CN111504608B - Brightness uniformity detection system and brightness uniformity detection method - Google Patents

Abstract

The brightness uniformity detection system is used for detecting an object to be detected and is provided with an image sensor, a storage unit and a processing device. The image sensor is used for capturing an image including an object to be detected so as to acquire a plurality of gray scale information in the image. The storage unit is used for storing a plurality of luminance curves corresponding to the detection position information. The processing device is used for acquiring a plurality of pieces of to-be-detected gray scale information corresponding to a plurality of detection positions on the to-be-detected object from the gray scale information of the image according to the image and the detection position information. The processing device also respectively obtains a plurality of pieces of estimated luminance information at the detection positions according to the luminance curve and the gray scale information to be detected, and judges whether the luminance of the object to be detected is uniform or not according to the difference of the estimated luminance information among the detection positions of the object to be detected. The brightness uniformity detection system and the brightness uniformity detection method do not need to measure each detection position one by one, and can effectively save the time consumed by detection and reduce the manpower required by detection.

Description

Brightness uniformity detection system and brightness uniformity detection method

technical field

The present invention relates to a detection technology, and in particular, to a luminance uniform detection system and a luminance uniform detection method.

Background technique

In order to ensure the yield rate, before leaving the factory, the panels are all subject to brightness inspection to ensure uniform brightness. Existing detection methods mostly use automatic optical detection machines equipped with luminance measurement equipment to measure a plurality of detection positions on the panel respectively, and determine the measured brightness according to the luminance measured at these detection positions. Whether the brightness of the panel is uniform.

However, the luminance measuring device can only measure the luminance value of a single position on the panel at a time. Therefore, it takes a long time to measure all the detection positions on the single panel. In particular, for each detection position, the luminance measurement device must repeat the measurement after a period of time to take the average of several times as the luminance value at the detection position, resulting in a rather lengthy overall measurement time. In addition, in the process of measuring luminance, testers are required to assist in confirming the relationship between the automatic optical inspection machine and the detection position of the panel to be tested, so as to ensure that the measurement position of the automatic optical inspection machine is indeed detection location. Therefore, how to reduce the time-consuming and labor-intensive situation during detection is the subject of those skilled in the art.

The "Background Art" paragraph is only used to help understand the content of the present invention, so the content disclosed in the "Background Art" paragraph may contain some that do not constitute the conventional technology known to those skilled in the art. The content disclosed in the "Background Art" paragraph does not represent the content or the problem to be solved by one or more embodiments of the present invention, and has been known or recognized by those skilled in the art before the application of the present invention.

SUMMARY OF THE INVENTION

The present invention provides a brightness uniform detection system and a brightness uniform detection method, so as to reduce the time and labor cost during detection.

To achieve one or part or all of the above objectives or other objectives, an embodiment of the present invention provides a luminance uniform detection system. The luminance uniform detection system of the present invention is used for detecting an object to be tested, and has an image sensor, a storage unit and a processing device. The image sensor is used for capturing an image including the object to be tested, so as to obtain a plurality of grayscale information in the image. The storage unit is used for storing a plurality of luminance curves corresponding to the detected position information. The processing device is connected to the image sensor and the storage unit. The processing device is used for acquiring a plurality of gray-scale information to be tested for a plurality of detection positions on the corresponding object to be tested from the gray-scale information of the image according to the image and the detection position information. In addition, the processing device also obtains a plurality of estimated luminance information at the detection position according to the luminance curve and the grayscale information to be measured, and judges the object to be tested according to the difference of the estimated luminance information between the detection positions of the object to be tested. whether the brightness is uniform.

In order to achieve one or part or all of the above objectives or other objectives, an embodiment of the present invention provides a luminance uniform detection method for detecting an object to be tested. The luminance uniform detection method has the following steps: capturing an image including the object to be tested to obtain a plurality of gray-scale information located in the image; obtaining corresponding to-be-measured information from the gray-scale information of the image according to the image and a detection position information obtaining a plurality of grayscale information to be detected at a plurality of detection positions on the object; obtaining a plurality of estimated luminance information at the detection position according to the plurality of luminance curves of the corresponding detection position information and the grayscale information to be detected; and The difference in the estimated brightness information between the detection positions of the object to be measured determines whether the brightness of the object to be measured is uniform.

Based on the above, the brightness uniformity detection system and brightness uniformity detection method of the present invention do not need to measure each detection position one by one, and the brightness uniformity detection system and brightness uniformity detection method can effectively save the time required for detection. In addition, the luminance uniformity detection system and the luminance uniformity detection method do not need to locate each detection position individually, thus reducing the manpower required for detection.

In order to make the above-mentioned features and advantages of the present invention more obvious and easy to understand, the following specific embodiments are given and described in detail in conjunction with the accompanying drawings as follows.

Description of drawings

FIG. 1 is a system schematic diagram of a luminance uniformity detection system according to an embodiment of the present invention.

FIG. 2 is a schematic diagram of a circuit connection of a luminance uniformity detection system according to an embodiment of the present invention.

FIG. 3 is a flowchart of a method for detecting uniform luminance according to an embodiment of the present invention.

FIG. 4 is a schematic diagram illustrating a detection position on a DUT according to an embodiment of the present invention.

FIG. 5 is a detailed flow chart of a method for detecting uniform luminance according to an embodiment of the present invention.

FIG. 6 is a schematic image diagram of an object positioning procedure according to an embodiment of the present invention.

FIG. 7 is a schematic image diagram of a texture analysis program according to an embodiment of the present invention.

FIG. 8 is a schematic diagram of a texture image and an image associated with an object to be tested according to an embodiment of the present invention.

FIG. 9 is a flowchart of a method for detecting uniform luminance according to an embodiment of the present invention.

FIG. 10 is a detailed flowchart of a method for detecting uniform luminance according to an embodiment of the present invention.

List of reference signs

10: Object to be tested

100: Brightness uniform detection system

110: Image sensor

120: storage unit

130: Processing device

S310-S340, S510-S560, S910-S960, S1010-S1060: steps.

Detailed ways

FIG. 1 is a system schematic diagram of a luminance uniformity detection system according to an embodiment of the present invention. Referring to FIG. 1 , in an embodiment of the present invention, the luminance uniformity detection system 100 is used to detect the object to be tested 10 . For example, the object to be tested 10 may be a panel or a backlight module suitable for a panel. The present invention is not limited to this. Furthermore, in an embodiment of the present invention, the luminance uniformity detection system 100 detects a plurality of detection positions of the object to be tested 10 to determine whether the luminance of the detection positions of the object to be tested 10 is uniform.

FIG. 2 is a schematic diagram of a circuit connection of a luminance uniformity detection system according to an embodiment of the present invention. The circuit connection of the luminance uniformity detection system of FIG. 2 is applicable to at least the luminance uniformity detection system of FIG. 1 . Hereinafter, the components of the luminance uniformity detection system according to an embodiment of the present invention will be described with reference to FIG. 1 and FIG. 2 at the same time. Specifically, the luminance uniform detection system 100 includes an image sensor 110 , a storage unit 120 and a processing device 130 .

The image sensor 110 is used to capture images including the object to be tested 10 . In an embodiment of the present invention, the image sensor 110 is, for example, a sensor using a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) sensor. , but the present invention is not limited to this. The image sensor 110 first converts the sensed light into a current signal and then converts it into a digital signal, wherein the converted digital signal corresponds to the grayscale information of the image. Specifically, when the object to be tested 10 emits currents corresponding to different gray levels, the image sensor 110 will obtain the luminance values of multiple detection positions on the object to be tested 10 at different gray levels, so as to obtain the brightness values of the object to be tested. Multiple grayscale information of multiple detection positions on the object 10. In one embodiment, the image is imaged on the display of the processing device 130 according to the digital signal.

The storage unit 120 stores a plurality of luminance curves corresponding to the detected position information. Specifically, the detection position information is a plurality of detection positions on the object to be tested 10, and the detection positions are determined by the detection personnel and established in the luminance uniform detection system 100. The present invention does not limit the number and actual detection positions Corresponding to the position above the object to be tested 10 . In addition, the storage unit 120 stores the corresponding luminance curve of each detection position respectively. Specifically, the luminance curve is used to describe the corresponding relationship between gray scales and luminance. If it is described by two-dimensional coordinates, the horizontal axis of the two-dimensional coordinates, for example, will record the gray scale, and the vertical axis will record the brightness, so that the corresponding brightness of each gray scale can be known. In an embodiment of the present invention, the luminance curve can be represented in the form of a mathematical function or a table, so as to record the corresponding luminance situation under each gray scale. The present invention does not limit the manner of describing the luminance curve. In an embodiment of the present invention, the storage unit 120 may be various types of non-volatile memory, such as hard disk drive (HDD) and solid-state drive (SSD) storage. device, although the present invention is not limited to this.

The processing device 130 is connected to the image sensor 110 and the storage unit 120 . The processing device 130 is used for executing various operations of the luminance uniformity detection system 100 , and the detailed details will be described later. In an embodiment of the present invention, the processing device 130 is, for example, a central processing unit (Central Processing Unit, CPU), a microprocessor (Microprocessor), a digital signal processor (Digital Signal Processor, DSP), a programmable controller, The present invention is not limited to a programmable logic device (Programmable Logic Device, PLD) or other similar devices or a combination of these devices. In the embodiment of the present invention, the storage unit 120 can be integrated in the processing device 130, or can be independently built outside the processing device 130, and is electrically or communicatively connected (for example, through wi-fi, local area network, etc.) route, etc.) to the processing device 130 for the processing device 130 to access, the present invention is not limited to this.

FIG. 3 is a flowchart of a method for detecting uniform luminance according to an embodiment of the present invention. The luminance uniformity detection method of FIG. 3 is at least applicable to the luminance uniformity detection systems of FIGS. 1 and 2 . The operation process of the luminance uniformity detection system 100 according to an embodiment of the present invention and the details of the luminance uniformity detection method will be described below simultaneously with reference to FIGS. 1 to 3 .

In step S310 , the image including the object to be tested 10 is captured by the image sensor 110 to obtain a plurality of grayscale information in the image. Specifically, in an embodiment of the present invention, the image sensor 110 generates a digital signal through the sensed light, and generates a corresponding image based thereon. Moreover, what the digital signal represents is the grayscale information in the image. The image captured by the image sensor 110 includes the object to be tested 10 , but is not limited to the object to be tested 10 . For example, the image captured by the image sensor 110 may include a crawler that transports the object 10 under test, and the present invention does not limit the content of the image captured by the image sensor 100 .

In step S320, the processing device 130 acquires a plurality of gray-scale information to be detected for a plurality of detection positions on the corresponding object to be tested from a plurality of gray-scale information of the image according to the image and the detection position information. Please refer to FIG. 4 , which is a schematic diagram illustrating a detection position on the object to be tested according to an embodiment of the present invention. In an embodiment of the present invention, there are 13 detection positions in total, which are evenly distributed on the object to be tested 10 , however, the present invention is not limited to this. In other embodiments, there may be 9 or 25 detection positions. In addition, the inspection positions at least meet the necessary inspection positions for industrial quality control inspection, and are pre-established by inspection personnel and stored in the storage unit 120 . Based on this, the processing device 130 can further acquire the grayscale information corresponding to each detection position on the object to be tested according to the detection position of the object to be tested and the plurality of grayscale information obtained in step S310, as the grayscale information to be detected. level information.

In step S330, the processing device 130 obtains a plurality of estimated luminance information at a plurality of detection positions respectively according to a plurality of luminance curves and a plurality of gray scale information to be measured. Due to factors such as errors in the image sensor 110 , characteristics of the object to be tested 10 and other factors, the luminance presented by different positions on the object to be tested 10 under the same brightness may be different. Therefore, the storage unit 120 stores the luminance curve corresponding to each detection position respectively, and enables the processing device 130 to obtain the luminance curve corresponding to each detection position and the grayscale information corresponding to each detection position to obtain the corresponding luminance curve at each detection position. Estimated luminance information corresponding to the grayscale information to be measured at the detection position.

As mentioned above, the luminance curve can be represented in the form of a mathematical function or a table, so as to record the corresponding luminance situation under each gray scale. If the luminance curve is recorded in the form of a mathematical function, the processing device 130 will calculate the estimated luminance information in real time by inputting the grayscale information to be measured into the mathematical function. If the luminance curve is recorded in the form of a table, the processing device 130 can obtain the estimated luminance information by looking up the table, but the present invention is not limited to this.

In step S340 , the processing device 130 determines whether the brightness of the object to be tested 10 is uniform or not according to the difference of the plurality of estimated brightness information among the plurality of detection positions of the object to be tested 10 . In an embodiment of the present invention, the processing device 130 determines whether the object to be tested 10 is uniform according to the difference between the maximum estimated luminance and the minimum estimated luminance in the estimated luminance information corresponding to all detection positions. If the difference between the maximum estimated luminance and the minimum estimated luminance does not exceed a certain threshold value (for example, the difference value is less than 5%), it is determined that the luminance on the object to be tested 10 is uniform; otherwise, it is determined that the object to be tested is uniform. 10 has uneven brightness. However, the present invention is not limited to this.

It is worth mentioning that, in the embodiment of the present invention, the processing device 130 can respectively obtain the gray scales to be tested of the detection positions of more than 10 or all the detection positions of the corresponding object to be tested according to an image obtained by the image sensor 110 . information, and obtain the corresponding estimated luminance information accordingly, it is not necessary to take images for each detection position separately, which effectively saves the time required for detection.

FIG. 5 is a detailed flow chart of a method for detecting uniform luminance according to an embodiment of the present invention. Please refer to FIG. 1 to FIG. 5 at the same time. The following will explain more clearly how the processing device 130 according to the image and the detection position information in the luminance uniformity detection system and the luminance uniformity detection method according to the image and the detection position information, from the image A plurality of gray-scale information to be detected of a plurality of detection positions on the corresponding object to be tested is obtained from the plurality of gray-scale information.

In step S510, the processing device 130 executes a positioning procedure on the image to obtain the position of the object to be tested 10 in the image, and determines the position of the detection position in the image according to the position of the object to be tested 10 in the image and the detection position information. In detail, as mentioned above, in addition to the object to be tested 10 , there may be other objects in the image sensed by the image sensor 110 . Therefore, in this embodiment, the processing device 130 further performs an object positioning procedure on the image to find the position of the object to be measured 10 in the image.

Please refer to FIG. 5 and FIG. 6 at the same time. FIG. 6 is a schematic image diagram of an object positioning procedure according to an embodiment of the present invention. The process of executing the object localization procedure on the image by the processing device 130 will be described below with reference to FIG. 6 .

First, as shown in (1) of FIG. 6 , the processing device 130 performs binarization processing on the image, so that the image is presented in black and white.

As shown in (2) in FIG. 6 , the processing device 130 performs denoising on the binarized image to remove the part that does not belong to the object to be tested 10 . In detail, it can be seen in the image of FIG. 6( 1 ) that the panel corresponds to a square block with a larger area and a relatively complete area in the middle, and there is noise around the image that does not belong to the object to be tested 10 . The noise is mostly formed by points, and does not form a large and complete quadrilateral space. Therefore, the processing device 130 can perform operations on the image to find the closed space in the image, (ie, a quadrilateral area with a large area and completeness), and filter out other parts that belong to noise (ie, as shown in FIG. 6(1) ). four corners) to acquire images belonging to the object to be tested 10 .

As shown in (3) of FIG. 6 , the processing device 130 performs edge detection on the part of the object to be tested 10 to obtain the edge of the corresponding object to be tested 10 .

As shown in (4) of FIG. 6 , the processing device 130 further performs line detection on the edge of the object to be tested 10 to find a line equation corresponding to the edge of the object to be tested 10 . Since the DUT 10 shown here is a panel, the processing device 130 obtains four straight line equations.

As shown in (5) of FIG. 6 , after obtaining the four straight line equations, the processing device 130 can respectively obtain the intersection position with the straight line equations corresponding to the two adjacent sides, and the intersection position is the vertex position of the object to be tested 10 . Based on this, the processing device 130 acquires the position of the object to be tested 10 in the image, and completes the object positioning procedure. At this time, since the position of the object to be tested 10 in the image is known, the processing device 130 can further obtain the positions of the plurality of detection positions on the object to be tested in the image according to the vertex position of the object to be tested 10 . It should be noted that FIG. 6(1) to FIG. 6(5) are only schematic diagrams corresponding to the operation results of the processing device 130, and the image of FIG. 6 may not be actually drawn during the operation of the uniform luminance detection system 100. .

In step S520 , the processing device 130 executes a texture analysis program on the image to obtain a texture image including the object to be tested 10 . In detail, if the object to be tested 10 is stained with dirt, for example, fingerprints, dander or damage, etc., although it will be removed by the cleaning process in the subsequent process, it will affect the detection process. Test results. Therefore, the processing device 130 executes the texture analysis program on the image to find the dirty position on the panel, so as to avoid misjudging the grayscale information of the detected position on the object 10 under test by the processing device 130 during the detection process. In the process of texture analysis, the processing device 130 sequentially performs differential edge detection, sharpening and binarization on the image.

In detail, the difference between the dirty edge and the surrounding environment may be displayed on the object 10 in a gradient state, and the grayscale difference between the dirty edge and the surrounding environment is not necessarily obvious. Based on this, in the embodiment of the present invention, a Gaussian differential edge detector (Gaussian filter) is used to obtain the graphic edges existing on the image. Since the principle of the Gaussian differential edge detector can be understood by those skilled in the art, it will not be repeated here. The processing device 130 further sharpens the edge of the image to highlight the edge of the image detected by the Gaussian differential edge detector. Finally, the processing device 130 binarizes the image to render the image in black and white. Based on this, the processing device 130 can acquire the texture image including the object to be tested 10 .

Please refer to FIG. 5 and FIG. 7 at the same time. FIG. 7 is a schematic image diagram of a texture analysis program according to an embodiment of the present invention. The left image of FIG. 7 is the grayscale information of the corresponding object to be tested 10 in the original image, and the right image of FIG. 7 is the texture image generated after edge detection, sharpening and binarization. The white area is the part without defects, and the black area represents the texture corresponding to the defect. It is worth mentioning that, in this embodiment, the processing device 130 performs differential edge detection on the overall image, so other textures may exist. Moreover, for easy understanding, the image shown in FIG. 7 only retains a portion of the corresponding object to be tested 10 without other textures. However, in other embodiments of the present invention, the processing device 130 will directly perform texture analysis on the block corresponding to the object to be tested 10 in the image according to the position of the object to be tested 10 obtained in step S510. Not limited to this.

In step S530, the processing device 130 associates the texture image and the image, and obtains a texture pattern corresponding to the position of the object to be tested 10 in the image according to the position of the object to be tested 10 in the image. FIG. 8 is a schematic diagram of a texture image and an image associated with an object to be tested according to an embodiment of the present invention. Referring to FIG. 5 and FIG. 8 at the same time, after correlating the texture image and the image including the object to be tested, the processing device 130 can know the position of the dirt on the object to be tested 10 .

In step S540, the processing device 130 determines whether there is a texture at the detection position according to the texture pattern corresponding to the position of the object to be tested 10 in the image. In the embodiment of the present invention, the processing device 130 determines whether there is a texture at the detection position. However, in other embodiments of the present invention, the processing device 130 determines whether there is a texture within a radius range (eg, within 10 pixel values) centered on the detection position, but the present invention is not limited thereto.

If there is a texture, it means that if the processing device 130 performs luminance detection on the detection position, errors may be caused due to defects. Therefore, in step S550, the processing device 130 moves the detection position where the texture exists from the first position to the second position. For example, in this embodiment, the processing device 130 shifts the detection positions with defects by one unit in the direction of the center of the object to be tested 10 . For example, taking the detection position in the upper left corner of FIG. 8 as an example, if there is a defect, the processing device 130 will shift the detection position to the right and the bottom by one unit, and execute step S540 again until there is no any defect in the detection position. texture. It should be noted that the up, down, left, and right described herein are only directions corresponding to the drawings, and the way the processing device 130 adjusts the detection position will be adjusted according to different embodiments and practical needs. Not limited to this.

However, if there is no texture, in step S560, the processing device 130 acquires the average grayscale information of the corresponding preset radius according to the detection position where the texture does not exist, so as to set the average grayscale information as the corresponding detection position where the texture does not exist. Grayscale information to be measured. Based on this, the processing device 130 can further obtain the corresponding estimated luminance information according to the grayscale information to be measured.

FIG. 9 is a flowchart of a method for detecting uniform luminance according to an embodiment of the present invention. 1 to FIG. 3 and FIG. 9 at the same time, FIG. 9 will be used to describe the details of the luminance curve obtained by the luminance uniformity detection method and the luminance uniformity detection system in an embodiment of the present invention.

In step S910, a sample image including the sample object is captured by the image sensor 110 to obtain a plurality of grayscale information in the sample image including the sample object. Step S910 is the same as step S310 , the difference is that in step S910 image sensing is performed on the sample object, while step S310 is performed on the object to be measured 10 , therefore, details are not repeated here.

In step S920, the processing device 130 acquires real luminance information of multiple detection positions on the corresponding sample object, and acquires multiple sample grayscale information of the detection positions on the corresponding sample object in the sample image according to the sample image and the detection position information . In detail, in order to establish a real situation that conforms to the object to be measured and the image of the object to be measured 10 obtained by the image sensor 110 , in an embodiment of the present invention, the processing device 130 pre-obtains the grayscale and luminance in the real situation. Correspondence. Therefore, the processing device 130 must first obtain the real luminance information of a plurality of detection positions on the sample object. In this embodiment, the sample object will first obtain the real luminance information of each detection position on the object to be tested 10 through an automatic optical inspection machine equipped with luminance measurement equipment, and the real luminance information of each detection position will be obtained in advance is transmitted and built into the storage unit 120 . Based on this, the processing device 130 can access the storage unit 120 or directly use the received real luminance information of each detection position to perform operations.

In addition, the processing device 130 also captures the sample image including the sample object through the image sensor 110 to obtain sample gray-scale information of the detected position on the corresponding sample object in the sample image. The process for the processing device 130 to obtain the sample gray-scale information of the detection position on the corresponding sample object is the same as the aforementioned steps S310 and S320, the difference is that in the steps S310 and S320, the gray-scale information to be detected of the corresponding detection position is obtained for the object to be tested 10, Step S920 is to obtain sample grayscale information of the corresponding detection position for the sample object. Based on this, details are not described here. Moreover, in this embodiment, the detection position of the sample object is consistent with the detection position of the object to be tested 10 .

It is worth mentioning that, in order to respond to various situations that may occur in the object to be tested 10, in the embodiment of the present invention, the real luminance information of the sample object under various luminance conditions and the corresponding sample grayscales are collected. information. For example, in this embodiment, the DUT 10 will output a current corresponding to 64 grayscales (that is, from the brightness of 0 to 255, every four brightness is regarded as a grayscale and a corresponding current is generated), so as to Obtain the real luminance information corresponding to 64 grayscales through an automatic optical inspection machine. At the same time, through the cooperation of the image sensor 110 and the processing device 130 , the sample grayscale information of the corresponding sample object in the corresponding different real luminance information in each detection position is obtained.

In step S930, the processing device 130 executes a curve fitting program to determine the luminance prediction curve of the sample grayscale information and the real luminance information of each detection position on the corresponding sample object, and store the luminance corresponding to each detection position. The degree prediction curve is stored in the storage unit 120 . Specifically, in the curve fitting program, the processing device 130 uses the data input curve mathematical formula of the sample grayscale information and the corresponding real brightness information to find the brightness prediction corresponding to the sample grayscale information and the real brightness information. Evaluate curve math. In this way, the relationship between the sample grayscale information and the real luminance information can be found under a variety of different luminance prediction curves. The mathematical formula of the curve used in the embodiment of the present invention may be, for example, a polynomial function (Polynomial), an interpolation function (Spline), an exponential function (Exponential), etc., but is not limited thereto. The polynomial function is, for example, a linear function (Linear), a quadratic function (Quadratic), a cubic function (Cubic), a quartic function (Quintic), a multiple function (nth order), and the like. The interpolation function is, for example, Linear interpolation function (Linear), Hermite interpolation function (Hermite), Catmull-rom interpolation function (Catmull-rom), Cubic interpolation function (Cubic), Akima interpolation function, Monotone interpolation function (Monotone), etc. . The exponential function is, for example, a symmetric S-function (Symmetrical sigmoidal), an asymmetrical S-function (Asymmetrical sigmoidal), a michaelis menten, a basic exponential function (Exponential basic), an exponential half-life function (Exponential half-life), Exponential proportional rate function (Exponential proportional rate), power function (Power curve), normal distribution function (Gaussianbell curve) and so on. Not only that, in this embodiment, the processing device 130 can also use a deep learning method, such as, but not limited to, a neural network-like network for learning, thereby estimating the corresponding relationship between the real luminance information and the sample grayscale information. Luminance prediction curve. In this embodiment, the processing device 130 uses all the above-mentioned curve functions and deep learning methods to obtain the mathematical formula of the relationship curve matching the sample gray-scale information and the real luminance information of each detection position, that is, the processing device 130 29 kinds of luminance prediction curves are obtained, however, the present invention is not limited to the aforementioned mathematical functions. Any combination of the above curve functions, or other mathematical expressions that are not described in detail here, can be used as the correlation between the estimated real luminance information and the sample grayscale information in the present invention without departing from the present invention. mathematical function.

The processing device 130 acquires a plurality of estimated luminance curves at each detection position on the sample object. That is to say, in this embodiment, each detection position has 29 kinds of luminance prediction curves. The processing device 130 further stores the luminance prediction curve in the storage unit 120 .

In step S940, the verification image including the verification object is captured by the processing device 130 to obtain a plurality of grayscale information in the verification image including the verification object. Step S940 is the same as steps S310 and S910, the difference is that in steps S310 and S910, image sensing is performed on the object to be tested 10 and the sample object, respectively. Here, image sensing is performed on the verification object, which will not be repeated here. detail. It should be noted that, in the embodiment of the present invention, the object to be tested, the sample object and the verification object all belong to the same type of panel, backlight module or other types of objects to be tested.

In step S950, the processing device 130 acquires the real luminance information of the detection position on the corresponding verification object, and the processing device 130 acquires a plurality of verification grayscale information of the verification object for a plurality of detection positions according to the verification image, so that each detection The plurality of luminance prediction curves of the positions and the plurality of verification grayscale information determine the plurality of verification estimated luminance information of the plurality of detection positions on the corresponding verification object. Step S950 is the same as step S920, the difference is that step S920 obtains the real luminance information and sample grayscale information at each detection position for the sample object, and here is for the verification object to obtain the real luminance at each detection position. information and verification grayscale information, and details are not repeated here. In this embodiment, the detection position of the verification object is consistent with the detection position of the object to be tested 10 and the detection position of the sample object.

In step S960, the processing device 130 determines the brightness of each estimated luminance curve corresponding to each detection position on the verification object according to the estimated luminance information and the actual luminance information of the plurality of detection positions on the verification object, respectively. For the error value, in each detection position of the verification object, one of the corresponding plurality of luminance prediction curves with the smallest error value is set as the luminance curve corresponding to each detection position of the object to be tested.

In an embodiment of the present invention, the process of acquiring the error value by the processing device 130 and the definition of the error value can be expressed as the following equation:

Among them, N is the estimated luminance curve, e is the error value, and L ^Est is the estimated luminance information at the detection position obtained by inputting the estimated luminance curve of the verification object from the gray level of the detection position to the corresponding detection position , L ^Gnd is the real luminance information obtained by verifying that the object is at the detection position in the automatic detection machine at the gray level.

After obtaining the error value of each corresponding luminance prediction curve for the detection position, the processing device 130 will determine that the luminance prediction curve with the smallest error value is the luminance prediction curve closest to the detection position, based on this, The processing device 130 uses the estimated luminance curve with the smallest error value as the luminance curve at the detected position. It is worth mentioning that since in different detection positions, the luminance prediction curves obtained for the same mathematical function are not necessarily the same, and the corresponding error values are not necessarily the same. Therefore, the luminance prediction curves corresponding to the minimum error values at different detection positions are also different. Therefore, the processing unit 130 will store the corresponding luminance curve for each different detection position, and use the corresponding luminance curve for different detection positions to evaluate the estimated luminance during subsequent luminance measurement. information.

Please refer to FIG. 10 . FIG. 10 illustrates a detailed flowchart of a method for detecting uniform luminance according to an embodiment of the present invention. In the following, with reference to FIG. 10 , it will be described that the processing device 130 obtains the real luminance information of multiple detection positions on the corresponding sample object, and obtains the sample grayscale information of the detection positions on the corresponding sample object according to the sample image and the detection position information, and the processing device 130 obtains the sample grayscale information of the detection positions on the corresponding sample object, The device 130 acquires the real luminance information of the detection position on the corresponding verification object, and the processing device 130 acquires the verification grayscale information of the detection position on the verification object according to the image including the verification object, so as to determine the corresponding verification object according to the luminance prediction curve. Details of multiple estimated luminance information for the detection location.

In step S1010, the processing device 130 executes the object positioning program to obtain the positions of the sample object and the verification object in the sample image and the verification image respectively, and respectively obtains the detection position on the sample object and the detection position on the verification object according to the detection position information The position in the sample image and the verification image. The processing device 130 further performs binarization processing on the sample image and the verification image respectively to remove the parts that do not belong to the sample object and the verification object, and performs edge detection on the parts of the corresponding sample object and the verification object respectively, so as to obtain the sample object and the verification object. Multiple vertex positions of the object's edge.

In step S1020, the processing device 130 executes a texture analysis program on the sample image and the verification image, so as to obtain texture images including the sample object and the verification object, respectively. The processing device 130 also executes the differential edge detection procedure on the sample image and the verification image respectively, so as to obtain the graphic edges existing on the sample image and the verification image, respectively. And the sharpened graphics edges are binarized to obtain texture images.

In step S1030, the processing device 130 associates the sample image with the texture image including the sample object, and the verification image and the texture image including the sample object, and obtains them respectively according to the position of the sample object in the sample image and the position of the verification object in the verification image In the sample image, the corresponding sample object and the corresponding verification object are verifying the texture pattern of the image.

In step S1040, the processing device 130 determines whether there is texture at the detection position of the sample object and the detection position of the verification object according to the texture patterns of the corresponding sample object in the sample image and the position of the verification object in the verification image, respectively.

In step S1050, if there is a texture at one of the detection positions of the sample object or at one of the detection positions of the verification object, the processing device 130 moves the detection position of the texture in the sample object or the verification object from the first position to the second position Second position. In one embodiment, when a texture exists in one of the detection positions of the sample object, one of the detection positions of the sample object in which the texture exists is moved from the first position to the second position. At the same time, a detection position of the detection position where the texture exists in the corresponding sample object among the plurality of detection positions of the verification object is also moved from the first position to the second position.

In step S1060, if there is no texture at one of the detection positions of the sample object or one of the detection positions of the verification object, the processing device 130 further determines that the texture does not exist in one of the detection positions of the sample object and in one of the detection positions of the verification object. Obtaining average grayscale information corresponding to a predetermined radius at a detection position where no texture exists, so as to set the average grayscale information as the sample grayscale information of the detection positions where the texture does not exist in one of the corresponding sample objects or Verification grayscale information of the detection position where the texture does not exist in one of the verification objects. In one embodiment, when there is no texture in one of the detection positions of the sample object, and a detection position of the detection position where the texture does not exist in the corresponding sample object among the plurality of detection positions of the verification object has a texture, only the verification object is One of the detection positions in which the texture exists in the first position is moved from the first position to the second position.

Since the details of steps S1010 to S1060 are the same as those of steps S510 to S560, the difference is that steps S510 to S560 process the object to be tested 10 to obtain grayscale information to be measured at multiple detection positions on the object to be tested 10 , and in steps S1010 to S1060, the sample object and the verification object are processed to obtain sample grayscale information and verification grayscale information of multiple detection positions on the sample object and the verification object. Therefore, the details of steps S1010 to S1060 are not repeated here.

To sum up, the luminance uniformity detection system and the luminance uniformity detection method of the present invention can obtain the grayscale information to be measured at multiple detection positions of the corresponding test object according to the image obtained by the image sensor, and then obtain the corresponding grayscale information. Estimated luminance information. Since it is not necessary to measure the luminance at each detection position one by one, the rapid luminance uniform detection method of the luminance uniform detection system can effectively save the time required for detection. In addition, since the brightness uniformity detection system and the brightness uniformity detection method can automatically locate the object, it is not necessary to locate each detection position separately, and there is no need for the tester to assist in confirming the automatic optical inspection machine and the object to be tested. The relationship between the detection positions of the panels, thus reducing the manpower required for detection. Not only that, because the luminance uniformity detection system and luminance uniformity detection method of the present invention adopts a variety of different estimated luminance curves, and finds luminance curves closer to different detection positions through the auxiliary verification of the verification object, the basic to provide more accurate luminance estimation results.

Although the present invention has been disclosed as above with examples, it is not intended to limit the present invention. Any person skilled in the art can make some changes and modifications without departing from the spirit and scope of the present invention, so the protection of the present invention The scope shall be as defined by the claims.

Claims (30)

1. A system for detecting brightness uniformity comprises an image sensor, a storage unit and a processing device,

the image sensor captures an image including the object to be detected so as to acquire a plurality of gray scale information in the image;

the storage unit stores a plurality of luminance curves corresponding to the detection position information;

the processing device is connected to the image sensor and the storage unit, acquires a plurality of pieces of gray scale information corresponding to a plurality of detection positions on the object to be detected from the plurality of pieces of gray scale information of the image according to the image and the detection position information, respectively acquires a plurality of pieces of estimated luminance information at the plurality of detection positions according to the plurality of luminance curves and the plurality of pieces of gray scale information to be detected, and judges whether the luminance of the object to be detected is uniform according to the difference of the plurality of pieces of estimated luminance information among the plurality of detection positions of the object to be detected, wherein the image sensor further captures a sample image including a sample object to acquire the plurality of pieces of gray scale information in the sample image including the sample object,

the processing device further obtains a plurality of real luminance information corresponding to a plurality of detection positions on the sample object, and the processing device obtains a plurality of sample gray scale information corresponding to the plurality of detection positions on the sample object in the sample image according to the sample image and the plurality of detection positions,

the processing device further executes a curve fitting procedure to determine a plurality of luminance estimation curves corresponding to the plurality of sample gray scale information and the plurality of real luminance information at each of the plurality of detection positions on the sample object, and stores the plurality of luminance estimation curves in the storage unit,

the image sensor further captures a verification image including a verification object to obtain the plurality of gray scale information in the verification image including the verification object,

the processing device further obtains the real luminance information corresponding to the detection positions on the verification object in the verification image, and the processing device obtains verification gray-scale information corresponding to the detection positions on the verification object in the verification image according to the verification image to determine verification estimated luminance information corresponding to the detection positions on the verification object according to the luminance estimation curves and the verification gray-scale information,

the processing device further determines an error value of each of the plurality of luminance estimation curves corresponding to each of the plurality of detection positions on the verification object according to the plurality of verification estimated luminance information and the plurality of real luminance information corresponding to the plurality of detection positions on the verification object, respectively, so as to set one of the plurality of luminance estimation curves having the smallest error value in each of the plurality of detection positions of the verification object as each of the plurality of luminance curves corresponding to each of the plurality of detection positions of the object to be tested.

2. The system according to claim 1, wherein the testing position information comprises the testing positions, the storage unit stores each of the plurality of luminance curves corresponding to each of the testing positions, and the processing device obtains each of the estimated luminance information corresponding to each of the testing positions according to each of the plurality of luminance curves corresponding to each of the testing positions and each of the gray scale information to be tested.

3. The luminance uniformity detection system according to claim 1,

the processing device further executes an object positioning program on the image to acquire the position of the object to be detected in the image, and judges the positions of the detection positions in the image according to the position of the object to be detected in the image and the detection position information.

4. The luminance uniformity detection system according to claim 3, wherein, in the object localization program,

the processing device also carries out binarization processing on the image so as to remove parts which do not belong to the object to be detected, and carries out edge detection on the parts corresponding to the object to be detected so as to obtain a plurality of vertex positions of the edge of the object to be detected.

5. The luminance uniformity detection system according to claim 3,

the processing device further executes a texture analysis program on the image to acquire a texture image including the object to be measured,

the processing device is also associated with the texture image and the image, and acquires a texture pattern corresponding to the position of the object to be detected in the image according to the position of the object to be detected in the image.

6. The luminance uniformity detection system according to claim 5, wherein, in the texture analysis program,

the processing device further performs a differential edge detection procedure on the image to obtain a graphic edge existing on the image,

and the processing device also sharpens the image edge and performs binarization processing on the image and the sharpened image edge to obtain the texture image.

7. The system according to claim 5, wherein the processing device further determines whether a texture exists at the plurality of detection positions according to the texture pattern corresponding to the position of the object in the image, and moves one of the plurality of detection positions where the texture exists from a first position to a second position when the texture exists at the one of the plurality of detection positions.

8. The system according to claim 7, wherein when the texture does not exist at one of the plurality of detection positions, average gray scale information with a corresponding predetermined radius is obtained according to each of the plurality of detection positions where the texture does not exist, so as to set the average gray scale information as each of the plurality of to-be-measured gray scale information corresponding to each of the plurality of detection positions where the texture does not exist.

9. The luminance uniformity detection system according to claim 1,

the processing device further executes an object positioning program on the sample image and the verification image to respectively acquire positions of the sample object and the verification object in the sample image and the verification image, and respectively acquires positions of the plurality of detection positions on the sample object and the plurality of detection positions on the verification object in the sample image and the verification image according to the detection position information.

10. The luminance uniformity detection system according to claim 9, wherein, in the object localization program,

the processing device further performs binarization processing on the sample image and the verification image respectively to remove parts which do not belong to the sample object and the verification object, and performs edge detection on the parts corresponding to the sample object and the verification object respectively to obtain a plurality of vertex positions of the edges of the sample object and the verification object.

11. The luminance uniformity detection system according to claim 9,

the processing device further executes a texture analysis program on the sample image and the verification image to respectively acquire texture images including the sample object and the verification object,

the processing device is further used for associating the sample image with the texture image comprising the sample object, the verification image with the texture image comprising the verification object, and respectively obtaining the corresponding sample object and the texture pattern of the corresponding verification object in the sample image and the verification image according to the position of the sample object in the sample image and the position of the verification object in the verification image.

12. The luminance uniformity detection system according to claim 11, wherein, in the texture analysis program,

the processing device further performs a differential edge detection procedure on the sample image and the verification image respectively to obtain the graphic edges existing on the sample image and the verification image respectively,

the processing device also sharpens the image edges of the sample image and the verification image, and performs binarization processing on the sample image, the verification image and the sharpened image edges to respectively obtain the texture images corresponding to the sample image and the verification image.

13. The system according to claim 11, wherein the processing device further determines whether there is a texture at the plurality of detection positions of the sample object and at the plurality of detection positions of the verification object according to the texture patterns corresponding to the positions of the sample object and the verification object in the sample image, respectively, and moves one of the plurality of detection positions where the texture exists in the sample object or the verification object from a first position to a second position when the texture exists at one of the plurality of detection positions of the sample object or at one of the plurality of detection positions of the verification object.

14. The system according to claim 13, wherein the processing device further obtains average gray-scale information with a predetermined radius from the plurality of detection positions of the sample object where the texture is not present or from the plurality of detection positions of the verification object where the texture is not present, when the texture is not present at one of the plurality of detection positions of the sample object or at one of the plurality of detection positions of the verification object where the texture is not present, to set the average gray-scale information as the sample gray-scale information corresponding to the plurality of detection positions of the sample object where the texture is not present or the verification gray-scale information corresponding to the plurality of detection positions of the verification object where the texture is not present.

15. The system according to claim 1, wherein the plurality of luminance estimation curves corresponding to each of the plurality of detection positions on the sample object are plural.

16. A method for detecting uniformity of luminance is used for detecting an object to be detected, and comprises the following steps:

capturing an image comprising the object to be detected to acquire a plurality of gray scale information in the image;

acquiring a plurality of pieces of to-be-detected gray scale information corresponding to a plurality of detection positions on the to-be-detected object from the plurality of pieces of gray scale information of the image according to the image and the detection position information;

respectively acquiring a plurality of estimated luminance information at a plurality of detection positions according to a plurality of luminance curves corresponding to the detection position information and the plurality of to-be-detected gray scale information;

judging whether the brightness of the object to be detected is uniform or not according to the difference of the estimated brightness information among the detection positions of the object to be detected; and

capturing a sample image including a sample object to obtain the plurality of gray scale information in the sample image including the sample object;

acquiring a plurality of real luminance information corresponding to a plurality of detection positions on the sample object, and acquiring a plurality of sample gray scale information corresponding to the plurality of detection positions on the sample object in the sample image according to the sample image and the detection positions;

executing a curve fitting program to determine a plurality of luminance estimation curves corresponding to the plurality of sample gray scale information and the plurality of real luminance information at each of the plurality of detection positions on the sample object, and storing the plurality of luminance estimation curves;

capturing a verification image comprising a verification object to acquire the plurality of gray scale information in the verification image comprising the verification object;

obtaining the real luminance information of the detection positions on the verification object in the verification image, and obtaining verification gray scale information of the detection positions on the verification object in the verification image according to the verification image, so as to determine verification estimated luminance information of the detection positions on the verification object according to the luminance estimation curves and the verification gray scale information; and

determining an error value of each of the plurality of luminance estimation curves corresponding to each of the plurality of detection positions on the verification object according to the plurality of verification estimated luminance information and the plurality of real luminance information corresponding to the plurality of detection positions on the verification object, respectively, so as to set one of the plurality of luminance estimation curves having the smallest error value in each of the plurality of detection positions of the verification object as each of the plurality of luminance curves corresponding to each of the plurality of detection positions of the object to be tested.

17. The method as claimed in claim 16, wherein the detection position information comprises the plurality of detection positions, and at least one of the plurality of luminance curves is associated with each of the plurality of detection positions, and the step of obtaining the estimated luminance information at the detection positions according to the luminance curves and the gray scale information comprises:

and obtaining each estimated brightness information of each detection position according to each brightness curve corresponding to each detection position and each gray scale information to be detected.

18. The luminance uniformity detection method according to claim 16, further comprising:

executing an object positioning program on the image to acquire the position of the object to be detected in the image; and

and judging the positions of the detection positions in the image according to the position of the object to be detected in the image and the detection position information.

19. The method according to claim 18, wherein the step of performing the object-locating procedure on the image comprises:

carrying out binarization processing on the image to remove parts which do not belong to the object to be detected; and

and carrying out edge detection on the part corresponding to the object to be detected so as to obtain a plurality of vertex positions of the edge of the object to be detected.

20. The luminance uniformity detection method according to claim 18, further comprising:

executing a texture analysis program on the image to obtain a texture image comprising the object to be detected; and

and associating the texture image with the image, and acquiring a texture pattern corresponding to the position of the object to be detected in the image according to the position of the object to be detected in the image.

21. The luminance uniformity detection method according to claim 20, wherein the step of performing the texture analysis procedure on the image further comprises:

executing a differential edge measurement program on the image to obtain a graph edge existing on the image; and

and sharpening the image edge, and performing binarization processing on the image and the sharpened image edge to obtain the texture image.

22. The method as claimed in claim 20, wherein the step of associating the texture image with the image and obtaining the texture pattern corresponding to the position of the object in the image according to the position of the object in the image further comprises:

judging whether textures exist at the detection positions or not according to the texture patterns corresponding to the positions of the object to be detected in the images; and

when the texture exists in one of the plurality of detection positions, moving the one of the plurality of detection positions where the texture exists from a first position to a second position.

23. The luminance uniformity detection method according to claim 22, further comprising:

when the texture does not exist in one of the detection positions, respectively acquiring average gray scale information of a corresponding preset radius according to each detection position without the texture; and

and respectively setting the average gray scale information as each piece of the plurality of pieces of to-be-detected gray scale information corresponding to each detection position without the texture.

24. The luminance uniformity detection method according to claim 16, further comprising:

executing an object positioning program on the sample image and the verification image to respectively obtain the positions of the sample object and the verification object in the sample image and the verification image, and respectively obtaining the positions of the plurality of detection positions on the sample object and the plurality of detection positions on the verification object in the sample image and the verification image according to the detection position information.

25. The method according to claim 24, wherein the step of performing the object-locating procedure on the sample image and the verification image further comprises:

respectively carrying out binarization processing on the sample image and the verification image so as to remove parts which do not belong to the sample object and the verification object; and

and respectively carrying out edge detection on parts corresponding to the sample object and the verification object so as to obtain a plurality of vertex positions of the edges of the sample object and the verification object.

26. The luminance uniformity detection method according to claim 24, further comprising:

executing a texture analysis program on the sample image and the verification image to respectively obtain texture images comprising the sample object and the verification object; and

and associating the sample image with the texture image comprising the sample object and the verification image with the texture image comprising the verification object, and respectively acquiring the corresponding sample object and the texture pattern of the verification object in the sample image and the texture pattern of the verification object in the verification image according to the position of the sample object in the sample image and the position of the verification object in the verification image.

27. The luminance uniformity detection method according to claim 26, wherein in the step of performing the texture analysis procedure on the sample image and the verification image, further comprising:

respectively executing a differential edge measurement program on the sample image and the verification image so as to respectively obtain the graph edges on the sample image and the verification image; and

sharpening the image edges of the sample image and the verification image, and performing binarization processing on the sample image, the verification image and the sharpened image edges to respectively obtain the texture images corresponding to the sample image and the verification image.

28. The luminance uniformity detection method according to claim 26, further comprising:

judging whether textures exist at the plurality of detection positions of the sample object and at the plurality of detection positions of the verification object according to the texture patterns corresponding to the positions of the sample object in the sample image and the positions of the verification object in the verification image respectively; and

moving the one of the plurality of detection locations where the texture is present in the sample object or the validation object from a first location to a second location when the texture is present in the one of the plurality of detection locations in the sample object or the validation object.

29. The luminance uniformity detection method according to claim 28, further comprising:

when the texture does not exist at one of the plurality of detection positions of the sample object or one of the plurality of detection positions of the verification object, respectively obtaining average gray scale information of corresponding preset radii according to one of the plurality of detection positions of the sample object where the texture does not exist or the plurality of detection positions of the verification object where the texture does not exist; and

setting the average grayscale information to correspond to the sample grayscale information for the plurality of detection locations of the sample object where the texture is not present or the verification grayscale information for the plurality of detection locations of the verification object where the texture is not present.

30. The method as claimed in claim 29, wherein the plurality of luminance estimation curves corresponding to each of the plurality of detection positions on the sample object are plural.

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