Defect Detection Method, Device, Medium and Equipment for a Set-Top Box

Abstract

Embodiments of the present application disclose a method, device, medium, and equipment for defect detection of a set-top box, which relate to the technical field of image processing. In the present application, defect detection is performed by connecting the set-top box to a test signal source to output an image. First, a target parameter is determined among the test parameters, and other non-target parameters are adjusted according to a gradient to cover the test situation under one target parameter. Then, the target parameter is adjusted by a gradient, and the test process is cycled to achieve a comprehensive coverage of the test situation. Then, the output detection images are classified and fused according to the categories of non-target parameters to obtain multi-scale fused images under different gradients of the target parameter. In the case of realizing comprehensive testing of multi-source indicators, both the reliability of the test method and the quality of the output image are improved, thereby improving the defect detection effect of the set-top box based on image quality testing.

Description

Technical Field

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

[0002] A network TV set-top box is a device similar in shape to a home broadband modem. Through it, the network and the TV can be connected. As long as there is a network cable installed at home and it is in use, a network cable branched from the router can be plugged into this network set-top box to enable on-demand viewing and live streaming. Defect detection during the production process of the set-top box is a crucial link, which directly affects the quality of the final product and the user experience.

[0003] In addition to basic detections such as physical interface detection, radio frequency detection, and function check for defect detection of the set-top box, 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, sound channel and stereo, image quality, Internet TV, etc. Among them, image quality testing is crucial during the production and detection process of the set-top box because it directly affects the user experience of watching TV programs.

[0004] Although multiple test indicators are set in the current test methods, each indicator is independent during the test process. For example, resolution testing and contrast testing are carried out separately. In fact, the influences of various indicators coexist when users watch programs. The multi-source influence makes the single-indicator test method unreliable, thus reducing the defect detection effect of the set-top box. Summary of the Invention

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

[0006] To achieve the above purpose, the technical solutions adopted in the embodiments of this application are as follows:

[0007] In a first aspect, an embodiment of this application provides a method for defect detection of a set-top box, including the following steps:

[0008] Connect the target set-top box to a test signal source;

[0009] According to the target parameter to be tested, adjust the non-target parameters according to the first gradient for testing, and output a number of original images; wherein, the target parameter is one of color restoration, resolution, contrast, brightness, and clarity, and the non-target parameter is at least one of color restoration, resolution, contrast, brightness, and clarity. In the same test, the target parameter and the non-target parameter do not repeat;

[0010] Adjust the target parameter according to the second gradient, and return to the step of adjusting the non-target parameter according to the first gradient for testing according to the target parameter to be tested, and output a number of original images, until the second gradient traversal is completed, and a number of detection images are obtained;

[0011] Classify and fuse the detection images according to the type of non-target parameters to obtain target detection images;

[0012] Based on the target detection images, perform defect detection on the target set-top box.

[0013] In a possible implementation manner of the first aspect, classifying and fusing the detection images according to the type of non-target parameters to obtain target detection images includes:

[0014] Classify the detection images according to the type of non-target parameters to obtain a set of detection images;

[0015] Fuse the images in the same set of detection images to obtain target detection images.

[0016] In a possible implementation manner of the first aspect, fusing the images in the same set of detection images to obtain target detection images includes:

[0017] Fuse the images in the same set of detection images to obtain a number of first detection images;

[0018] According to the second gradient, perform multi-scale fusion on the first detection images to obtain target detection images.

[0019] In a possible implementation manner of the first aspect, fusing the images in the same set of detection images to obtain a number of first detection images includes:

[0020] Extract features from the images in the same set of detection images to obtain feature information;

[0021] Fuse the feature information to obtain a number of first detection images.

[0022] In a possible implementation manner of the first aspect, according to the target parameter to be tested, adjust the non-target parameters according to the first gradient for testing, and output a number of original images, including:

[0023] According to the target parameters of the test, adjust the non-target parameters according to the first gradient for testing, and output a number of test images;

[0024] In the case where the test images are missing, interpolate and complete the test images according to the change of the first gradient, and output a number of original images.

[0025] In a possible implementation manner of the first aspect, before outputting a number of original images according to the target parameters of the test and adjusting the non-target parameters according to the first gradient for testing, the method further includes:

[0026] Set the number of gradients of the first gradient according to the weight of the non-target parameter relative to the target parameter.

[0027] In a possible implementation manner of the first aspect, before outputting a number of original images according to the target parameters of the test and adjusting the non-target parameters according to the first gradient for testing, the method further includes:

[0028] Set the target parameters and non-target parameters according to the test requirements.

[0029] In a second aspect, an embodiment of the present application provides a defect detection device for a set-top box, including:

[0030] A connection module for connecting the target set-top box to a test signal source;

[0031] A test module for testing according to the target parameters of the test and adjusting the non-target parameters according to the first gradient, and outputting a number of original images; wherein, the target parameter is one of color reproducibility, resolution, contrast, brightness, and clarity, and the non-target parameter is at least one of color reproducibility, resolution, contrast, brightness, and clarity, and the target parameter and the non-target parameter do not repeat in the same test;

[0032] A loop module for adjusting the target parameters according to the second gradient, and returning to test according to the target parameters of the test and adjusting the non-target parameters according to the first gradient, and outputting a number of original images until the second gradient traversal is completed to obtain a number of detection images;

[0033] A fusion module for classifying and fusing the detection images according to the type of non-target parameters to obtain a target detection image;

[0034] A detection module for defect detecting the target set-top box based on the target detection image.

[0035] In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which when loaded and executed by a processor, implements the defect detection method for a set-top box provided in any one of the above first aspects.

[0036] In a fourth aspect, an embodiment of the present application provides an electronic device, including a processor and a memory. Among them,

[0037] The memory is used to store a computer program;

[0038] The processor is used to load and execute the computer program so that the electronic device executes the defect detection method of the set-top box provided in any one of the above first aspects.

[0039] Compared with the prior art, the beneficial effects of the present application are:

[0040] A defect detection method, device, medium and device of a set-top box proposed in an embodiment of the present application. The method includes: connecting a target set-top box to a test signal source; according to the tested target parameters, and adjusting non-target parameters according to a first gradient for testing, and outputting a number of original images; where the target parameter is one of color restoration degree, resolution, contrast, brightness, and clarity, and the non-target parameter is at least one of color restoration degree, resolution, contrast, brightness, and clarity. In the same test, the target parameter and the non-target parameter do not repeat; adjusting the target parameter according to a second gradient, and returning to the step of adjusting the non-target parameter according to the tested target parameter and testing according to the first gradient to output a number of original images until the second gradient traversal is completed to obtain a number of detection images; classifying and fusing the detection images according to the type of non-target parameters to obtain a target detection image; based on the target detection image, performing defect detection on the target set-top box. The present application performs defect detection by connecting the set-top box to a test signal source to output images. First, a target parameter is determined among the test parameters, and other non-target parameters are adjusted according to the gradient to cover the test situation under one target parameter. Then, the target parameter is adjusted by the gradient, and the test process is cycled to achieve a comprehensive coverage of the test situation. Then, the output detection images are classified and fused according to the category of non-target parameters to obtain multi-scale fusion images under different gradient target parameters. In the case of realizing the comprehensive test of multi-source indicators, both the reliability of the test method and the quality of the output images are improved, and further the defect detection effect based on the image quality test of the set-top box is improved. Description of the Drawings

[0041] Figure 1 It is a schematic structural diagram of an electronic device for the hardware operating environment involved in an embodiment of the present application;

[0042] Figure 2 It is a schematic flowchart of the defect detection method of the set-top box provided in an embodiment of the present application;

[0043] Figure 3 It is a schematic module diagram of the defect detection device of the set-top box provided in an embodiment of the present application;

[0044] Markings in the figure: 101 - Processor, 102 - Communication bus, 103 - Network interface, 104 - User interface, 105 - Memory. Detailed implementation

[0045] It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0046] Refer to the appendix Figure 1 , appendix Figure 1 is a schematic structural diagram of an electronic device for the hardware operating environment involved in the solution of the embodiment of this 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 screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, 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 (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 (Random Access Memory, RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as at least one disk memory; the processor 101 may be a general-purpose processor, including a central processor, a network processor, etc., or may also be a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0047] Those skilled in the art can understand that the structure shown in the appendix Figure 1 does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange different components.

[0048] As shown in the appendix Figure 1 , 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 for the set-top box.

[0049] In the appendix Figure 1In the electronic device shown, the network interface 103 is mainly used for data communication with a network server; the user interface 104 is mainly used for data interaction with a user; the processor 101 and the memory 105 in this application can be arranged 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 embodiments of this application.

[0050] Referring to the attached Figure 2 , based on the hardware device of the foregoing embodiment, an embodiment of this application provides a method for detecting defects of a set-top box, including the following steps:

[0051] S10: Connect the target set-top box to a test signal source.

[0052] In a specific implementation process, the target set-top box, that is, the set-top box that needs to be subjected to defect detection; the defect detection performed in the embodiments of this application is implemented based on image quality testing, that is, an image is output through image quality testing, and the quality of the image is evaluated, so as to feedback whether there are defects in the set-top box. The test signal source such as a high-definition TV signal, a DVD player, etc., simply put, is to give a signal to the set-top box to make it start normal playback work, and what these test signal sources make it play is different from complex TV programs, and may be simple color images, or some images with simple calibration patterns.

[0053] S20: According to the target parameter to be tested, and adjust the non-target parameters according to the first gradient for testing, and output a plurality of original images; where the target parameter is one of color restoration degree, resolution, contrast, brightness, and clarity, and the non-target parameter is at least one of color restoration degree, resolution, contrast, brightness, and clarity, and the target parameter and the non-target parameter do not repeat in the same test.

[0054] In a specific implementation process, color restoration degree, resolution, contrast, brightness, and clarity are basic indicators of image quality testing. When the target parameter is used as the test index, the remaining non-target parameters are used as the basis for adjusting other indicators to obtain multi-scale features under this test index, which is the reason for setting that the target parameter and the non-target parameter do not repeat in the same test. The setting of the parameters can be determined according to the test requirements, that is: before outputting a plurality of original images according to the target parameter to be tested and adjusting the non-target parameters according to the first gradient for testing, the method further includes:

[0055] Set the target parameter and the non-target parameter according to the test requirements.

[0056] In the specific implementation process, various parameters and their specific values are set according to the test requirements. For example, if the current test is the color restoration test in the image quality test, then the target parameter can be determined as color restoration, and the non-target parameters can be determined as at least one of resolution, contrast, brightness, and clarity. The specific number of non-target parameters can be determined according to the actual requirements.

[0057] The first gradient is a way of adjusting step by step according to a rule. In the embodiments of the present application, for each non-target parameter, there is a corresponding first gradient. Since a test range is set for each parameter, within a relatively fixed range, the number of gradients of the first gradient determines the size of the step value. If the number of gradients is set too small, it is insufficient to support multi-scale feature extraction; if it is too large, it will waste resources in image processing. Therefore, the number of gradients can be determined according to the importance level, that is: before outputting a number of original images by testing according to the target parameter of the test and adjusting the non-target parameters according to the first gradient, the method further includes:

[0058] Set the number of gradients of the first gradient according to the weight of the non-target parameter relative to the target parameter.

[0059] In the specific implementation process, the weight is used to determine the number of settings of the first gradient to ensure that the index data corresponding to the non-target parameter is reliable enough. For example, if the target parameter is clarity, the importance level of color restoration relative to clarity is lower than that of contrast. Therefore, the number of gradients of the first gradient corresponding to contrast is set more than that of color restoration. It should be noted that when adjusting according to the first gradient, it is not necessary to adjust all at the same time. Instead, each is adjusted step by step, so that the detection situation includes the combination of gradient values of each parameter.

[0060] In one embodiment, when testing according to the target parameter of the test and adjusting the non-target parameters according to the first gradient, and outputting a number of original images, it includes:

[0061] Test according to the target parameter of the test and adjust the non-target parameters according to the first gradient to output a number of test images;

[0062] In the case of missing test images, interpolate and complete the test images according to the change of the first gradient to output a number of original images.

[0063] In the specific implementation process, due to the continuous adjustment of parameters during the image quality test, many test images will be output, and there may be a situation where the images output in a certain test are lost. In this case, in order not to delay the detection and recognition, the missing images are complemented by using the change of the first gradient. Applying the principle of linear interpolation to image completion, since the non-target parameters are adjusted according to the first gradient, this rule will be reflected in the change of image pixels. According to this change of the image, the value on the first gradient corresponding to the missing image is found, and then interpolation can be performed to complete the complement, obtain the lost image, and thus obtain a complete original image.

[0064] S30: Adjust the target parameter according to the second gradient, return according to the tested target parameter, and adjust the non-target parameter according to the first gradient for testing, and output several steps of the original images until the second gradient traversal is completed to obtain several detection images.

[0065] In the specific implementation process, after determining a target parameter, the non-target parameter is adjusted through the first gradient, and the test conditions under this target parameter are fully covered. Then, the target parameter is adjusted with the second gradient to achieve a comprehensive coverage of the test range. When all the test conditions under all target parameters are obtained by adjusting according to the second gradient, the traversal of the second gradient is completed, and all the original images are collected and recorded as detection images.

[0066] S40: Classify and fuse the detection images according to the type of non-target parameters to obtain the target detection images.

[0067] In the specific implementation process, the ultimate goal is to reflect the quality of the set-top box from the image quality. Therefore, it is necessary to reduce the number of images and ensure the quality of the finally detected images. The method of classification and fusion is adopted to match the types of non-target parameters for fusion. If one non-target parameter is selected for testing, the classification is carried out according to the type of the non-target parameter, and this non-target parameter can be determined according to its weight relative to the target parameter; if more than one non-target parameter is selected for testing, then the classification is carried out based on the non-target parameters included. In this way, although a set of duplicate images will be obtained, after fusion, the images become single images, which does not affect the detection in terms of quantity. That is: classify and fuse the detection images according to the type of non-target parameters to obtain the target detection images, including:

[0068] Classify the detection images according to the type of non-target parameters to obtain a set of detection images;

[0069] Fuse the images in the same set of detection images to obtain the target detection images.

[0070] More specifically: fuse the images in the same set of detection images to obtain the target detection images, including:

[0071] Fuse the images in the same set of detection images to obtain a number of first detection images;

[0072] Perform multi-scale fusion on the first detection images according to the second gradient to obtain the target detection image.

[0073] In the specific implementation process, by fusing the images in the same set of detection images, the fusion of multi-scale images is realized. After fusion, there is a first detection image corresponding to each gradient value of the target parameter. Then, these images are further subjected to multi-scale fusion 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: fuse the images in the same set of detection images to obtain a number of first detection images, including:

[0074] Extract the feature information from the images in the same set of detection images;

[0075] Fuse the feature information to obtain a number of first detection images.

[0076] 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, so as to generate an image containing richer information. Its principle is based on multi-scale analysis. In the embodiments of the present application, the image can be decomposed and represented at different color restoration degrees, resolutions, contrasts, brightnesses, and sharpnesses, and then the feature information at each scale is extracted and fused, which can improve the image quality.

[0077] The multi-scale fusion method can be a pyramid structure, multi-scale transform, or deep learning method, etc.; the pyramid structure method decomposes the image into multiple sub-images of different scales by constructing a pyramid structure, extracts the corresponding features, and then uses a 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 transforms such as wavelet transform and curvelet transform can decompose the image at different scales, extract the 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 the feature representations at different scales and achieve effective fusion of features by training a deep learning model. This method has strong feature extraction and fusion capabilities and can significantly improve the quality of the fused image.

[0078] S50: Perform defect detection on the target set-top box based on the target detection image.

[0079] In the specific implementation process, defects on the image, such as bright spots, dark spots, missing parts, stripes and other defects, are identified by recognizing the target detection image, and it is determined that there are defect problems with the set-top box. Of course, it is also possible to directly evaluate using test software, run Nokia Monitor Test or other professional test software, and perform quality analysis on the image output by the set-top box.

[0080] In this embodiment, defect detection is performed by connecting the 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 cover the test situation under one target parameter. Then, the target parameter is adjusted by gradient, and the test process is looped to achieve full coverage of the test situation. Then, the output detection images are classified and fused according to the categories of non-target parameters to obtain multi-scale fused images under different gradients of target parameters. In the case of realizing comprehensive testing of multi-source indicators, both the reliability of the test method and the quality of the output image are improved, and further, the defect detection effect of the set-top box based on image quality testing is improved.

[0081] Refer to the attached Figure 3 , based on the same inventive concept as in the foregoing embodiment, the embodiment of the present application further provides a defect detection device for a set-top box, including:

[0082] A connection module, which is used to connect the target set-top box to a test signal source;

[0083] A test module, which is used to perform tests according to the target parameters of the test and adjust the non-target parameters according to the first gradient to output a number of original images; wherein, the target parameter is one of color reproducibility, resolution, contrast, brightness, and clarity, and the non-target parameter is at least one of color reproducibility, resolution, contrast, brightness, and clarity. The target parameter and the non-target parameter do not repeat in the same test;

[0084] A loop module, which is used to adjust the target parameter according to the second gradient, and return to perform tests according to the target parameters of the test and adjust the non-target parameters according to the first gradient to output a number of original images until the second gradient traversal is completed to obtain a number of detection images;

[0085] A fusion module, which is used to classify and fuse the detection images according to the types of non-target parameters to obtain a target detection image;

[0086] A detection module, which is used to perform defect detection on the target set-top box based on the target detection image.

[0087] Those skilled in the art should understand that the division of each module in the embodiments is only a division of logical functions. In actual applications, they can be fully or partially integrated into one or more actual carriers, and these modules can all be implemented in the form of software called by a processing unit, or all be implemented in the form of hardware, or be implemented in the form of a combination of software and hardware. It should be noted that each module in the defect detection device of the set-top box in this embodiment corresponds one by one to each step in the defect detection method of the set-top box in the foregoing embodiment. Therefore, the specific implementation manners of this embodiment can refer to the implementation manners of the foregoing defect detection method of the set-top box, and will not be elaborated here.

[0088] Based on the same inventive concept as in the foregoing embodiments, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the defect detection method of the set-top box provided in the embodiment of the present application.

[0089] Based on the same inventive concept as in the foregoing embodiments, an embodiment of the present application further provides an electronic device, including a processor and a memory, where

[0090] the memory is used to store a computer program;

[0091] the processor is used to load and execute the computer program so that the electronic device executes the defect detection method of the set-top box provided in the embodiment of the present application.

[0092] 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 disc, or CD-ROM; or it may be various devices including one or any combination of the foregoing memories. The computer may be various computing devices including smart terminals and servers.

[0093] In some embodiments, the 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 being deployed as an independent program or being deployed as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] In summary, a defect detection method, device, medium and equipment for a set-top box provided by the present application, the method includes: connecting a target set-top box to a test signal source; according to the tested target parameter, adjusting the non-target parameter according to the first gradient for testing, and outputting a plurality of original images; wherein, the target parameter is one of color restoration degree, resolution, contrast, brightness and clarity, and the non-target parameter is at least one of color restoration degree, resolution, contrast, brightness and clarity, and the target parameter and the non-target parameter do not repeat in the same test; adjusting the target parameter according to the second gradient, and returning to the step of adjusting the non-target parameter according to the tested target parameter and testing according to the first gradient to output 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 the non-target parameter to obtain a target detection image; based on the target detection image, performing defect detection on the target set-top box. The present application performs defect detection by connecting the set-top box to the test signal source to output images. First, a target parameter is determined among the test parameters, and the other non-target parameters are adjusted according to the gradient to cover the test situation under one target parameter. Then, the target parameter is adjusted by the gradient, and the test process is cycled to achieve a comprehensive coverage of the test situation. Then, the output detection images are classified and fused according to the category of the non-target parameter to obtain multi-scale fusion images under different gradient target parameters. In the case of realizing the comprehensive test of multi-source indicators, both the reliability of the test method and the quality of the output images are improved, and further the defect detection effect based on the image quality test of the set-top box is improved.

[0100] The foregoing are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall 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 the 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 the target detection images; 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; Fusing the images in the same detection image set to obtain a target detection image; 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, performing multi-scale fusion on the first detection image 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 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.

3. 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.

4. 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.

5. 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.

6. 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; the classification and fusion of the detection images according to the types of the non-target parameters to obtain the target detection image includes: Classifying the detection images according to the types of the non-target parameters to obtain a detection image set; Fusing the images in the same detection image set to obtain a target detection image; 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, performing multi-scale fusion on the first detection image 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.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is loaded and executed by the processor, the defect detection method for the set-top box according to any one of claims 1 to 5 is implemented.

8. 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 5.

Images