. The transmission module 32 can be integrated into the image processing device 10, but can also be independent of the processing device. In another embodiment, the external device 40 may be, for example, a network server, a remote computer, or something similar, and is not limited to these, and is used to display the analysis results. Therefore, staff can learn about the analysis results of the image processing device 10 through the external device 40 and then review the analysis results.

[0071] In one embodiment, the object under test may be a display module, such as a display panel. The display module includes a display area, which may be implemented using liquid crystal display (LCD), organic light-emitting diode (OLED), micro light-emitting diode (Micro LED), mini light-emitting diode (Mini LED), quantum dot light-emitting diode (QLED), or flexible display technology, and is not limited thereto. In one embodiment, the display module may include a display dielectric layer, which may include a liquid crystal layer, a light-emitting diode layer, an organic light-emitting diode layer, an inorganic light-emitting diode layer, a sub-millimeter light-emitting diode layer, a micro light-emitting diode layer, a quantum dot layer, a fluorescent layer, a phosphorescent layer, various other types of display layers (e.g., electro-wetting display layer, electrophoresis display layer, plasma display layer), or any combination of the above dielectric layers, and is not limited thereto. Furthermore, in one embodiment, the display module may have touch functionality, which can be implemented through, for example, resistive touch sensing technology, capacitive touch sensing technology, ultrasonic touch sensing technology, or optical touch sensing technology, and is not limited thereto. Moreover, for ease of explanation, the following description uses a liquid crystal display module as an example, which will allow those skilled in the art to understand the implementation of other types of display modules.

[0072] To make the present invention clearer, it will be described below in the form of an application of the detection device 1.

[0073] Figure 3 This is a schematic diagram illustrating the application of the detection device 1 according to an embodiment of the present invention. Please also refer to... Figure 1 and Figure 2 .like Figure 3As shown, the testing device 1 can be a testing machine 100 for testing display modules. The testing machine 100 may include a carrier device 110, a testing platform 120, a material handling device 130, a transfer device 140, a cleaning device 150, and a positioning device 160, and the testing machine 100 is also connected to the image processing device 10. The carrier device 110 may be disposed on the periphery of the testing machine 100 and may include a cassette, a tray, or a conveyor belt (or assembly line) to hold the object to be tested. The object to be tested may be, for example, a bare panel of a liquid crystal display module. Here, "bare panel" may refer to a state where the thin-film transistor substrate, liquid crystal layer, and color filter substrate are already set, but the polarizing plate and control circuit are not yet set, but this is not a limitation. The object to be tested may also be a panel with more or fewer components. The material handling device 130 may be, for example, a robotic arm, for moving the object to be tested from the carrier device 110 into the testing machine 100. The transfer device 140 can move the object to be tested onto the detection platform 120 for testing (e.g., a light-up test). The cleaning device 150 can be used to remove dust or impurities from the detection machine 100. The positioning device 160 can assist the transfer device 140 in moving the object to be tested. The image acquisition device 20 can be disposed above the detection platform 120 for capturing images of the object to be tested. In addition, multiple light sources (e.g., but not limited to strip light sources) can be disposed around the detection platform 120 to provide light. Furthermore, the detection machine 100 may have an image processing device 10 or be connected to the image processing device 10. In one embodiment, the carrying device 110, the picking device 130, the transfer device 140, the cleaning device 150, and the positioning device 160 can operate automatically. Since how these devices achieve automation is not the focus of this invention, it will not be described in detail here. It should be noted that... Figure 3 The embodiments are merely examples, and the detection platform 100 can be modified according to requirements.

[0074] Next, the details of the detection method will be explained. For ease of explanation, this embodiment uses a liquid crystal display module as an example. First, the detection environment will be described. Figure 4 This is a detailed schematic diagram of the detection device 1 according to an embodiment of the invention. Please also refer to... Figures 1 to 3 .like Figure 4As shown, the detection device 1 includes a detection platform 120 and a plurality of bar light sources 124. The image acquisition device 20 is a camera device and includes a lens 22 and an optical sensing element 24. The liquid crystal display module 500 includes a backlight module 501 (e.g., but not limited to a backlight panel), and the liquid crystal display module 500 may have a display area A and a non-display area B, wherein the display area A may be defined as a detection area of ​​an image, and the non-display area B may be, for example, the peripheral area of ​​the liquid crystal display module 500. The liquid crystal display module 500 may be placed on the detection platform 120, the lens 22 of the image acquisition device 20 may face the detection platform 120, and the plurality of bar light sources 124 may be disposed on both sides above the liquid crystal display module 500, wherein the bar light sources 124 may be disposed in various feasible ways, such as being locked or pivotally connected to any component of the detection device 1, and are not limited thereto.

[0075] In one embodiment, during detection, the detection device 1 can illuminate the liquid crystal display module 500. For example, the liquid crystal display module 500 is connected to the detection device 1 via a connector to obtain data signals from the detection device 1. The backlight module 501 provides light (e.g., backlight) towards the lens 21, and the liquid crystal display module 500 displays an image (e.g., a single-color image) based on the data signals provided by the detection device 1. Furthermore, the bar light source 124 provides light from both sides of the detection platform 120 towards the liquid crystal display module 500 to enhance the brightness of the image captured by the image acquisition device 12. It should be noted that this detection environment is merely an example and not a limitation; the detection environment can be modified according to actual needs.

[0076] Please refer to this again. Figure 2 Next, the details of steps S11 to S16 will be explained (please also refer to...). Figure 1 and Figure 4 ).

[0077] Regarding step S11, in one embodiment, after the liquid crystal display module 500 is placed on the detection platform 120, the image processing device 10 can transmit an instruction (hereinafter referred to as an image acquisition instruction) to the image acquisition device 12. The image acquisition device 12 then acquires an image (e.g., takes a picture) of the liquid crystal display module 500 according to the image acquisition instruction and transmits the acquired image to the image processing device 10. In one embodiment, the image acquisition instruction can be transmitted by the user operating the image processing device 10; however, in another embodiment, the image acquisition instruction can also be set to be automatically transmitted by the image processing device 10 at a predetermined time; the present invention is not limited thereto.

[0078] Regarding step S12, since the acquired image may be too dark or too bright, which is not conducive to analysis, to address this problem, the exposure detection module 12 can detect a first exposure value of the image and compare the first exposure value with a preset brightness value. When the difference between the first exposure value and the preset brightness exceeds a brightness difference threshold, the exposure detection module 12 adjusts the brightness of the image, for example, brightening or darkening the image. When the difference between the first exposure value and the preset brightness does not exceed the brightness difference threshold, the brightness of the image can be maintained, i.e., the adjusted brightness is zero. In one embodiment, the first exposure value is an average grayscale value of all pixels in the image (referred to as the second average grayscale value), but this is not a limitation. In one embodiment, the preset brightness value is a pre-set value. In one embodiment, the preset brightness value can be changed at any time by the user's settings. In one embodiment, the brightness difference threshold is a pre-set value and can be changed at any time by the user's settings. Furthermore, in one embodiment, the "exposure detection module 12 adjusts the brightness of the image" is achieved by software (e.g., pre-set program instructions) increasing or decreasing the brightness of the image, for example, simultaneously increasing or decreasing the grayscale value of all pixels. In one embodiment, the "exposure detection module 12 adjusts the brightness of the image" by re-capturing the image. For example, when the brightness of the image needs to be adjusted, the detection device 1 can automatically adjust the brightness or position of the bar light source 124 or the backlight module 501, and cause the image acquisition device 20 to re-capture the image. The present invention is not limited thereto.

[0079] Regarding step S13, since some pixels in the image may have significantly abnormal grayscale values ​​(e.g., the grayscale values ​​differ greatly from those of neighboring pixels), these significantly abnormal grayscale values ​​may affect the analysis. Therefore, these significantly abnormal grayscale values ​​can be considered noise. The image filtering module 13 can detect whether the grayscale value of each pixel is noise and filter out the noise. In one embodiment, when the difference between the grayscale value of the pixel to be detected (hereinafter referred to as the grayscale value to be detected) and the average grayscale value (first average grayscale value) of multiple neighboring pixels of the pixel to be detected reaches a first preset condition, for example, exceeding a preset value, the image filtering module 13 determines the pixel to be detected as noise and filters it. In one embodiment, the preset condition is 3 grayscale or more, that is, when the difference between the grayscale value to be detected and the average grayscale value exceeds 3 grayscale, the pixel to be detected will be identified as noise. In one embodiment, the preset condition may be between 3 and 20 grayscale. Furthermore, in one embodiment, "image filtering module 13 performs filtering" by modifying the grayscale value to be detected to the average grayscale value of its neighboring pixels, but this is not a limitation. In another embodiment, neighboring pixels are defined as a plurality of pixels surrounding the pixel to be detected, with at least some of these pixels being adjacent to the pixel to be detected. In one embodiment, the number of neighboring pixels is at least two. For example, if the pixel to be detected is located in a corner of the display screen, it will be adjacent to at least two pixels; if the pixel to be detected is located in the center of the display screen, it will be adjacent to at least four pixels. Furthermore, the number and definition of neighboring pixels can be changed as needed, and the present invention is not limited thereto. Thus, step S13 can eliminate significant noise.

[0080] Step S14 can enhance the image, making less noticeable abnormal pixels stand out. Regarding step S14, the image enhancement module 14 can enhance the texture characteristics of the image, making the texture characteristics in the image more apparent. Figure 5 This is an embodiment of the present invention. Figure 2 Please refer to the detailed flowchart of step S14, and also refer to... Figure 1 , Figure 2 and Figure 4 .like Figure 5As shown, step S14 may include step S141: the image enhancement module 14 divides the image into multiple sub-blocks. In one embodiment, the image enhancement module 14 can perform subsequent processing on each sub-block simultaneously, thus improving the overall processing speed of step S14, but this is not a limitation. In one embodiment, the number of sub-blocks is equal to the number of pixels in the image, that is, each pixel can be considered as a sub-block. Therefore, if the number of pixels is 2900, the number of sub-blocks can be 2900. In another embodiment, the number of sub-blocks is less than the number of pixels in the image, meaning that one sub-block has multiple pixels, but this is not limited to this. In one embodiment, step S141 may also be omitted.

[0081] Furthermore, step S14 may include step S142: the image enhancement module 14 obtains the texture characteristics of the sub-block based on the image response frequency and directionality in each sub-block. In one embodiment, the image response frequency is defined as the grayscale value change among multiple adjacent pixels, such as the magnitude of the change, but is not limited thereto. In one embodiment, directionality is defined as the direction of grayscale value change among multiple adjacent pixels, such as the grayscale value increasing or decreasing along a specific direction, but is not limited thereto. The image enhancement module 14 may also obtain the texture characteristics of the sub-block in other ways, such as various image processing techniques, which are not limited in this invention.

[0082] Step S14 may include step S143: the image enhancement module 14 enhances the texture characteristics of each sub-block. In one embodiment, "enhancing the texture characteristics of each sub-block" may be defined by using a specific grayscale value as a threshold, adjusting grayscale values ​​higher than this specific grayscale value to be closer to a maximum grayscale value (e.g., closer to 255 grayscale), and adjusting grayscale values ​​lower than this specific grayscale value to be closer to a minimum grayscale value (e.g., closer to 0 grayscale). Therefore, lighter areas in the image will be lighter, and darker areas will be darker, but this is not a limitation. In one embodiment, "enhancing texture characteristics" can be achieved through various existing image processing techniques, and this invention is not limited thereto. This allows for more noticeable abnormal parts in the image when the entire display module displays a screen of the same color. Furthermore, in one embodiment, if the display module has a display area and a non-display area, this step can also make the difference between the texture characteristics of the display area and the non-display area in the image more obvious. Therefore, the image processing device 10 can identify the range of the display area and define the display area as a detection area, but this is not a limitation.

[0083] Regarding step S15, the region grayscale comparison module 15 can analyze each abnormal part in the image to determine the abnormal type of the abnormal part. In one embodiment, the abnormal type includes at least point-type abnormalities, line-type abnormalities, and brightness unevenness, but is not limited to these. In one embodiment, the smallest unit of the abnormal part is a single pixel, and for ease of explanation, the abnormal part will be described as a single pixel below.

[0084] First, the determination of point-type anomalies will be explained. A point-type anomaly may be, for example, a situation where a single pixel in a display screen is displayed incorrectly. Figure 6(A) is an embodiment of the present invention. Figure 2 A detailed flowchart of step S15 is shown in Figure 6(B), which is a schematic diagram of the first pixel 610 of image 600 in Figure 6(A) and the reference region 620 corresponding to the first pixel 610. The first pixel 610 is defined as the pixel being detected in this step. The reference region 620 includes multiple reference pixels 630, and references are also provided. Figure 1 , Figure 2 , Figure 4 and Figure 5 .

[0085] As shown in Figures 6(A) and 6(B), step S15 may include step S151: the region grayscale comparison module 15 obtains the grayscale value of the first pixel 610 of the image 600 (hereinafter referred to as the first pixel grayscale value) to begin detection of the first pixel 610. The region grayscale comparison module 15 may identify the grayscale value in various ways, such as a lookup table, but is not limited to this.

[0086] Step S15 may also include step S152: the region grayscale comparison module 15 obtains the grayscale values ​​of all reference pixels 630 in the reference region 620, and sets the grayscale value of a majority of the reference pixels 630 as the reference grayscale value. In one embodiment, the reference region 620 is defined as a region formed by extending N reference pixels 630 from the four sides of the first pixel 610 in a direction away from the first pixel 610, where N is a positive integer, and "the four sides of the first pixel 610" may be, for example, the positions of the four pixels adjacent to the first pixel 610 (e.g., the left, right, top, and bottom of the first pixel 610), but is not limited thereto. In one embodiment, the reference region 620 includes N reference pixels 630 extending in a first direction (defined as the X direction), N reference pixels 630 extending in the opposite direction of the first direction, N reference pixels 630 extending in a second direction (defined as the Y direction), and N reference pixels 630 extending in the opposite direction of the second direction, wherein the first direction and the second direction are orthogonal. In one embodiment, N is between 5 and 20 (i.e., 5 ≤ N ≤ 20). In another embodiment, N is 10. The invention is not limited thereto. In one embodiment, reference pixel 630 does not include first pixel 610.

[0087] It should be noted that since the display module displays a single color image when it is turned on, ideally, the grayscale values ​​of all pixels in image 600 should be consistent. However, the display module may have some defects (such as point anomalies, line anomalies, or uneven brightness), and the grayscale values ​​of a few pixels may be abnormal. Furthermore, since most pixels in the image are still normal pixels, the grayscale values ​​of most pixels in reference pixel 630 should also be normal grayscale values; therefore, the reference grayscale value can be considered a normal grayscale value. In one embodiment, "most pixels" refers to more than half of the pixels, but this is not a limitation.

[0088] Therefore, step S15 can also include step S153: the region grayscale comparison module 15 compares the grayscale value of the first pixel with the reference grayscale value. When the grayscale value of the first pixel differs from the reference grayscale value, it determines that the first pixel is a point-type anomaly. Since "the grayscale value of the first pixel differs from the reference grayscale value" means that the grayscale value of the first pixel is different from the grayscale values ​​of most pixels in the image, the probability that the first pixel 610 is an anomalous pixel is very high. Therefore, the region grayscale comparison module 15 can determine that the first pixel 610 is a point-type anomaly. The present invention is not limited thereto.

[0089] Furthermore, the steps in Figure 6(A) can be rearranged or added / removed if appropriate. Also, referring again to Figure 6(B), in one embodiment, when the perimeter of a detected pixel (e.g., the second pixel 610) cannot extend along a certain direction, that extension can be omitted, and the range of the reference region 620' corresponding to pixel 611 can be adjusted accordingly. The invention is not limited thereto. Additionally, the invention can employ other methods to perform point-type anomaly detection.

[0090] Next, the determination of line-related anomalies will be explained. "Line-related anomalies" can be, for example, a situation where an entire row or column of pixels in the display screen is displayed incorrectly, thus creating problems such as vertical or horizontal lines on the display screen. Figure 7(A) shows another embodiment of the present invention. Figure 2 The detailed flowchart of step S15 is shown in Figure 7(B), which is a schematic diagram of the first pixel 610 and the reference area 620 in Figure 7(A). Please also refer to... Figure 1 , Figure 2 , Figure 4 and Figure 5 .

[0091] As shown in Figure 7(A), step S15 may include step S154: the region grayscale comparison module 15 obtains the first pixel grayscale value of the first pixel 610 of the image 600 to detect the first pixel 610. Since the description of step S154 is applicable to step S151, its details will not be described in detail.

[0092] Furthermore, step S15 may include step S155: the region grayscale comparison module 15 obtains the reference grayscale values ​​of all pixels in the reference region 620 corresponding to the first pixel 610, wherein the reference region 620 includes a first reference region 621 and a second reference region 622. The first reference region 621 includes a plurality of first reference pixels 631, and the second reference region 622 includes a plurality of second reference pixels 632. In one embodiment, the image 600 may include a detection region 640, wherein the detection region 640 may be defined as the display region of the display module, and the detection region 640 includes a first edge 642 and a second edge 644, wherein the first edge 642 and the second edge 644 are opposite to each other.

[0093] In one embodiment, the first reference pixel 631 is defined as all pixels extending from the first pixel 610 along a first direction (e.g., defined as the X direction) to the first edge 642 of the detection region 640, and all pixels extending from the first pixel 610 along the opposite direction of the first direction (e.g., defined as the negative X direction) to the second edge 644 of the detection region 640. In other words, the first reference pixel 631 can be defined as all pixels other than the first pixel 610 in a pixel column containing the first pixel 610.

[0094] In one embodiment, the second reference pixel 632 comprises N pixels extending from the first pixel 610 along a second direction (e.g., defined as the Y direction) and N pixels extending from the first pixel 610 along the opposite direction of the second direction (e.g., defined as the negative Y direction), wherein the first direction is orthogonal to the second direction. In one embodiment, N is between 5 and 20 (i.e., 5 ≤ N ≤ 20). In one embodiment, N is 10. The invention is not limited thereto. In one embodiment, the reference pixel value is defined as the grayscale value possessed by a majority of the pixels in the second reference pixels 632. In one embodiment, a majority of pixels refers to more than half of the pixels, but is not limited thereto.

[0095] Additionally, step S15 may include step S156: the region grayscale comparison module 15 compares the grayscale value of the first pixel with the grayscale values ​​of all first reference pixels 631, and compares the grayscale value of the first pixel with the reference grayscale value. When the grayscale value of the first pixel is the same as the grayscale value of all first reference pixels 631, and the grayscale value of the first pixel is different from the reference grayscale value (i.e., the grayscale value possessed by most pixels in the second reference pixels 632), the region grayscale comparison module 15 determines that the first pixel 610 is a line-type abnormality. More specifically, if the grayscale value of the first pixel is the same as the grayscale value of all first reference pixels 631, it means that the grayscale values ​​of all pixels in the pixel column where the first pixel 610 is located are the same. If the grayscale value of the first pixel is different from the reference grayscale value, it means that the grayscale value of the pixel column where the first pixel 610 is located is not the same as the grayscale values ​​of most other pixel columns. Therefore, the pixel column where the first pixel 610 is located may be a pixel column with display abnormalities, such as a horizontal line. Therefore, the area grayscale comparison module 15 can determine that the first pixel 610 is a line-type anomaly. This invention is not limited thereto.

[0096] Furthermore, although the embodiments in Figures 7(A) and 7(B) are illustrated using the example of detecting horizontal line anomalies, those skilled in the art can learn from them how to detect vertical line anomalies (e.g., by changing the first direction to the Y direction and the second direction to the X direction).

[0097] Furthermore, the steps in Figure 7(A) can be rearranged or added / removed if appropriate. Additionally, as in the embodiment of Figure 6(B), when the currently detected pixel cannot be extended in a certain direction (e.g., when that direction already belongs to the edge of detection area 640), extension in that direction can be omitted, and the range of the reference area can be adjusted accordingly. The invention is not limited thereto. Furthermore, the invention can also employ other methods to perform line anomaly detection.

[0098] Next, the method for judging uneven brightness will be explained. Figure 8 This is another embodiment of the present invention. Figure 2 Please refer to the detailed flowchart of step S15, and also refer to... Figure 1 , Figure 2 , Figure 4 and Figure 5 .like Figure 8 As shown, step S15 may include step S157: the region grayscale comparison module 15 obtains the first pixel grayscale value of the first pixel of the image to detect the first pixel. In one embodiment, the first pixel may also be replaced by a first region formed by multiple pixels, and the first pixel grayscale value may also be replaced by the average grayscale value of the pixels in the first region. The present invention is not limited thereto.

[0099] Step S15 may include step S158: the region grayscale comparison module 15 obtains a reference grayscale value in the reference region, wherein the reference region is defined as the detection region of the entire image (e.g., the display region of the display module). In one embodiment, the reference grayscale value is an average grayscale value of all pixels in the detection region of the entire image (referred to as the third average grayscale value), and in another embodiment, the reference grayscale value is set as an average grayscale value of all pixels outside the first pixel (or the first region) in the detection region (referred to as the fourth average grayscale value). The present invention is not limited thereto.

[0100] Step S15 may include step S159: the region grayscale comparison module 15 compares the grayscale value of the first pixel with the reference grayscale value. When the difference between the grayscale value of the first pixel and the reference grayscale value exceeds a preset difference value, the first pixel (or the first region) is determined to be an abnormal type of uneven brightness. In one embodiment, the preset difference value is a pre-set threshold value that can be changed at any time by the user. In one embodiment, the preset difference value may be between 5 and 10 grayscale values. This allows the abnormal portion of the image with uneven brightness to be identified. The present invention is not limited to this. Alternatively, the present invention may employ other methods to perform the detection of uneven brightness.

[0101] Regarding step S16, once an abnormal pixel or abnormal type is identified, the image processing device 10 can record the location or type of the abnormal pixel through the storage module 30, and transmit this information to an external device 40, such as a server, through the transmission module 32. Inspectors can then further re-inspect based on this information. Therefore, inspectors can directly re-inspect the abnormal parts, saving significant time and manpower.

[0102] Therefore, the testing method provided by this invention enables the testing device to automatically detect the quality of display modules, thereby reducing labor costs and improving efficiency before the display modules are shipped. Furthermore, the testing device can maintain the quality of each test, avoiding the incomplete inspection problems caused by fatigue or carelessness during manual testing.

[0103] The display module tested in this invention can be applied to any device that requires a screen display function, such as display devices, mobile phones, laptops, desktop computers, watches, virtual reality (VR) displays, cameras, music players, mobile navigation devices, televisions, automotive dashboards, center consoles, electronic rearview mirrors, head-up displays, financial devices, and is not limited thereto.

[0104] Although the present invention has been described through preferred embodiments, it is understood that other possible modifications and variations that do not depart from the spirit and scope of the claims are also within the protection scope of the present invention.

Claims

1. A detection method, characterized in that, Includes the following steps: Obtain an image containing multiple pixels; The image's brightness is adjusted based on the difference between the first exposure value and the preset brightness value. A gray value to be detected of a pixel in the image is compared with an average gray value of a plurality of neighboring pixels. When the difference between the gray value to be detected and the average gray value of the plurality of neighboring pixels reaches a first preset condition, the pixel to be detected is filtered. Enhance the texture properties of the image; and The gray value of a first pixel in the image is compared with a reference gray value in a reference region, and the difference between the gray value of the first pixel and the reference gray value is used to determine whether the first pixel is abnormal. The image includes a detection region. This reference region includes a plurality of first reference pixels and a plurality of second reference pixels. The first reference pixels are defined as a plurality of pixels extending from the first pixel along a first direction to a first edge of the detection region, and a plurality of pixels extending from the first pixel along the opposite direction to a second edge of the detection region, wherein the first edge and the second edge are opposite. The second reference pixels include N pixels extending from the first pixel along a second direction, and N pixels extending from the first pixel along the opposite direction of the second direction, wherein the first direction and the second direction are orthogonal, N is a positive integer greater than 0, and the reference pixel value is defined as a grayscale value possessed by a majority of the second reference pixels. The method further includes the step of: when the gray value of the first pixel is the same as the gray values ​​of the first reference pixels, and the gray value of the first pixel is different from the value of the reference pixel, the first pixel is determined to be a line defect type.

2. The detection method according to claim 1, characterized in that, The reference region is a region formed by extending N pixels from each of the four sides of the first pixel, where N is a positive integer greater than 0. The reference gray value is defined as a gray value possessed by a majority of pixels in the reference region. The method further includes the step of: When the gray value of the first pixel differs from the reference gray value, the first pixel is determined to be a point-type defect.

3. The detection method according to claim 1, characterized in that, The reference region is a detection region of the image, and the reference gray value is the average gray value of all pixels in the detection region. The method further includes the step of: When the difference between the gray value of the first pixel and the reference gray value exceeds a preset difference, the first pixel is determined to be of the uneven brightness type (mura).

4. The detection method according to claim 1, characterized in that, The image is an image of a display module, and the first exposure value is the average grayscale value of all pixels in the image when the display module is turned on.

5. A detection device, characterized in that, Include: An image acquisition device for acquiring an image, wherein the image contains a plurality of pixels; An exposure detection module is used to compare a first exposure value of the image with a preset brightness value, and adjust the brightness of the image according to the difference between the first exposure value and the preset brightness value; An image filtering module is used to compare a gray value to be detected of a pixel in the image with an average gray value of a plurality of neighboring pixels. When the difference between the gray value to be detected and the average gray value of the plurality of neighboring pixels reaches a first preset condition, the pixel to be detected is filtered. An image enhancement module is used to enhance the texture properties of the image; and A region grayscale comparison module is used to compare the grayscale value of a first pixel of the image with a reference grayscale value in a reference region, and to determine whether the first pixel is abnormal based on the difference between the grayscale value of the first pixel and the reference grayscale value. The image includes a detection region. This reference region includes a plurality of first reference pixels and a plurality of second reference pixels. The first reference pixels are defined as a plurality of pixels extending from the first pixel along a first direction to a first edge of the detection region, and a plurality of pixels extending from the first pixel along the opposite direction to a second edge of the detection region, wherein the first edge and the second edge are opposite. The second reference pixels include N pixels extending from the first pixel along a second direction, and N pixels extending from the first pixel along the opposite direction of the second direction, wherein the first direction and the second direction are orthogonal, N is a positive integer greater than 0, and the reference grayscale value is defined as a grayscale value possessed by a majority of the pixels among the second reference pixels. When the gray value of the first pixel is the same as the gray values ​​of the first reference pixels, and the gray value of the first pixel is different from the gray value of the reference pixels, the gray value comparison module of the region determines that the first pixel is a line defect anomaly.

6. The detection device according to claim 5, characterized in that, The reference region is a region formed by extending N pixels from each of the four sides of the first pixel, where N is a positive integer greater than 0. The reference gray value is defined as a gray value possessed by the majority of pixels in the reference region. When the gray value of the first pixel is different from the reference gray value, the gray value comparison module of the region determines that the first pixel is a point-type defect.

7. The detection device according to claim 5, characterized in that, The reference area is a detection area of ​​the image, and the reference gray value is an average gray value of all pixels in the detection area. When the difference between the gray value of the first pixel and the reference gray value exceeds a preset difference, the gray value comparison module of the area determines that the first pixel is a brightness non-uniformity abnormality type.

8. The detection device according to claim 5, characterized in that, The image is an image of a display module, and the first exposure value is the average grayscale value of all pixels in the image when the display module is turned on.

Images