Projector picture consistency defect detection method and system

Abstract

The invention provides a picture consistency defect detection method and system for a projector, and relates to the technical field of picture detection, and the method comprises the steps: obtaining a detection image shot by the projector, then carrying out the image processing of the detection image, obtaining a plurality of decomposed images, and segmenting each decomposed image into a plurality of image blocks with the same size; sequentially performing peripheral brightness difference calculation on each image block to obtain a first judgment parameter, performing spot detection to obtain a second judgment parameter, and performing dead pixel detection to obtain a third judgment parameter; and judging whether the first judgment parameter, the second judgment parameter and the third judgment parameter are in the corresponding standard ranges or not, and indicating that the detection is passed. The method has the beneficial effects that through an automatic image processing technology, key parameters such as peripheral brightness difference, spots and dead pixels of a plurality of image blocks of each decomposed image can be accurately calculated, subjective deviation of manual observation is avoided, and accuracy and consistency of detection results are ensured.

Description

Technical Field

[0001] The present invention relates to the field of picture detection technology, and in particular to a method and system for detecting picture consistency defects of a projector. Background Art

[0002] In the modern production and manufacturing process, projectors, as a key display and testing equipment, are widely used in the quality inspection and evaluation of various products. Especially in the fields of semiconductors, electronic components, precision mechanical parts, etc., the quality of projector images directly determines the accuracy and reliability of product appearance defect detection. Traditional projector image UF (Uniformity & Flaw) inspection mainly relies on manual naked eye observation, which may be able to meet basic quality control needs in the short term. Observers need to visually judge the color and brightness consistency of the projected image under different grayscale conditions, and whether there are defects such as scratches, stains, and color differences according to preset standards, so as to determine whether the product is qualified or not.

[0003] However, with the expansion of production scale and the improvement of product precision, the limitations of manual visual observation are becoming increasingly prominent:

[0004] Observers are very likely to suffer from visual fatigue when conducting high-precision and high-intensity visual inspections for a long time. This not only reduces work efficiency, but also increases the possibility of misjudgment and missed inspections, resulting in the outflow of unqualified products, affecting product quality and customer satisfaction.

[0005] There is inevitable subjectivity in manual observation, and different observers may have different judgment criteria for the same defect. This subjective deviation affects the uniformity and accuracy of the test results and is not conducive to the standardized management of product quality.

[0006] Inspection methods that rely on manual labor are difficult to adapt to the rapidly changing production rhythm, especially in mass production environments. Manual inspection is not only inefficient, but also requires a lot of human resource investment, increasing the company's operating costs.

[0007] In view of the many defects in the above-mentioned background technology, it is particularly important to develop a new method that can automatically, accurately and efficiently complete the UF inspection of projector images. Summary of the invention

[0008] In view of the problems existing in the prior art, the present invention provides a method for detecting image consistency defects of a projector, comprising:

[0009] Step S1, obtaining a detection image captured by a projector, then performing image processing on the detection image to obtain a plurality of decomposed images, and dividing each of the decomposed images into a plurality of image blocks of equal size;

[0010] Step S2, performing peripheral brightness difference calculation on each image block in turn to obtain a first judgment parameter, performing spot detection to obtain a second judgment parameter, and performing bad pixel detection to obtain a third judgment parameter;

[0011] Step S3, judging whether the first judgment parameter, the second judgment parameter and the third judgment parameter are within corresponding standard ranges:

[0012] If yes, the test passed;

[0013] If not, the test fails.

[0014] Preferably, the peripheral brightness difference calculation process in step S2 includes:

[0015] The first judgment parameter is obtained by calculating the average brightness value of each pixel in each image block, and calculating the difference between the average brightness value of each image block and each of its adjacent image blocks.

[0016] Preferably, the spot detection process in step S2 includes:

[0017] For each of the image blocks, a spot area is identified therein, and the brightness difference between the spot area and surrounding pixels is calculated to obtain the second judgment parameter.

[0018] Preferably, the bad pixel detection process in step S2 includes:

[0019] For each of the image blocks, the area of ​​the bad pixels therein is calculated to obtain the third judgment parameter; the bad pixels are pure white and / or pure black pixels.

[0020] Preferably, the method further includes step S4, wherein the brightness of the projector is gradually reduced from the highest brightness, and each time the brightness of the projector is adjusted, the detection method in steps S1 to S3 is executed once to obtain a corresponding detection result.

[0021] The present invention also provides a system for detecting image consistency defects of a projector, which uses the above-mentioned image consistency defect detection method and comprises:

[0022] An image acquisition module is used to acquire a detection image captured by a projector, and then perform image processing on the detection image to obtain a plurality of decomposed images, and divide each of the decomposed images into a plurality of image blocks of equal size;

[0023] An image detection module, connected to the image acquisition module, configured to sequentially perform peripheral brightness difference calculation on each image block to obtain a first judgment parameter, perform spot detection to obtain a second judgment parameter, and perform bad pixel detection to obtain a third judgment parameter;

[0024] A defect judgment module is connected to the image detection module, and is used to output a detection result indicating that the detection is passed when it is judged that the first judgment parameter, the second judgment parameter and the third judgment parameter are within the corresponding standard range, and to output a detection result indicating that the detection is failed when it is judged that the first judgment parameter, the second judgment parameter, or the third judgment parameter is not within the corresponding standard range.

[0025] Preferably, the image detection module includes:

[0026] The peripheral brightness difference calculation unit is used to calculate the average brightness value of each pixel in each image block, and calculate the difference between the average brightness value of each image block and its adjacent image blocks to obtain the first judgment parameter.

[0027] Preferably, the image detection module includes:

[0028] The spot detection unit identifies a spot area in each image block, and calculates a brightness difference between the spot area and surrounding pixels to obtain the second judgment parameter.

[0029] Preferably, the image detection module includes:

[0030] The bad pixel detection unit is used to calculate the area of ​​the bad pixels in each image block to obtain the third judgment parameter; the bad pixels are pure white and / or pure black pixels.

[0031] Preferably, it also includes a brightness adjustment module, which is used to gradually reduce the brightness of the projector from the highest brightness. Each time the brightness of the projector is adjusted, the detection process in the image acquisition module, the image detection module and the defect judgment module is executed once to obtain the corresponding detection result.

[0032] The above technical solution has the following advantages or beneficial effects: Compared with the projector image UF inspection method that relies on manual naked eye observation in the background technology, the projector screen consistency defect detection method proposed in the present invention can accurately calculate the key parameters such as peripheral luminance difference, spots and bad pixels of multiple image blocks of each decomposed image through automated image processing technology, thereby avoiding the subjective bias of manual observation and ensuring the accuracy and consistency of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The figure is a flow chart of a method for detecting image consistency defects of a projector in a preferred embodiment of the present invention;

[0034] Figure 2The structure diagram of a system for detecting image consistency defects of a projector in a preferred embodiment of the present invention is shown. DETAILED DESCRIPTION

[0035] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The present invention is not limited to this embodiment, and other embodiments may also fall within the scope of the present invention as long as they conform to the gist of the present invention.

[0036] In a preferred embodiment of the present invention, based on the above problems existing in the prior art, a method for detecting image consistency defects of a projector is provided. Figure 1 As shown, including:

[0037] Step S1, obtaining a detection image captured by a projector, then performing image processing on the detection image to obtain a plurality of decomposed images, and dividing each decomposed image into a plurality of image blocks of equal size;

[0038] Step S2, calculating the peripheral brightness difference of each image block in turn to obtain a first judgment parameter, performing spot detection to obtain a second judgment parameter, and performing bad pixel detection to obtain a third judgment parameter;

[0039] Step S3, judging whether the first judgment parameter, the second judgment parameter and the third judgment parameter are within the corresponding standard range:

[0040] If yes, the test is passed;

[0041] If not, the test fails.

[0042] Specifically, compared with the projector image UF inspection method in the background technology that relies on manual naked eye observation, the projector screen consistency defect detection method proposed in this embodiment can accurately calculate key parameters such as peripheral brightness difference, spots and bad pixels of multiple image blocks of each decomposed image through automated image processing technology, thereby avoiding the subjective bias of manual observation and ensuring the accuracy and consistency of the detection results.

[0043] Standardized and quantified judgment parameters make the test results between different batches and different testers comparable, improving the stability and reliability of product quality.

[0044] The automated detection method greatly shortens the detection time, reduces the manpower required for manual observation, and improves production efficiency. At the same time, since the detection process can be programmed, it can easily adapt to large-scale, high-efficiency production environments and meet the needs of modern manufacturing for rapid response and high-quality output.

[0045] The automated detection method completely replaces manual visual observation, fundamentally eliminating the visual fatigue and misjudgment risks caused by long-term work. This not only improves the accuracy of the test results, but also ensures the health and job satisfaction of the testers.

[0046] It can accurately calculate the various judgment parameters of each image block, and further, immediately mark the defect location when the parameters exceed the standard range. This precise defect location capability provides strong data support for subsequent quality analysis and improvement measures, helping enterprises to continuously improve product quality.

[0047] The automated inspection system can record the results and defect locations of each inspection to form a complete inspection record. These records not only help companies trace the root causes of product quality problems, but also provide valuable data resources for quality management and continuous improvement.

[0048] In summary, the projector screen consistency defect detection method of the present invention has shown significant beneficial effects in improving detection accuracy and efficiency, reducing visual fatigue and misjudgment, accurately locating defects, and enhancing traceability and manageability. These advantages make the method have broad application prospects and important practical value in modern production and manufacturing processes.

[0049] In a preferred embodiment of the present invention, the peripheral brightness difference calculation process in step S2 includes:

[0050] The average brightness value of each pixel in each image block is calculated, and the difference between the average brightness value of each image block and each adjacent image block is calculated to obtain the first judgment parameter.

[0051] Specifically, in this embodiment, it is assumed that there is a decomposed image obtained by processing in step S1. The processing process includes performing HSV conversion and grayscale conversion on the color image obtained by directly photographing to obtain H, S, V and grayscale decomposed images. In this embodiment, each decomposed image is divided into 8x8 image blocks of equal size (a total of 64 image blocks). In other embodiments, it can be divided into other numbers of image blocks of equal size as required, and the size of each image block is MxN pixels (M and N are the width and height of the image block).

[0052] Calculate the average brightness value within the image block:

[0053] For each image block (denoted as Block[i][j], where i and j are the row and column indices of the image block, ranging from 0 to 7), we calculate the average luminance value (grayscale value) of all pixels inside it, denoted as AvgLuminance[i][j].

[0054] The calculation of the brightness value is usually to convert the RGB color space into a grayscale value. The formula in this embodiment can be: Gray = 0.299*R + 0.587*G + 0.114*B, but in other embodiments, the specific formula may adjust the coefficients according to the image format and processing requirements.

[0055] The calculation formula for the average luminance value is: AvgLuminance[i][j] = Σ(Gray values ​​in Block[i][j]) / (M*N).

[0056] For each image block Block[i][j], we calculate the difference between its average brightness value and the four adjacent image blocks above, below, left and right (if any).

[0057] The differences in average luminance values ​​of upper and lower adjacent image blocks are: DiffVerticalUp = |AvgLuminance[i][j] - AvgLuminance[i-1][j]| (i>1) and DiffVerticalDown = |AvgLuminance[i][j] - AvgLuminance[i+1][j]| (i<7).

[0058] The differences in average luminance values ​​of left and right adjacent image blocks are: DiffHorizontalLeft = |AvgLuminance[i][j] - AvgLuminance[i][j-1]| (j>1) and DiffHorizontalRight = |AvgLuminance[i][j] - AvgLuminance[i][j+1]| (j<7).

[0059] The maximum value of the four differences or the combination of the four differences according to a specific rule (such as summing, averaging, etc.) into a comprehensive peripheral brightness difference parameter is used as the first judgment parameter. For example, the maximum value of the four differences can be taken: FirstJudgmentParameter = max(DiffVerticalUp, DiffVerticalDown, DiffHorizontalLeft, DiffHorizontalRight).

[0060] The calculated first judgment parameter is compared with a preset brightness difference threshold.

[0061] If the first judgment parameter is less than or equal to the threshold, the image block is considered to be consistent in peripheral brightness and is recorded as passed; otherwise, it is recorded as failed and may require further processing or marking.

[0062] Through the above embodiment, the peripheral brightness difference calculation process in step S2 can be implemented, providing an important judgment basis for subsequent defect detection. This process not only considers the brightness characteristics within the image block, but also considers the brightness continuity between the image block and its adjacent blocks, which helps to accurately identify defects in picture consistency.

[0063] In a preferred embodiment of the present invention, the spot detection process in step S2 includes:

[0064] For each image block, the spot area is identified, and the brightness difference between the spot area and surrounding pixels is calculated to obtain the second judgment parameter.

[0065] Specifically, in this embodiment, there is a decomposed image obtained by processing in step S1 (the processing process is the same as the above embodiment), and each decomposed image is divided into a plurality of image blocks of equal size. Spot detection is performed on each image block.

[0066] For each image block (denoted as Block[i][j]), we apply a blob detection algorithm to identify the blob regions in it.

[0067] Blob detection algorithms can be based on methods such as image gradient, Laplacian operator, Difference of Gaussians (DoG), etc. These methods identify blobs by calculating the gradient change or brightness difference of pixels in the image.

[0068] In this embodiment, a simple threshold method combined with neighborhood comparison can be used to identify spots. Specifically, for each pixel in the image block, we calculate the brightness difference between it and the surrounding pixels (e.g., 3x3 or 5x5 neighborhood). If the brightness difference of a pixel exceeds a preset threshold and it is significantly different from the brightness of the surrounding pixels (e.g., a local maximum or minimum), it is considered to be part of the spot area.

[0069] Once the spot area is identified, the brightness difference between each pixel in the spot area and the surrounding non-spot area is calculated.

[0070] This can be achieved by traversing each pixel in the spot area and calculating the brightness difference between it and the surrounding pixels in the non-spot area within a certain range (for example, a circle of pixels outside the spot boundary).

[0071] In order to obtain a comprehensive second judgment parameter, the average value or maximum value of the brightness differences between all spot pixels and surrounding pixels may be calculated or they may be combined according to a specific rule (such as weighted summation).

[0072] Assuming that the average value of the luminance difference is selected as the second judgment parameter, the following formula can be used: SecondJudgmentParameter = Σ(LuminanceDiff[k]) / NumOfSpotPixels, where LuminanceDiff[k] represents the luminance difference between the kth pixel in the spot area and the pixels in the surrounding non-spot area, and NumOfSpotPixels represents the total number of pixels in the spot area.

[0073] Another option is to calculate the maximum value of the luminance difference, which can highlight the maximum luminance difference between the spot area and the surrounding pixels.

[0074] The calculated second judgment parameter is compared with a preset spot detection threshold.

[0075] If the second judgment parameter is greater than or equal to the threshold, it is considered that there is a significant spot defect in the image block and it is recorded as failed; otherwise, it is recorded as passed.

[0076] Through the above embodiment, the spot detection process in step S2 can be implemented, and the second judgment parameter for judging the spot defect can be obtained. This process not only takes into account the existence of the spot area, but also quantifies the significance of the spot defect by calculating the brightness difference between the spot area and the surrounding pixels, which helps to accurately identify the spot defects in the picture.

[0077] In a preferred embodiment of the present invention, the bad pixel detection process in step S2 includes:

[0078] For each image block, the area of ​​the bad pixel therein is calculated to obtain the third judgment parameter; the bad pixel is a pure white and / or pure black pixel.

[0079] Specifically, in this embodiment, there is a decomposed image obtained by processing in step S1 (the processing process is the same as in the above embodiment), and the image is divided into a plurality of image blocks of equal size. We will perform bad pixel detection on each image block.

[0080] In this embodiment, a bad pixel is defined as a pure white or pure black pixel. Specifically, if the brightness value of a pixel is equal to the maximum brightness value (indicating pure white) or the minimum brightness value (indicating pure black) of the image, and the brightness of this pixel is significantly different from that of the surrounding pixels (a threshold can be set according to actual conditions to determine), it is considered a bad pixel.

[0081] For each image block (denoted as Block[i][j]), traverse each pixel in it and check whether its brightness value is equal to the maximum brightness value or the minimum brightness value of the image.

[0082] If the brightness value of a pixel meets the above conditions, and the brightness difference between it and the surrounding pixels exceeds a preset threshold (this threshold can be set according to the actual situation of the image and the detection requirements), it will be marked as a bad pixel.

[0083] Once the bad pixels are identified, the total area occupied by these bad pixels is calculated as the third judgment parameter.

[0084] This can be achieved by traversing all pixels in the image block and counting the number of pixels marked as bad pixels. Since the image blocks are of equal size, the number of bad pixels can be directly converted into area (the area represented by each pixel is fixed).

[0085] Assuming that the resolution of the image block is MxN pixels and the number of bad pixels is NumOfBadPixels, the area of ​​the bad pixels can be expressed as: BadPixelArea = NumOfBadPixels*(image block area / MxN). However, here, in order to simplify the calculation, the number of bad pixels is usually used directly as the measure of the area (that is, assuming that the area of ​​each pixel is 1 unit area).

[0086] The third judgment parameter is the calculated bad pixel area (or the number of bad pixels).

[0087] The calculated third judgment parameter is compared with a preset bad pixel area threshold.

[0088] If the third judgment parameter is greater than or equal to the threshold, it is considered that there is a significant bad pixel defect in the image block and it is recorded as failed; otherwise, it is recorded as passed.

[0089] Through the above embodiment, the bad pixel detection process in step S2 can be implemented, and the third judgment parameter for judging the bad pixel defect can be obtained. This process not only takes into account the existence of bad pixels, but also quantifies the significance of bad pixel defects by calculating the area of ​​bad pixels, which helps to accurately identify bad pixel defects in the picture.

[0090] In a preferred embodiment of the present invention, Figure 1 As shown, the method further includes step S4, which gradually lowers the brightness of the projector from the highest brightness. Each time the brightness of the projector is adjusted, the detection method in steps S1 to S3 is executed once to obtain the corresponding detection result.

[0091] Specifically, in a preferred embodiment of the present invention, an additional step S4 is introduced, which is intended to optimize the image quality and identify possible problems with the image at different brightness levels by gradually reducing the brightness of the projector and executing the detection method in steps S1 to S3 after each adjustment.

[0092] The brightness of a projector is one of the key factors affecting image quality. Too high a brightness may cause overexposure of the image, loss of details, or even a halo effect; while too low a brightness may make the image dim, reduce contrast, and affect the viewing experience. Therefore, finding a suitable brightness setting is crucial to ensuring image quality.

[0093] First, the projector is set to its highest brightness level. The process camera is used to capture the inspection image of the projector, and then image processing is performed to obtain multiple decomposed images. The decomposed images should contain various colors, brightness and contrast levels to fully evaluate the image quality.

[0094] At the highest brightness level, the detection method in steps S1 to S3 is performed, including segmenting the test image into image blocks, detecting defects in the image blocks (such as bad pixels, noise points, spots, brightness detection, etc.), and calculating corresponding judgment parameters (such as bad pixel area, noise point density, etc.).

[0095] The brightness of the projector is gradually reduced with a certain step size (such as 5%, 10%, etc.). In another embodiment, the brightness level can be adjusted with a maximum of 100% and a minimum of 6%, from dark to bright as follows:

[0096] 6%-14%-22%-31%-36%-47%-55%-63%-71%-79%-87%-100%

[0097] After each brightness adjustment, the detection method in steps S1 to S3 is re-executed to obtain the detection result at the current brightness level.

[0098] By gradually adjusting the brightness of the projector and performing image detection at each brightness, the image performance of the projector under different brightness conditions can be fully evaluated. This helps to find image inconsistency defects that may occur when the brightness of the projector changes, such as uneven brightness and color shift.

[0099] By performing multiple tests at different brightness levels, you can evaluate the adaptability and stability of the projector under different brightness conditions. This helps ensure that the projector can maintain stable picture quality in various usage scenarios and avoid picture distortion or failure caused by brightness changes.

[0100] Through automated and standardized detection methods, the consistency of images under multiple brightness levels can be detected in a short time. This improves the detection efficiency while ensuring the accuracy and reliability of the detection results.

[0101] The present invention also provides a system for detecting image consistency defects of a projector, which uses the above-mentioned image consistency defect detection method, such as Figure 2 As shown, including:

[0102] An image acquisition module 1 is used to acquire a detection image captured by a projector, and then perform image processing on the detection image to obtain multiple decomposed images, and divide each decomposed image into multiple image blocks of equal size;

[0103] The image detection module 2 is connected to the image acquisition module 1 and is used to calculate the peripheral brightness difference of each image block in turn to obtain a first judgment parameter, perform spot detection to obtain a second judgment parameter, and perform bad pixel detection to obtain a third judgment parameter;

[0104] The defect judgment module 3 is connected to the image detection module 2, and is used to output a detection result indicating that the detection is passed when the first judgment parameter, the second judgment parameter and the third judgment parameter are within the corresponding standard range, and to output a detection result indicating that the detection is failed when the first judgment parameter, the second judgment parameter, or the third judgment parameter is not within the corresponding standard range.

[0105] In a preferred embodiment of the present invention, the image detection module 2 includes:

[0106] The peripheral brightness difference calculation unit 21 is used to calculate the average brightness value of each pixel in each image block, and calculate the difference between the average brightness value of each image block and its adjacent image blocks to obtain a first judgment parameter.

[0107] In a preferred embodiment of the present invention, the image detection module 2 includes:

[0108] The spot detection unit 22 identifies the spot area in each image block, and calculates the brightness difference between the spot area and surrounding pixels to obtain a second judgment parameter.

[0109] In a preferred embodiment of the present invention, the image detection module 2 includes:

[0110] The bad pixel detection unit 23 is used to calculate the area of ​​the bad pixels in each image block to obtain a third judgment parameter; the bad pixels are pure white and / or pure black pixels.

[0111] In a preferred embodiment of the present invention, it also includes a brightness adjustment module 4, which is used to gradually reduce the brightness of the projector from the highest brightness. Each time the brightness of the projector is adjusted, the detection process in the image acquisition module 1, the image detection module 2 and the defect judgment module 3 is executed once to obtain the corresponding detection result.

[0112] The above are only preferred embodiments of the present invention, and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the contents of this specification and illustrations should be included in the protection scope of the present invention.

Claims

1. A method for detecting image consistency defects of a projector, characterized in that: include: Step S1, obtaining a detection image captured by a projector, then performing image processing on the detection image to obtain a plurality of decomposed images, and dividing each of the decomposed images into a plurality of image blocks of equal size; Step S2, performing peripheral brightness difference calculation on each image block in turn to obtain a first judgment parameter, performing spot detection to obtain a second judgment parameter, and performing bad pixel detection to obtain a third judgment parameter; Step S3, judging whether the first judgment parameter, the second judgment parameter and the third judgment parameter are within corresponding standard ranges: If yes, the test passed; If not, the test fails.

2. The image consistency defect detection method according to claim 1, characterized in that: The peripheral brightness difference calculation process in step S2 includes: The first judgment parameter is obtained by calculating the average brightness value of each pixel in each image block, and calculating the difference between the average brightness value of each image block and each of its adjacent image blocks.

3. The image consistency defect detection method according to claim 1, characterized in that: The spot detection process in step S2 includes: For each of the image blocks, a spot area is identified therein, and the brightness difference between the spot area and surrounding pixels is calculated to obtain the second judgment parameter.

4. The image consistency defect detection method according to claim 1, characterized in that: The bad pixel detection process in step S2 includes: For each of the image blocks, the area of ​​the bad pixels therein is calculated to obtain the third judgment parameter; the bad pixels are pure white and / or pure black pixels.

5. The image consistency defect detection method according to claim 1, characterized in that: The method further includes step S4, wherein the brightness of the projector is gradually reduced from the highest brightness. Each time the brightness of the projector is adjusted, the detection method in steps S1 to S3 is executed once to obtain a corresponding detection result.

6. A system for detecting image consistency defects of a projector, characterized in that: The method for detecting image consistency defects as claimed in any one of claims 1 to 5 comprises: An image acquisition module is used to acquire a detection image captured by a projector, and then perform image processing on the detection image to obtain a plurality of decomposed images, and divide each of the decomposed images into a plurality of image blocks of equal size; An image detection module, connected to the image acquisition module, configured to sequentially perform peripheral brightness difference calculation on each image block to obtain a first judgment parameter, perform spot detection to obtain a second judgment parameter, and perform bad pixel detection to obtain a third judgment parameter; A defect judgment module is connected to the image detection module, and is used to output a detection result indicating that the detection is passed when it is judged that the first judgment parameter, the second judgment parameter and the third judgment parameter are within the corresponding standard range, and to output a detection result indicating that the detection is failed when it is judged that the first judgment parameter, the second judgment parameter, or the third judgment parameter is not within the corresponding standard range.

7. The image consistency defect detection system according to claim 6, characterized in that: The image detection module includes: The peripheral brightness difference calculation unit is used to calculate the average brightness value of each pixel in each image block, and calculate the difference between the average brightness value of each image block and its adjacent image blocks to obtain the first judgment parameter.

8. The image consistency defect detection system according to claim 6, characterized in that: The image detection module includes: The spot detection unit identifies a spot area in each image block, and calculates a brightness difference between the spot area and surrounding pixels to obtain the second judgment parameter.

9. The image consistency defect detection system according to claim 6, characterized in that: The image detection module includes: The bad pixel detection unit is used to calculate the area of ​​the bad pixels in each image block to obtain the third judgment parameter; the bad pixels are pure white and / or pure black pixels.

10. The image consistency defect detection system according to claim 6, characterized in that: It also includes a brightness adjustment module, which is used to gradually reduce the brightness of the projector from the highest brightness. Each time the brightness of the projector is adjusted, the detection process in the image acquisition module, the image detection module and the defect judgment module is executed once to obtain the corresponding detection result.

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