Image projection detection method and system

Abstract

The invention provides an image projection detection method and system, and relates to the technical field of image projection detection. According to the method, the original image is quickly divided into a plurality of image areas, the display main body is accurately identified, the actual projection image is automatically aligned with the original image, and the actual projection image is divided into the corresponding projection areas, so that the image projection can be conveniently and quickly detected; according to the method, a weight value is allocated for each projection area, more important areas can be processed preferentially in the detection process according to a plurality of weight levels, so that distribution of detection resources is further optimized while the detection quality is ensured, and finally, when feature parameters in an actual projection image exceed a verification threshold range, the detection resources are further optimized. The projection image can be quickly marked as abnormal, so that the abnormal area in the projection image can be quickly identified, and the detection accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image projection detection, and in particular to an image projection detection method and system. Background Art

[0002] During the projection process, the quality of the projected image often fluctuates due to the influence of various factors such as equipment performance, environmental conditions, and projection materials. These fluctuations may manifest as image blur, color distortion, uneven brightness, etc., which seriously affect the audience's visual experience and the effect of information transmission. Therefore, it is necessary to test the image projection.

[0003] Traditional projection image detection methods often adopt a global detection strategy, that is, uniformly processing and analyzing the entire projection image. However, in practical applications, projection images often contain multiple different display subjects, such as text, graphics, charts, etc. These display subjects have significant differences in content, layout, and importance. Therefore, the use of a global detection strategy is often difficult to accurately reflect the actual situation of different display subjects, and it is also impossible to achieve priority processing of important areas.

[0004] Secondly, it is difficult for traditional projection image detection methods or systems to quickly identify and locate abnormal areas. With the continuous development of projection technology, the resolution and complexity of projection images are also increasing. This makes the detection of projection images more and more arduous, and the requirements for computing resources and time costs are also increasing. Traditional detection methods often cannot achieve priority processing of different important areas, resulting in waste of detection resources and low detection efficiency.

[0005] Therefore, it is necessary to provide an image projection detection method and system to solve the above technical problems. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides an image projection detection method and system for solving the problems that traditional projection image detection methods are difficult to accurately reflect the actual conditions of different display subjects, have slow abnormal area recognition, waste of detection resources and low detection efficiency.

[0007] The present invention provides an image projection detection method, the detection method comprising the following steps: Acquire an original image and divide it into a plurality of image regions according to the range of the display subject; Acquire an actual projection image through an image acquisition device, and divide the actual projection image into a plurality of projection areas corresponding to the image areas according to the divided plurality of image areas; By evaluating the importance, weights are assigned to display subjects, and the divided multiple projection areas are divided into projection weight levels according to a preset weight threshold range, wherein the projection weight levels specifically include low, medium and high levels; Extracting characteristic parameters of the subject displayed in the original image, and setting a corresponding verification threshold range for each projection area according to the extracted characteristic parameters of the subject displayed in the original image, wherein the higher the projection weight level of the projection area, the smaller the verification threshold range; The characteristic parameters of the image information of multiple projection areas in the actual projection image are extracted through image processing technology, and the extracted characteristic parameters of the image information of multiple projection areas are compared with the corresponding verification threshold range. The characteristic parameters exceeding the verification threshold range are displayed as abnormal.

[0008] Preferably, the specific steps of acquiring the original image and dividing it into a plurality of image areas according to the range of the display subject are: Acquire an original image to be projected, and identify a display subject in the original image, wherein the display subject includes text, graphics or charts; According to the content or layout of the display subject, the original image is divided into a plurality of image areas, a unique identifier is assigned to each divided image area, and the boundary coordinates of each image area are recorded.

[0009] Preferably, the actual projection image is acquired by an image acquisition device, and the actual projection image is divided into a plurality of projection areas corresponding to the image areas according to the divided plurality of image areas, and the specific steps are: The original image is projected onto a target surface using a projection device, and the actual projection image is acquired through a camera or a scanning device in an image acquisition device; According to the divided multiple image regions, aligning the coordinates of the actual projection image with the coordinates of the original image, and dividing the actual projection image into multiple projection regions accordingly; Record the bounding coordinates and identifier of each projected region.

[0010] Preferably, the weights are assigned to the display subjects by evaluating the importance, and the projection weight levels are divided into the multiple divided projection areas in combination with the preset weight threshold range, and the specific steps are as follows: According to the text density, contrast or visual focus range of the layout, each display subject is evaluated according to a plurality of preset scoring threshold ranges to obtain an evaluation result of each display subject; The corresponding weight values ​​are set for the preset multiple scoring threshold ranges respectively, and according to the evaluation value of each display subject, a weight value corresponding to the evaluation result is allocated to each display subject; According to the weight value of the display subject, a weight value is assigned to each projection area, and according to the preset weight threshold range, each projection area is divided into three corresponding weight levels of low, medium or high.

[0011] Preferably, the feature parameters of the subject displayed in the original image are extracted, and a corresponding verification threshold range is set for each projection area according to the extracted feature parameters of the subject displayed in the original image, wherein the higher the projection weight level of the projection area, the smaller the verification threshold range, and the specific steps are: Extracting characteristic parameters of a subject displayed in the original image, wherein the characteristic parameters include color, brightness, contrast or clarity; A basic verification threshold range is set for each feature parameter according to the extracted feature parameters, and a weight coefficient is set for each weight level. The threshold node of the basic verification threshold range is multiplied by the corresponding weight coefficient to obtain the adjusted verification threshold range.

[0012] Preferably, the feature parameters of the image information of the multiple projection areas in the actual projection image are extracted by image processing technology, and the extracted feature parameters of the image information of the multiple projection areas are compared with the corresponding verification threshold range, and the feature parameters exceeding the verification threshold range are displayed as abnormal, and the specific steps are: Use image recognition or edge detection in image processing technology to extract image information feature parameters of each projection area from the actual projection image; Compare the extracted characteristic parameters with the adjusted verification threshold range to determine whether the extracted characteristic parameters fall within the adjusted verification threshold range; The projection area to which the characteristic parameters that are judged to be beyond the verification threshold range belong is marked as abnormal, and an abnormality report is generated, listing all the projection areas marked as abnormal and their characteristic parameters.

[0013] An image projection detection system, the detection system comprising: An image division module, used for acquiring an original image and dividing it into a plurality of image regions according to the range of a display subject; A region division module, used for acquiring an actual projection image through an image acquisition device, and dividing the actual projection image into a plurality of projection regions corresponding to the image regions according to the divided plurality of image regions; A weight assignment module is used to assign weights to display subjects by evaluating their importance, and to classify the divided multiple projection areas into projection weight levels in combination with a preset weight threshold range, wherein the projection weight levels specifically include low, medium and high levels; A feature extraction and definition module is used to extract feature parameters of the display subject in the original image, and set a corresponding verification threshold range for each projection area according to the extracted feature parameters of the display subject in the original image, wherein the higher the projection weight level of the projection area, the smaller the verification threshold range; The feature detection module is used to extract the feature parameters of the image information of multiple projection areas in the actual projection image through image processing technology, and compare the extracted feature parameters of the image information of multiple projection areas with the corresponding verification threshold range. The feature parameters exceeding the verification threshold range are displayed as abnormal.

[0014] Compared with the related art, the image projection detection method and system provided by the present invention have the following beneficial effects: The present invention quickly divides the original image into multiple image areas and accurately identifies the display subject, automatically aligns the actual projected image with the original image, and divides them into corresponding projection areas, so as to facilitate rapid detection of image projection. Secondly, according to factors such as the content, layout, text density, contrast or visual focus of the display subject, a weight value is assigned to each projection area, and multiple weight levels are provided accordingly, so that more important areas can be processed preferentially during the detection process, thereby further optimizing the allocation of detection resources while ensuring the detection quality. Finally, a flexible verification threshold range is set for each projection area according to the characteristic parameters. When the characteristic parameters in the actual projection image exceed the verification threshold range, it can be quickly marked as abnormal, thereby realizing rapid identification of abnormal areas in the projection image and improving the accuracy of detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of an image projection detection method of the present invention; Figure 2 The system block diagram of an image projection detection system of the present invention. DETAILED DESCRIPTION

[0016] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0017] Embodiment 1 like Figure 1 As shown, an image projection detection method specifically includes the following steps: S1, obtaining an original image and dividing it into a plurality of image regions according to the range of the display subject; S2, acquiring an actual projection image through an image acquisition device, and dividing the actual projection image into a plurality of projection areas corresponding to the image areas according to the divided plurality of image areas; S3, assigning weights to display subjects by evaluating importance, and dividing the divided multiple projection areas into projection weight levels in combination with a preset weight threshold range, wherein the projection weight levels specifically include low, medium and high levels; S4, extracting characteristic parameters of the subject displayed in the original image, and setting a corresponding verification threshold range for each projection area according to the extracted characteristic parameters of the subject displayed in the original image, wherein the higher the projection weight level of the projection area, the smaller the verification threshold range; S5. Extract characteristic parameters of multiple projection area image information in the actual projection image through image processing technology, and compare the extracted characteristic parameters of multiple projection area image information with the corresponding verification threshold range. The characteristic parameters exceeding the verification threshold range are displayed as abnormal.

[0018] In the specific implementation process, the specific steps of step S1 are: S101, obtaining an original image to be projected, and identifying a display subject in the original image, wherein the display subject includes text, graphics or charts.

[0019] Specifically, the original image to be projected is obtained through an image acquisition device such as a scanner, a digital camera or professional image acquisition software. Next, the original image is analyzed using computer vision technology in image recognition technology to identify the display subject therein, where the display subject includes key information elements such as text, graphics, and charts. These information elements need to be accurately transmitted during the projection process.

[0020] S102: Divide the original image into a plurality of image regions according to the content or layout of the display subject, assign a unique identifier to each divided image region, and record the boundary coordinates of each image region.

[0021] Specifically, after identifying the display subject, the original image is divided into multiple image areas according to the content or layout characteristics of the display subject, wherein the image areas can be divided based on the natural boundaries of the display subject (such as text paragraphs, chart frames, etc.), or based on preset rules (such as fixed size, fixed ratio, etc.). Then, a unique identifier is assigned to each divided image area so that these areas can be accurately identified and referenced in the subsequent detection process. Finally, the boundary coordinates of each image area are recorded. These coordinate information are used to align the actual projected image with the original image in subsequent steps and divide the corresponding projection area.

[0022] Exemplarily, the original image is divided into multiple image areas based on the identified text areas, chart areas, and graphic areas. Then, a unique identifier (such as "area 1", "area 2", etc.) is assigned to each image area, and the boundary coordinates of each image area are recorded. In subsequent steps, these identifiers and coordinate information can be used to accurately align the actual projected image with the original image and divide the corresponding projection area.

[0023] In the specific implementation process, the specific steps of step S2 are: S201, using a projection device to project an original image onto a target surface, and obtaining an actual projection image through a camera or a scanning device in an image acquisition device.

[0024] Specifically, the original image to be detected is projected onto a designated target surface through a projection device. The target surface can be a wall, a curtain, a screen, etc. The specific selection depends on the actual application scenario and the requirements of the projection effect. During the projection process, the projected image is captured and recorded using a camera or scanning device in the image acquisition device. It should be noted that when both the projection device and the image acquisition device have automatic control functions, the automatic operation of projection and acquisition can be achieved through preset programs or instructions.

[0025] S202 . Align the coordinates of the actual projection image with the coordinates of the original image according to the divided multiple image regions, and divide the actual projection image into multiple projection regions accordingly.

[0026] Specifically, the coordinates of the actual projected image are aligned with the coordinates of the original image by using image registration or image transformation in image processing technology. It should be noted that this process needs to ensure that each pixel in the projected image can be accurately matched with the corresponding pixel in the original image. Then, according to the divided multiple image areas, the actual projected image is divided into multiple projection areas accordingly. These projection areas should correspond one-to-one with the image areas in the original image to ensure the accuracy of subsequent detection work.

[0027] In this embodiment, the projection image of a company's promotional materials uses image processing technology to align the coordinates of the actual projection image with the coordinates of the original image. Then, according to the image areas divided in step S1 (such as text areas or chart areas, etc.), the actual projection image is divided into multiple projection areas accordingly. These projection areas correspond one-to-one with the image areas in the original image, providing an accurate data basis for subsequent detection work.

[0028] S203: Record the boundary coordinates and identifier of each projection area.

[0029] Specifically, the boundary coordinates of each projection area are recorded by using image processing technology or manual annotation. It should be noted that these coordinate information are used to accurately locate the projection area in subsequent steps, and each projection area is assigned a unique identifier so that these areas can be accurately identified and referenced in the subsequent detection process.

[0030] In this embodiment, the projection image of a company's promotional materials uses image processing technology to automatically record the boundary coordinates of each projection area and assign it a unique identifier. For example, for the text area, its identifier is set to "Text_Area_1" and its boundary coordinates are recorded as (x1, y1, x2, y2). These identifiers and coordinate information will be used to accurately locate the projection area in the subsequent detection process.

[0031] In the specific implementation process, the specific steps of step S3 are: S301 , evaluating each display subject according to the text density, contrast or visual focus range of the layout and according to a plurality of preset scoring threshold ranges, to obtain an evaluation result of each display subject.

[0032] Specifically, text density evaluation: text density is defined as the number of characters or pixel density per unit area, which is used to measure the density of text information in the display subject. Then, the calculated text density is compared with the preset text density score threshold range, and the corresponding score is given according to the comparison result; contrast evaluation: contrast is the brightness difference between the bright area and the dark area in the image, which affects the clarity and readability of the image. Using the existing image processing software, the contrast value of each display subject can be calculated and compared with the preset contrast score threshold range to give a corresponding score; layout visual focus evaluation: according to the layout position, size, shape and other factors of the display subject in the original image, its visual focus range is evaluated. The visual focus is the area in the image that attracts the observer's attention the most and usually contains the most important information. By calculating the distance between the display subject and the center of the image or the visual focus, the degree of overlap and other parameters, the importance of its visual focus can be evaluated and given a corresponding score. Among them, multiple scoring threshold ranges are preset manually. In this embodiment, the scoring threshold ranges are set as: excellent (90-100 points), good (80-89 points), medium (70-79 points), passing (60-69 points) and failing (0-59 points).

[0033] S302: setting corresponding weight values ​​for the preset multiple scoring threshold ranges respectively, and assigning a weight value corresponding to the evaluation result to each display subject according to the evaluation value of each display subject.

[0034] Specifically, corresponding weight values ​​are set for the preset multiple scoring threshold ranges, namely, excellent (90-100 points), good (80-89 points), medium (70-79 points), passing (60-69 points) and failing (0-59 points), and the weight values ​​corresponding to the scoring threshold ranges are 5, 4, 3, 2 and 1 respectively.

[0035] S303: assign a weight value to each projection area according to the weight value of the display subject, and divide each projection area into three corresponding weight levels of low, medium or high according to a preset weight threshold range.

[0036] Specifically, for each projection area, the weight values ​​of all the display subjects contained therein are weighted averaged, that is, the weight value of each display subject is multiplied by its corresponding importance coefficient, and then all the products are added together and divided by the sum of the importance coefficients of all display subjects. It should be noted that the importance coefficient is defined according to the different display subjects of the projected image each time.

[0037] In this embodiment, there are two projection areas A and B, where area A contains two display entities: a chart (weight value 3) and a text paragraph (weight value 2); area B also contains two display entities: a picture (weight value 2) and a table (weight value 1). At the same time, the importance coefficients of the chart, text paragraph, picture and table are set to 1.5, 1.2, 1.0 and 0.8 respectively, then the weighted average weight value of area A can be calculated as: (3 × 1.5 + 2 × 1.2) ÷ (1.5+ 1.2) = 2.7 ÷ 2.7 = 1.0 (where the sum of the importance coefficients of area A is 1.5+1.2=2.7); the weighted average weight value of area B can be calculated as: (2 × 1.0 + 1 × 0.8) ÷ (1.0 + 0.8) = 2.8 ÷ 1.8 ≈1.56 (the sum of the importance coefficients of area B is 1.0+0.8=1.8).

[0038] In the specific implementation process, the specific steps of step S4 are: S401. Extract characteristic parameters of a subject displayed in an original image, wherein the characteristic parameters include color, brightness, contrast or clarity.

[0039] Specifically, the acquired original image is preprocessed, including denoising and contrast enhancement operations. Feature parameters are selected for extraction according to the type of display subject and detection requirements. In this embodiment, the feature parameters include color, brightness, contrast or clarity. The existing image processing algorithms or software are used to extract feature parameters of the preprocessed original image. Exemplarily, a color space conversion algorithm is used to extract color features, a histogram statistical method is used to extract brightness and contrast features, and an edge detection algorithm is used to extract clarity features.

[0040] S402. Set a basic verification threshold range for each feature parameter according to the extracted feature parameters, and set a weight coefficient for each weight level, multiply the threshold node of the basic verification threshold range by the corresponding weight coefficient to obtain an adjusted verification threshold range.

[0041] In this embodiment, the verification threshold range of basic feature parameters such as color, brightness and contrast is set, and then a weight coefficient is set for each weight level according to the weight value assigned to each projection area in step S3. Finally, we multiply the threshold node of the basic verification threshold range with the corresponding weight coefficient to obtain the adjusted verification threshold range. For the brightness feature parameter, the basic verification threshold range is set to [100, 200], and for the projection area of ​​the high weight level, it is adjusted to [120, 240]; for the projection area of ​​the low weight level, it is adjusted to [80, 160].

[0042] In the specific implementation process, the specific steps of step S5 are: S501 , using image recognition or edge detection in image processing technology to extract image information feature parameters of each projection area from the actual projection image.

[0043] Specifically, the actual projection image is preprocessed, including denoising and contrast enhancement operations, and the preprocessed image is identified and edge detected, specifically implemented through an existing deep learning algorithm, so as to accurately identify the boundary of each projection area and extract the characteristic parameters of the image information in the projection area. According to the characteristic parameters (color, brightness, contrast, clarity, etc.) set in step S4, the corresponding characteristic parameter values ​​are extracted from the image information of each projection area using image processing software.

[0044] S502: Compare the extracted characteristic parameters with the adjusted verification threshold range to determine whether the extracted characteristic parameters fall within the adjusted verification threshold range.

[0045] Specifically, the characteristic parameter value of each projection area extracted in step S501 is compared with the adjusted verification threshold range, and according to the comparison result, the characteristic parameters of each projection area are divided into two categories: characteristic parameters belonging to the adjusted verification threshold range and characteristic parameters exceeding the verification threshold range.

[0046] S503: Mark the projection area to which the characteristic parameters that are judged to be beyond the verification threshold range belong as abnormal, and generate an abnormality report, listing all the projection areas marked as abnormal and their characteristic parameters.

[0047] Specifically, according to the comparison result of step S502, the projection area to which the characteristic parameters exceeding the verification threshold range belong is marked as abnormal, so as to facilitate rapid identification of abnormal areas in the projection image. Then, the image processing software is used to automatically generate an abnormality report, which should include all projection areas marked as abnormal and their characteristic parameter values, abnormality types (such as exceeding the upper or lower limit of the verification threshold range). Secondly, the generated abnormality report is output to users or relevant managers so that they can take timely measures to repair or improve.

[0048] Embodiment 2 like Figure 2 As shown, an image projection detection system applied to an image projection detection method specifically includes: An image division module, used for acquiring an original image and dividing it into a plurality of image regions according to the range of a display subject; A region division module, used for acquiring an actual projection image through an image acquisition device, and dividing the actual projection image into a plurality of projection regions corresponding to the image regions according to the divided plurality of image regions; A weight assignment module is used to assign weights to display subjects by evaluating their importance, and to classify the divided multiple projection areas into projection weight levels in combination with a preset weight threshold range, wherein the projection weight levels specifically include low, medium and high levels; A feature extraction and definition module is used to extract feature parameters of the display subject in the original image, and set a corresponding verification threshold range for each projection area according to the extracted feature parameters of the display subject in the original image, wherein the higher the projection weight level of the projection area, the smaller the verification threshold range; The feature detection module is used to extract the feature parameters of the image information of multiple projection areas in the actual projection image through image processing technology, and compare the extracted feature parameters of the image information of multiple projection areas with the corresponding verification threshold range. The feature parameters exceeding the verification threshold range are displayed as abnormal.

[0049] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0051] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. An image projection detection method, characterized in that: The detection method comprises the following steps: Acquire an original image and divide it into a plurality of image regions according to the range of the display subject; Acquire an actual projection image through an image acquisition device, and divide the actual projection image into a plurality of projection areas corresponding to the image areas according to the divided plurality of image areas; By evaluating the importance, weights are assigned to display subjects, and the divided multiple projection areas are divided into projection weight levels according to a preset weight threshold range, wherein the projection weight levels specifically include low, medium and high levels; Extracting characteristic parameters of the subject displayed in the original image, and setting a corresponding verification threshold range for each projection area according to the extracted characteristic parameters of the subject displayed in the original image, wherein the higher the projection weight level of the projection area, the smaller the verification threshold range; The characteristic parameters of the image information of multiple projection areas in the actual projection image are extracted through image processing technology, and the extracted characteristic parameters of the image information of multiple projection areas are compared with the corresponding verification threshold range. The characteristic parameters exceeding the verification threshold range are displayed as abnormal.

2. The image projection detection method according to claim 1, characterized in that: The specific steps of obtaining the original image and dividing it into multiple image areas according to the range of the display subject are as follows: Acquire an original image to be projected, and identify a display subject in the original image, wherein the display subject includes text, graphics or charts; According to the content or layout of the display subject, the original image is divided into a plurality of image areas, a unique identifier is assigned to each divided image area, and the boundary coordinates of each image area are recorded.

3. The image projection detection method according to claim 1, characterized in that: The actual projection image is acquired by an image acquisition device, and the actual projection image is divided into a plurality of projection areas corresponding to the image areas according to the divided multiple image areas, and the specific steps are: The original image is projected onto a target surface using a projection device, and the actual projection image is acquired through a camera or a scanning device in an image acquisition device; According to the divided multiple image regions, aligning the coordinates of the actual projection image with the coordinates of the original image, and dividing the actual projection image into multiple projection regions accordingly; Record the bounding coordinates and identifier of each projected region.

4. The image projection detection method according to claim 1, characterized in that: The specific steps of assigning weights to display subjects by evaluating importance and dividing the divided multiple projection areas into projection weight levels in combination with a preset weight threshold range are as follows: According to the text density, contrast or visual focus range of the layout, each display subject is evaluated according to a plurality of preset scoring threshold ranges to obtain an evaluation result of each display subject; The corresponding weight values ​​are set for the preset multiple scoring threshold ranges respectively, and according to the evaluation value of each display subject, a weight value corresponding to the evaluation result is allocated to each display subject; According to the weight value of the display subject, a weight value is assigned to each projection area, and according to the preset weight threshold range, each projection area is divided into three corresponding weight levels of low, medium or high.

5. The image projection detection method according to claim 1, characterized in that: The characteristic parameters of the subject displayed in the original image are extracted, and a corresponding verification threshold range is set for each projection area according to the characteristic parameters of the subject displayed in the original image extracted, wherein the higher the projection weight level of the projection area, the smaller the verification threshold range, and the specific steps are as follows: Extracting characteristic parameters of a subject displayed in the original image, wherein the characteristic parameters include color, brightness, contrast or clarity; A basic verification threshold range is set for each feature parameter according to the extracted feature parameters, and a weight coefficient is set for each weight level. The threshold node of the basic verification threshold range is multiplied by the corresponding weight coefficient to obtain the adjusted verification threshold range.

6. The image projection detection method according to claim 5, characterized in that: The method extracts characteristic parameters of multiple projection area image information in the actual projection image by image processing technology, and compares the extracted characteristic parameters of multiple projection area image information with the corresponding verification threshold range. The characteristic parameters exceeding the verification threshold range are displayed as abnormal. The specific steps are as follows: Use image recognition or edge detection in image processing technology to extract image information feature parameters of each projection area from the actual projection image; Compare the extracted characteristic parameters with the adjusted verification threshold range to determine whether the extracted characteristic parameters fall within the adjusted verification threshold range; The projection area to which the characteristic parameters that are judged to be beyond the verification threshold range belong is marked as abnormal, and an abnormality report is generated, listing all the projection areas marked as abnormal and their characteristic parameters.

7. An image projection detection system, applied to an image projection detection method according to any one of claims 1 to 6, characterized in that: The detection system comprises: An image division module, used for acquiring an original image and dividing it into a plurality of image regions according to the range of a display subject; A region division module, used for acquiring an actual projection image through an image acquisition device, and dividing the actual projection image into a plurality of projection regions corresponding to the image regions according to the divided plurality of image regions; A weight assignment module is used to assign weights to display subjects by evaluating their importance, and to classify the divided multiple projection areas into projection weight levels in combination with a preset weight threshold range, wherein the projection weight levels specifically include low, medium and high levels; A feature extraction and definition module is used to extract feature parameters of the display subject in the original image, and set a corresponding verification threshold range for each projection area according to the extracted feature parameters of the display subject in the original image, wherein the higher the projection weight level of the projection area, the smaller the verification threshold range; The feature detection module is used to extract the feature parameters of the image information of multiple projection areas in the actual projection image through image processing technology, and compare the extracted feature parameters of the image information of multiple projection areas with the corresponding verification threshold range. The feature parameters exceeding the verification threshold range are displayed as abnormal.

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