Defect detection method and device for set top box, medium and equipment

Abstract

The embodiment of the invention discloses a set top box defect detection method and device, a medium and equipment, and relates to the technical field of image processing.The set top box is connected with a test signal source to output an image for defect detection, and firstly, a target parameter is determined in test parameters; other non-target parameters are adjusted according to the gradient to achieve coverage of the test condition under one target parameter, then the target parameters are adjusted in the gradient mode, the test process is cycled to achieve comprehensive coverage of the test condition, and then output detection images are classified and fused according to the categories of the non-target parameters to achieve classification fusion of the non-target parameters. The multi-scale fusion image under the target parameters of different gradients is obtained, the reliability of the test method and the quality of the output image are improved under the condition that the multi-source index comprehensive test is realized, and the defect detection effect of the set top box based on the image quality test is further improved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a defect detection method, device, medium and equipment for a set-top box. Background Art

[0002] The Internet TV set-top box is a device that looks like a home broadband modem. It can connect the Internet and TV. As long as the home has an Internet cable installed and is in use, you can plug an Internet cable from the router into this Internet set-top box to watch online programs and live broadcasts. Defect detection in the set-top box production process is a crucial link, which is directly related to the quality of the final product and user experience.

[0003] In addition to basic tests such as physical interface testing, radio frequency testing, and functional inspection, a series of specific tests are also required to verify the performance and quality of the set-top box, so as to achieve more complete defect detection. These specific tests include: channel search, channel and stereo, image quality, network TV, etc. Among them, image quality testing is crucial in the production and testing process of set-top boxes because it is directly related to the user's experience of watching TV programs.

[0004] Although the current testing method sets multiple test indicators, each indicator is independent of each other during the testing process. For example, the resolution test and contrast test are conducted separately. In fact, the influence of various indicators on users' viewing of programs exists simultaneously. The multi-source influence makes the testing method of a single indicator unreliable, which in turn leads to a reduction in the defect detection effect of the set-top box. Summary of the invention

[0005] The main purpose of the present application is to provide a set-top box defect detection method, device, medium and equipment, aiming to solve the problem that the single indicator testing method in the prior art is unreliable, which leads to a decrease in the defect detection effect of the set-top box based on image quality testing.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows: In a first aspect, an embodiment of the present application provides a defect detection method for a set-top box, comprising the following steps: Connect the target set-top box to the test signal source; According to the target parameter of the test, the non-target parameter is adjusted according to the first gradient to perform the test, and a plurality of original images are output; wherein the target parameter is one of color reproduction, resolution, contrast, brightness and clarity, and the non-target parameter is at least one of color reproduction, resolution, contrast, brightness and clarity, and the target parameter and the non-target parameter are not repeated in the same test; The target parameters are adjusted according to the second gradient, and the target parameters according to the test are returned, and the non-target parameters are adjusted according to the first gradient for testing, and a number of original images are output until the second gradient traversal is completed to obtain a number of detection images; Classify and fuse the detection images according to the types of non-target parameters to obtain the target detection images; Based on the target detection image, defect detection is performed on the target set-top box.

[0007] In a possible implementation manner of the first aspect, classifying and fusing the detection images according to the types of non-target parameters to obtain the target detection images includes: Classify the detection images according to the types of non-target parameters to obtain a detection image set; The images in the same detection image set are fused to obtain the target detection image.

[0008] In a possible implementation manner of the first aspect, fusing images in the same detection image set to obtain a target detection image includes: Fusing images in the same detection image set to obtain a number of first detection images; According to the second gradient, multi-scale fusion is performed on the first detection image to obtain a target detection image.

[0009] In a possible implementation manner of the first aspect, images in the same detection image set are fused to obtain a plurality of first detection images, including: Extract features from images in the same detection image set to obtain feature information; The feature information is fused to obtain a number of first detection images.

[0010] In a possible implementation manner of the first aspect, according to the target parameter of the test, the non-target parameter is adjusted according to the first gradient to perform the test, and a plurality of original images are output, including: According to the target parameters of the test, the non-target parameters are adjusted according to the first gradient to perform the test, and a plurality of test images are output; In the case that the test image is missing, the test image is interpolated and completed according to the change of the first gradient, and several original images are output.

[0011] In a possible implementation manner of the first aspect, according to the target parameter of the test, and adjusting the non-target parameter according to the first gradient to perform the test, before outputting the plurality of original images, the method further includes: The number of gradients of the first gradient is set according to the weight of the non-target parameter relative to the target parameter.

[0012] In a possible implementation manner of the first aspect, according to the target parameter of the test, and adjusting the non-target parameter according to the first gradient to perform the test, before outputting the plurality of original images, the method further includes: Set target parameters and non-target parameters according to test requirements.

[0013] In a second aspect, an embodiment of the present application provides a defect detection device for a set-top box, comprising: A connection module, the connection module is used to connect the target set-top box to the test signal source; A test module, the test module is used to adjust the non-target parameters according to the first gradient according to the target parameters of the test, and output a plurality of original images; wherein the target parameter is one of color reproduction, resolution, contrast, brightness and clarity, and the non-target parameter is at least one of color reproduction, resolution, contrast, brightness and clarity, and the target parameter and the non-target parameter are not repeated in the same test; A loop module, the loop module is used to adjust the target parameters according to the second gradient, return the target parameters according to the test, adjust the non-target parameters according to the first gradient for testing, output a number of original images, until the second gradient traversal is completed, and obtain a number of detection images; A fusion module is used to classify and fuse the detection images according to the types of non-target parameters to obtain the target detection images; The detection module is used to perform defect detection on the target set-top box based on the target detection image.

[0014] In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, the defect detection method for a set-top box provided in any one of the first aspects above is implemented.

[0015] In a fourth aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute a computer program so that the electronic device executes the defect detection method for a set-top box provided in any one of the first aspects above.

[0016] Compared with the prior art, the beneficial effects of this application are: The embodiment of the present application proposes a defect detection method, device, medium and equipment for a set-top box, the method comprising: connecting a target set-top box to a test signal source; adjusting non-target parameters according to a first gradient according to a target parameter of the test to perform a test, and outputting a plurality of original images; wherein the target parameter is one of color reproduction, resolution, contrast, brightness and clarity, and the non-target parameter is at least one of color reproduction, resolution, contrast, brightness and clarity, and the target parameter and the non-target parameter are not repeated in the same test; adjusting the target parameter according to a second gradient, and returning to the step of adjusting the non-target parameter according to the first gradient according to the target parameter of the test to perform a test, and outputting a plurality of original images, until the second gradient traversal is completed to obtain a plurality of detection images; classifying and fusing the detection images according to the type of non-target parameters to obtain a target detection image; and performing defect detection on the target set-top box based on the target detection image. The present application performs defect detection by connecting a set-top box to a test signal source output image. First, a target parameter is determined in the test parameters, and other non-target parameters are adjusted according to the gradient to achieve coverage of the test conditions under a target parameter. The target parameter is then adjusted by gradient, and the test process is cyclically performed to achieve full coverage of the test conditions. The output detection image is then classified and fused according to the category of the non-target parameter to obtain a multi-scale fused image under target parameters with different gradients. While realizing comprehensive testing of multi-source indicators, both the reliability of the test method and the quality of the output image are improved, thereby achieving an improvement in the defect detection effect of the set-top box based on image quality testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the structure of an electronic device of a hardware operating environment involved in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart of a defect detection method for a set-top box provided in an embodiment of the present application; Figure 3 A schematic diagram of a module of a defect detection device for a set-top box provided in an embodiment of the present application; Markings in the figure: 101 - processor, 102 - communication bus, 103 - network interface, 104 - user interface, 105 - memory. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0019] See attached Figure 1 , attached Figure 1This is a schematic diagram of the structure of an electronic device of the hardware operating environment involved in the embodiment of the present application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. Among them, the communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display (Display), an input unit such as a keyboard (Keyboard), and the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 105 may optionally be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) memory, or it may be a stable non-volatile memory (NVM), such as at least one disk storage. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component.

[0020] Those skilled in the art will appreciate that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0021] As attached Figure 1 As shown, the memory 105 as a storage medium may include an operating system, a network communication module, a user interface module, and a defect detection device of a set-top box.

[0022] In the attached Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in the present application can be set in the electronic device, and the electronic device calls the set-top box defect detection device stored in the memory 105 through the processor 101, and executes the set-top box defect detection method provided in the embodiment of the present application.

[0023] See attached Figure 2 Based on the hardware device of the foregoing embodiment, the embodiment of the present application provides a defect detection method for a set-top box, comprising the following steps: S10: Connect the target set-top box to the test signal source.

[0024] In the specific implementation process, the target set-top box is the set-top box that needs to be defect-detected; the defect detection performed in the embodiment of the present application is based on image quality testing, that is, the image is output through image quality testing, and the quality of the image is evaluated, so as to feedback whether the set-top box has defects. Test signal sources such as high-definition TV signals, DVD players, etc., simply put, give a signal to the set-top box to start normal playback, and these test signal sources make it play different from complex TV programs, which may be simple color images, or some images with simple calibration patterns.

[0025] S20: According to the target parameter of the test, the non-target parameter is adjusted according to the first gradient for testing, and a plurality of original images are output; wherein the target parameter is one of color reproduction, resolution, contrast, brightness and clarity, and the non-target parameter is at least one of color reproduction, resolution, contrast, brightness and clarity, and the target parameter and the non-target parameter are not repeated in the same test.

[0026] In the specific implementation process, color reproduction, resolution, contrast, brightness and clarity are the basic indicators of image quality testing. When the target parameter is used as the test indicator, the remaining non-target parameters are used as the basis for adjusting other indicators under the test indicator to obtain multi-scale features. This is why the target parameters and non-target parameters are set not to be repeated in the same test. The parameter setting can be determined according to the test requirements, that is: according to the target parameters of the test, and the non-target parameters are adjusted according to the first gradient for testing, before outputting several original images, the method also includes: Set target parameters and non-target parameters according to test requirements.

[0027] During the specific implementation process, various parameters and their specific values ​​are set according to the test requirements. For example, if the current test is a color reproduction test in an image quality test, the target parameter can be determined as color reproduction, and the non-target parameter can be determined as at least one of resolution, contrast, brightness and clarity. The specific number can be determined based on actual needs.

[0028] The first gradient is a method of stepping adjustment according to a rule. The first gradient mentioned in the embodiment of the present application is that each non-target parameter corresponds to a first gradient. Since each parameter will set a test range, the number of gradients of the first gradient determines the size of the step value within a relatively fixed range. If the number of gradients is set too small, it will not be enough to support multi-scale feature extraction. If it is too large, resources will be wasted on image processing. Therefore, the number of gradients can be determined according to the importance, that is, according to the target parameter of the test, and the non-target parameter is adjusted according to the first gradient for testing, before outputting a number of original images, the method also includes: The number of gradients of the first gradient is set according to the weight of the non-target parameter relative to the target parameter.

[0029] In the specific implementation process, the number of first gradient settings is determined by weight to ensure that the indicator data corresponding to the non-target parameter is sufficiently reliable. For example, if the target parameter is clarity, the importance of color reproduction relative to clarity is lower than that of contrast, so the number of gradients of the first gradient corresponding to contrast is set to be more than that of color reproduction. It should be noted that when adjusting according to the first gradient, not all of them need to be adjusted at the same time, but each is adjusted in steps so that the detection situation includes the combination of the gradient values ​​of each parameter.

[0030] In one embodiment, according to the target parameter of the test, the non-target parameter is adjusted according to the first gradient to perform the test, and a plurality of original images are output, including: According to the target parameters of the test, the non-target parameters are adjusted according to the first gradient to perform the test, and a plurality of test images are output; In the case that the test image is missing, the test image is interpolated and completed according to the change of the first gradient, and several original images are output.

[0031] In the specific implementation process, due to the continuous adjustment of parameters during the image quality test, many test images will be output. There may be a situation where an image output from a certain test is lost. In this case, in order not to delay detection and recognition, the missing image is completed using the change in the first gradient. The principle of linear interpolation is applied to image completion. Since the non-target parameters are adjusted according to the first gradient, this law will be fed back to the change of image pixels. According to this change in the image, the value on the first gradient corresponding to the missing image is found, and interpolation can be performed to complete the missing image, thereby obtaining the complete original image.

[0032] S30: adjusting the target parameters according to the second gradient, returning the target parameters according to the test, adjusting the non-target parameters according to the first gradient for testing, and outputting a number of original images until the second gradient traversal is completed to obtain a number of detection images.

[0033] In the specific implementation process, after determining a target parameter, the non-target parameters are adjusted through the first gradient, and the test conditions under the target parameter are fully covered. Then the target parameter is adjusted with the second gradient to achieve full coverage of the test range. After all test conditions under all target parameters are obtained according to the second gradient adjustment, the traversal of the second gradient is completed, and all original images are collected and recorded as detection images.

[0034] S40: Classify and fuse the detection images according to the types of non-target parameters to obtain the target detection images.

[0035] In the specific implementation process, the ultimate goal is to reflect the quality of the set-top box from the image quality, so it is necessary to reduce the number of images and ensure the quality of the final detected image. The classification fusion method is adopted to match the type of non-target parameters for fusion. The test can select a non-target parameter, and the classification is performed based on the type of non-target parameter. The non-target parameter can be determined according to its weight relative to the target parameter; if the test selects more than one non-target parameter, then the classification is based on the inclusion of the non-target parameter. Although a collection of repeated images will be obtained, the image becomes a single image after fusion, which does not affect the detection in terms of quantity, that is: the detection image is classified and fused according to the type of non-target parameter to obtain the target detection image, including: Classify the detection images according to the types of non-target parameters to obtain a detection image set; The images in the same detection image set are fused to obtain the target detection image.

[0036] More specifically: images in the same detection image set are fused to obtain a target detection image, including: Fusing images in the same detection image set to obtain a number of first detection images; According to the second gradient, multi-scale fusion is performed on the first detection image to obtain a target detection image.

[0037] In the specific implementation process, multi-scale image fusion is achieved by fusing images in the same detection image set. After fusion, each gradient value of the target parameter corresponds to a first detection image. These images are then multi-scale fused according to the second gradient to obtain the final target detection image. The multi-scale fusion of images is based on feature extraction, that is, the images in the same detection image set are fused to obtain several first detection images, including: Extract features from images in the same detection image set to obtain feature information; The feature information is fused to obtain a number of first detection images.

[0038] In the specific implementation process, multi-scale image fusion refers to the organic combination of image information of the same scene or object at different scales to generate an image containing richer information. Its principle is based on multi-scale analysis. In the embodiment of the present application, the image can be decomposed and represented at different color reproduction, resolution, contrast, brightness and clarity, and then the feature information at each scale is extracted and fused, which can improve the image quality.

[0039] The methods of multi-scale fusion can be pyramid structure, multi-scale transformation or deep learning methods; the pyramid structure method decomposes the image into multiple sub-images of different scales by constructing a pyramid structure, and extracts the corresponding features, and then uses the fusion algorithm to organically combine these features to generate a fused image, which can provide more comprehensive and accurate feature information and help generate real details and textures; multi-scale transformations such as wavelet transform and curvelet transform can decompose images at different scales, extract feature information at each scale, and then combine these feature information through a fusion algorithm to generate a fused image; the deep learning algorithm can automatically learn feature representations at different scales and realize effective feature fusion by training the deep learning model. This method has powerful feature extraction and fusion capabilities and can significantly improve the quality of the fused image.

[0040] S50: Perform defect detection on the target set-top box based on the target detection image.

[0041] In the specific implementation process, by identifying the target detection image, the defects on the image are identified, such as bright spots, dark spots, missing spots, stripes, etc., to determine whether the set-top box has defects. Of course, you can also use the test software to directly evaluate, run Nokia Monitor Test or other professional test software, and analyze the quality of the image output by the set-top box.

[0042] In this embodiment, defect detection is performed by connecting a set-top box to a test signal source to output an image. First, a target parameter is determined in the test parameters, and other non-target parameters are adjusted according to the gradient to achieve coverage of the test conditions under a target parameter. The target parameter is then adjusted by gradient, and the test process is cyclically performed to achieve full coverage of the test conditions. The output detection image is then classified and fused according to the category of the non-target parameter to obtain a multi-scale fused image under target parameters with different gradients. While realizing comprehensive testing of multi-source indicators, both the reliability of the test method and the quality of the output image are improved, thereby achieving an improvement in the defect detection effect of the set-top box based on image quality testing.

[0043] Refer to the attached Figure 3Based on the same inventive concept as in the above-mentioned embodiment, the embodiment of the present application further provides a defect detection device for a set-top box, comprising: A connection module, the connection module is used to connect the target set-top box to the test signal source; A test module, the test module is used to adjust the non-target parameters according to the first gradient according to the target parameters of the test, and output a plurality of original images; wherein the target parameter is one of color reproduction, resolution, contrast, brightness and clarity, and the non-target parameter is at least one of color reproduction, resolution, contrast, brightness and clarity, and the target parameter and the non-target parameter are not repeated in the same test; A loop module, the loop module is used to adjust the target parameters according to the second gradient, return the target parameters according to the test, adjust the non-target parameters according to the first gradient for testing, output a number of original images, until the second gradient traversal is completed, and obtain a number of detection images; A fusion module is used to classify and fuse the detection images according to the types of non-target parameters to obtain the target detection images; The detection module is used to perform defect detection on the target set-top box based on the target detection image.

[0044] Those skilled in the art should understand that the division of the various modules in the embodiment is only a division of logical functions, and can be fully or partially integrated into one or more actual carriers in actual application, and these modules can be implemented entirely in the form of software called by the processing unit, or entirely in the form of hardware, or in the form of a combination of software and hardware. It should be noted that each module in the defect detection device for the set-top box in this embodiment corresponds one-to-one to each step in the defect detection method for the set-top box in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the defect detection method for the set-top box in the aforementioned embodiment, which will not be repeated here.

[0045] Based on the same inventive concept as in the aforementioned embodiment, an embodiment of the present application further provides a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, a defect detection method for a set-top box as provided in an embodiment of the present application is implemented.

[0046] Based on the same inventive concept as in the above-mentioned embodiment, an embodiment of the present application further provides an electronic device, including a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute a computer program so that the electronic device executes the defect detection method for a set-top box provided in the embodiment of the present application.

[0047] In some embodiments, the computer readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or various devices including one or any combination of the above memories. The computer may be various computing devices including intelligent terminals and servers.

[0048] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0049] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file storing other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0050] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0051] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system 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, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0052] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0053] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a multimedia terminal device (which can be a mobile phone, a computer, a television receiver, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0054] In summary, the present application provides a defect detection method, device, medium and equipment for a set-top box, the method comprising: connecting a target set-top box to a test signal source; adjusting non-target parameters according to a first gradient according to a target parameter of the test for testing, and outputting a plurality of original images; wherein the target parameter is one of color reproduction, resolution, contrast, brightness and clarity, and the non-target parameter is at least one of color reproduction, resolution, contrast, brightness and clarity, and the target parameter and the non-target parameter are not repeated in the same test; adjusting the target parameter according to a second gradient, and returning to the step of adjusting the non-target parameter according to the first gradient for testing according to the target parameter of the test, and outputting a plurality of original images, until the second gradient traversal is completed to obtain a plurality of detection images; classifying and fusing the detection images according to the type of non-target parameters to obtain a target detection image; and performing defect detection on the target set-top box based on the target detection image. The present application performs defect detection by connecting a set-top box to a test signal source output image. First, a target parameter is determined in the test parameters, and other non-target parameters are adjusted according to the gradient to achieve coverage of the test conditions under a target parameter. The target parameter is then adjusted by gradient, and the test process is cyclically performed to achieve full coverage of the test conditions. The output detection image is then classified and fused according to the category of the non-target parameter to obtain a multi-scale fused image under target parameters with different gradients. While realizing comprehensive testing of multi-source indicators, both the reliability of the test method and the quality of the output image are improved, thereby achieving an improvement in the defect detection effect of the set-top box based on image quality testing.

[0055] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A defect detection method for a set-top box, characterized in that: The following steps are involved: Connect the target set-top box to the test signal source; According to the target parameter of the test, the non-target parameter is adjusted according to the first gradient to perform the test, and a plurality of original images are output; wherein the target parameter is one of color reproduction, resolution, contrast, brightness and clarity, and the non-target parameter is at least one of color reproduction, resolution, contrast, brightness and clarity, and the target parameter and the non-target parameter are not repeated in the same test; The target parameters are adjusted according to the second gradient, and the target parameters according to the test are returned, and non-target parameters are adjusted according to the first gradient for testing, and a plurality of original images are outputted until the second gradient traversal is completed to obtain a plurality of detection images; Classifying and fusing the detection images according to the types of the non-target parameters to obtain a target detection image; Based on the target detection image, defect detection is performed on the target set-top box.

2. The defect detection method of a set-top box according to claim 1, characterized in that: The step of classifying and fusing the detection images according to the types of the non-target parameters to obtain the target detection images includes: Classifying the detection images according to the types of the non-target parameters to obtain a detection image set; The images in the same detection image set are fused to obtain a target detection image.

3. The defect detection method of a set-top box according to claim 2, characterized in that: The step of fusing the images in the same detection image set to obtain the target detection image includes: Fusing the images in the same detection image set to obtain a plurality of first detection images; According to the second gradient, multi-scale fusion is performed on the first detection image to obtain a target detection image.

4. The defect detection method of a set-top box according to claim 1, characterized in that: The step of fusing the images in the same detection image set to obtain a plurality of first detection images comprises: Extracting features from the images in the same detection image set to obtain feature information; The feature information is fused to obtain a plurality of first detection images.

5. The defect detection method of a set-top box according to claim 1, characterized in that: The method of testing the target parameters and adjusting the non-target parameters according to the first gradient to output a plurality of original images includes: According to the target parameters of the test, the non-target parameters are adjusted according to the first gradient to perform the test, and a plurality of test images are output; In the case that the test image is missing, the test image is interpolated and completed according to the change of the first gradient, and a plurality of original images are output.

6. The defect detection method of a set-top box according to claim 1, characterized in that: According to the target parameter of the test, the non-target parameter is adjusted according to the first gradient to perform the test, and before outputting a plurality of original images, the method further includes: The gradient number of the first gradient is set according to the weight of the non-target parameter relative to the target parameter.

7. The defect detection method of a set-top box according to claim 1, characterized in that: According to the target parameter of the test, the non-target parameter is adjusted according to the first gradient to perform the test, and before outputting a plurality of original images, the method further includes: The target parameters and the non-target parameters are set according to test requirements.

8. A defect detection device for a set-top box, characterized in that: include: A connection module, the connection module is used to connect the target set-top box to the test signal source; A test module, the test module is used to adjust the non-target parameters according to the first gradient according to the target parameters of the test and to output a plurality of original images; wherein the target parameter is one of color reproduction, resolution, contrast, brightness and clarity, and the non-target parameter is at least one of color reproduction, resolution, contrast, brightness and clarity, and the target parameter and the non-target parameter are not repeated in the same test; A loop module, the loop module is used to adjust the target parameter according to the second gradient, return the target parameter according to the test, adjust the non-target parameter according to the first gradient for testing, output a plurality of original images, until the second gradient traversal is completed, and obtain a plurality of detection images; A fusion module, wherein the fusion module is used to classify and fuse the detection images according to the types of the non-target parameters to obtain a target detection image; A detection module is used to perform defect detection on the target set-top box based on the target detection image.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is loaded and executed by a processor, the defect detection method for a set-top box as described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: comprising a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute the computer program so that the electronic device executes the defect detection method for a set-top box according to any one of claims 1 to 7.

Images