Power transmission and transformation equipment inspection infrared image super-resolution reconstruction method and system

Abstract

The invention discloses a power transmission and transformation equipment inspection infrared image super-resolution reconstruction method and system, and the method comprises the steps: obtaining at least one power transmission and transformation equipment inspection infrared image, and carrying out the preprocessing of the at least one power transmission and transformation equipment inspection infrared image, and obtaining at least one low-resolution infrared image; constructing a super-resolution reconstruction network based on the dynamic region sensing residual block and variable sub-pixel convolution, and inputting at least one low-resolution infrared image as a training set into the super-resolution reconstruction network for training to obtain a target super-resolution reconstruction model; and inputting the obtained real-time inspection infrared image of the power transmission and transformation equipment into the target super-resolution reconstruction model, and outputting by the target super-resolution reconstruction model to obtain a reconstructed image. According to the method, a low-quality and fuzzy infrared image can be improved into a high-resolution and clear infrared image, so that image details are enhanced, and a target detection technology can identify and analyze the heating state of power transmission and transformation equipment more accurately.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of infrared image processing, and particularly relates to a power transmission and transformation equipment inspection infrared image super-resolution reconstruction method and system. BACKGROUND

[0002] Power transmission and transformation equipment is an important part of power transmission, which is used to reliably and economically transmit high-voltage and high-power electric energy at the power generation end to load centers at all levels, and to complete voltage transformation, power distribution and protection control at all levels of transformation nodes. In this process, any loose conductive joint, insulation deterioration or poor heat dissipation condition will first show temperature abnormalities.

[0003] Traditional power transmission and transformation equipment detection mainly relies on manual inspection and regular maintenance. Manual inspection requires experienced technicians to observe with the naked eye and use manual tools for inspection, and the perception speed of subtle changes in equipment temperature is slow, and potential faults can often be detected when the thermal defects have been significantly aggravated. This method is not only inefficient, but also easily affected by subjective factors, making it difficult to detect potential faults in a timely manner. Regular maintenance can prevent problems to some extent, but due to the lack of real-time performance, there is still a large lag, which has made it difficult to meet the stringent requirements of modern power systems for safety and high reliability. SUMMARY

[0004] The present application provides a power transmission and transformation equipment inspection infrared image super-resolution reconstruction method and system, which solves the technical problem of low resolution and poor clarity of infrared images, thereby affecting the fault detection effect of infrared technology on power transmission and transformation equipment.

[0005] In a first aspect, the present application provides a power transmission and transformation equipment inspection infrared image super-resolution reconstruction method, comprising: obtaining at least one power transmission and transformation equipment inspection infrared image, preprocessing the at least one power transmission and transformation equipment inspection infrared image to obtain at least one low-resolution infrared image; constructing a super-resolution reconstruction network based on a dynamic region-aware residual block and a variable sub-pixel convolution, inputting the at least one low-resolution infrared image as a training set into the super-resolution reconstruction network for training to obtain a target super-resolution reconstruction model; inputting the obtained real-time power transmission and transformation equipment inspection infrared image into the target super-resolution reconstruction model, and outputting a reconstructed image from the target super-resolution reconstruction model.

[0006] In a second aspect, the present application provides a power transmission and transformation equipment inspection infrared image super-resolution reconstruction system, comprising: an acquisition module configured to obtain at least one power transmission and transformation equipment inspection infrared image, preprocess the at least one power transmission and transformation equipment inspection infrared image to obtain at least one low-resolution infrared image; The construction module is configured to construct a super-resolution reconstruction network based on a dynamic region-aware residual block and a variability sub-pixel convolution, and input the at least one low-resolution infrared image as a training set into the super-resolution reconstruction network for training to obtain a target super-resolution reconstruction model. The output module is configured to input the obtained real-time power transmission and transformation equipment inspection infrared image into the target super-resolution reconstruction model, and the target super-resolution reconstruction model outputs a reconstructed image.

[0007] In a third aspect, an electronic device is provided, which includes at least one processor and a memory connected to the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the power transmission and transformation equipment inspection infrared image super-resolution reconstruction method of any embodiment of the present application.

[0008] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, and the program instructions are executed by a processor to enable the processor to perform the steps of the power transmission and transformation equipment inspection infrared image super-resolution reconstruction method of any embodiment of the present application.

[0009] The power transmission and transformation equipment inspection infrared image super-resolution reconstruction method and system of the present application can improve low-quality and blurred infrared images to high-resolution and clear infrared images through super-resolution reconstruction technology, thereby enhancing image details and enabling target detection technology to more accurately identify and analyze the heating state of power transmission and transformation equipment. The method effectively solves the problem of insufficient infrared image quality in traditional inspection, improves detection accuracy and reliability, and avoids power outages and safety accidents caused by power transmission and transformation equipment failures. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0011] Figure 1 A flowchart of a power transmission and transformation equipment inspection infrared image super-resolution reconstruction method provided by an embodiment of the present application; Figure 2 A structural block diagram of a power transmission and transformation equipment inspection infrared image super-resolution reconstruction system provided by an embodiment of the present application; Figure 3 A structural schematic diagram of an electronic device provided by an embodiment of the present application. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] Please see Figure 1 The flowchart illustrates a method for super-resolution reconstruction of infrared images of power transmission and transformation equipment inspection according to this application.

[0014] like Figure 1 As shown, the method for super-resolution reconstruction of infrared images during power transmission and transformation equipment inspection specifically includes the following steps: Step S101: Obtain an infrared image of at least one power transmission and transformation equipment inspection, and preprocess the infrared image of the at least one power transmission and transformation equipment inspection to obtain at least one low-resolution infrared image.

[0015] In this step, infrared images of power transmission and transformation equipment will be used for inspection. The image is divided into multiple regions. For each region, a different convolution kernel is applied for blurring. The blurred regions are then merged back into the first low-resolution image. Infrared images of power transmission and transformation equipment inspection Perform Fourier transform on the infrared images of power transmission and transformation equipment inspection. Transform from the time domain to the frequency domain, and examine the infrared images of power transmission and transformation equipment in the frequency domain. Downsampling is performed, and the frequency domain image is converted back to the time domain using inverse Fourier transform to obtain a second low-resolution image; The first low-resolution image and the second low-resolution image are fused together to generate an infrared image for the inspection of the power transmission and transformation equipment. The corresponding low-resolution image, wherein the expression for the low-resolution image is: , In the formula, Infrared images for inspecting power transmission and transformation equipment The corresponding low-resolution image, The contribution of the first low-resolution image obtained through region adaptive blurring operation. Let the convolution kernel for the i-th region be of size . or The size of the convolution kernel is adjusted according to the complexity of the region. For the i-th region, represents the i-th region , the i-th convolution kernel is applied to perform a two-dimensional convolution operation, is the number of regions, is a two-dimensional inverse Fourier transform for converting frequency domain information back to the time domain to generate a low-resolution image, is a filter, is a sound suppression nonlinear transformation function, is a second low-resolution image obtained by Fourier transform and downsampling.

[0016] Step S102, based on the dynamic region perception residual block and the deformable sub-pixel convolution, an ultra-resolution reconstruction network is constructed, and the at least one low-resolution infrared image is input into the ultra-resolution reconstruction network as a training set for training, to obtain a target ultra-resolution reconstruction model.

[0017] In this step, the ultra-resolution reconstruction network includes: an input layer; a dynamic region perception residual block connected with the input layer; a deformable sub-pixel convolution layer connected with the dynamic region perception residual block; an output layer connected with the deformable sub-pixel convolution layer; and a discriminator connected with the output layer.

[0018] Specifically, the expression of the loss function of the discriminator in the target ultra-resolution reconstruction model is: , , , , , In the formula, is the loss function of the discriminator, is a feature similarity loss function, is a deformation loss weight, is a texture loss weight, is the total number of feature layers, is a structure loss weight, is a deformation smoothness loss, is a multi-scale texture consistency loss, which measures the texture of the generated image and the real image at different scales, is a spatial consistency loss, which is used to measure the spatial consistency of the generated image and the real image, is the feature map of the generated image at the i-th layer, indicating the feature information extracted by the i-th layer, is the feature map of the real image at the i-th layer, is the feature information extracted by the i-th layer, is the feature information extracted by the i-th layer, feature map of a layer, representing high-level feature information of a real image, is a self-defined feature correlation loss function, representing the comparison of the features extracted from the generated image and the real image at the layer, is the square of L2 norm, calculating the distance and difference between the feature maps, is the feature map of the layer, is the spatial gradient of the offset of the channel at the pixel , used to adjust the convolution sampling position of each pixel, is the texture information of the generated image at the layer scale using the Sobel operator, is the texture information of the real image at the layer scale using the Sobel operator, is the texture information of the generated image at the layer scale, is the scale level index, representing different image spatial scales, is the texture information of the real image at the layer scale, is the super-resolution generated image, is the original high-resolution image, is the gradient of the generated image at the pixel position , is the gradient of the real image at the pixel position , is the summation of each pixel position , is the difference between the generated image and the real image at the pixel position , representing the pixel-level error.

[0019] It should be noted that the dynamic region-aware residual block comprises a region classifier, a convolution kernel generator and a basic convolution layer connected in sequence; The region classifier is used to calculate the probability distribution of each channel, and the expression is as follows: , , , In the formula, is the output of the region classifier, representing the probability of the pixel belonging to the class , represents the summation of 64 feature channels, is the The channel weights are responsible for generating classification scores based on the channel features. For from the first The first layer Feature map transmitted from the channel. For bias terms, Corresponding to pixels The region mask, representing the category. The probability distribution, Let be the classification score of the r-th class at position p; The convolution kernel generator performs global average pooling on each channel of the image and passes the global information to the fully connected layer to generate the corresponding dynamic convolution kernel.

[0020] The deformable pixel convolutional layer contains an offset convolutional layer, a convolutional layer, and a pixel rearrangement layer connected in sequence; The specific execution process of the deformable pixel convolutional layer includes: The offset is generated through an offset convolutional layer, expressed as follows: , , In the formula, For in pixels Place, No. The channel offset, generated by the offset convolutional layer, indicates the degree of offset of each pixel during sampling. The weights are the kernel weights for offset prediction, representing the parameters of the convolution operation that generates the offsets. For the input feature map, For convolution operations, the input feature map is... Convolutional kernel weights with offset prediction Perform element-wise weighted summation to generate the output feature for each position. Channel index representing offset The value ranges from 1 to 18, representing 18 offset channels.

[0021] Step S103: The acquired real-time infrared image of the power transmission and transformation equipment inspection is input into the target super-resolution reconstruction model, and the target super-resolution reconstruction model outputs the reconstructed image.

[0022] In summary, the method of this application, through super-resolution reconstruction technology, can upscale low-quality, blurry infrared images to high-resolution, clear infrared images, thereby enhancing image details and enabling target detection technology to more accurately identify and analyze the heating status of power transmission and transformation equipment. This method effectively solves the problem of insufficient infrared image quality in traditional inspections, improves detection accuracy and reliability, and avoids power outages and safety accidents caused by power transmission and transformation equipment failures.

[0023] Please refer to Figure 2 which shows the structural block diagram of the power transmission and transformation equipment inspection infrared image super-resolution reconstruction system of the present application.

[0024] As Figure 2 shown, the power transmission and transformation equipment inspection infrared image super-resolution reconstruction system 200 comprises an acquisition module 210, a construction module 220 and an output module 230.

[0025] Among them, the acquisition module 210 is configured to acquire at least one power transmission and transformation equipment inspection infrared image, pre-process the at least one power transmission and transformation equipment inspection infrared image to obtain at least one low-resolution infrared image; the construction module 220 is configured to construct a super-resolution reconstruction network based on a dynamic region-aware residual block and a variability sub-pixel convolution, and input the at least one low-resolution infrared image as a training set into the super-resolution reconstruction network for training to obtain a target super-resolution reconstruction model; the output module 230 is configured to input a real-time power transmission and transformation equipment inspection infrared image obtained into the target super-resolution reconstruction model, and the target super-resolution reconstruction model outputs a reconstructed image.

[0026] It should be understood that Figure 2 the modules described in the above detailed description correspond to the respective steps in the methods described with reference to Figure 1 . Thus, the operations and features described above for the methods, and the corresponding technical effects, apply equally to the modules in Figure 2 , and will not be described again here.

[0027] In some other embodiments, the present application also provides a computer readable storage medium having a computer program stored thereon, wherein the program instructions are executed by a processor to make the processor execute the power transmission and transformation equipment inspection infrared image super-resolution reconstruction method in any of the above method embodiments. As an implementation form, the computer readable storage medium of the present application stores computer executable instructions, which are configured to: acquire at least one power transmission and transformation equipment inspection infrared image, pre-process the at least one power transmission and transformation equipment inspection infrared image to obtain at least one low-resolution infrared image; construct a super-resolution reconstruction network based on a dynamic region-aware residual block and a variability sub-pixel convolution, and input the at least one low-resolution infrared image as a training set into the super-resolution reconstruction network for training to obtain a target super-resolution reconstruction model; input a real-time power transmission and transformation equipment inspection infrared image obtained into the target super-resolution reconstruction model, and the target super-resolution reconstruction model outputs a reconstructed image.

[0028] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the infrared image super-resolution reconstruction system for power transmission and transformation equipment inspection. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely configured relative to a processor, which can be connected to the infrared image super-resolution reconstruction system for power transmission and transformation equipment inspection via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0029] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the infrared image super-resolution reconstruction method for power transmission and transformation equipment inspection described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the infrared image super-resolution reconstruction system for power transmission and transformation equipment inspection. The output device 340 may include a display screen or other display device.

[0030] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0031] In one implementation, the above-described electronic device is used in a super-resolution reconstruction system for infrared images of power transmission and transformation equipment inspection, serving as a client, and includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Acquire an infrared image of at least one power transmission and transformation equipment during inspection, and preprocess the infrared image of the at least one power transmission and transformation equipment to obtain at least one low-resolution infrared image; An ultra-resolution reconstruction network is constructed based on a dynamic region perception residual block and a variable sub-pixel convolution, and the at least one low-resolution infrared image is input into the ultra-resolution reconstruction network as a training set for training, to obtain a target ultra-resolution reconstruction model; The obtained real-time power transmission and transformation equipment inspection infrared image is input into the target ultra-resolution reconstruction model, and the target ultra-resolution reconstruction model outputs a reconstructed image.

[0032] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.

[0033] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for super-resolution reconstruction of infrared images from power transmission and transformation equipment inspection, characterized in that, include: Acquire an infrared image of at least one power transmission and transformation equipment during inspection, and preprocess the infrared image of the at least one power transmission and transformation equipment to obtain at least one low-resolution infrared image; A super-resolution reconstruction network is constructed based on dynamic region-aware residual blocks and variable sub-pixel convolution, and the at least one low-resolution infrared image is used as a training set to be input into the super-resolution reconstruction network for training, thereby obtaining the target super-resolution reconstruction model. The acquired real-time infrared images of power transmission and transformation equipment inspection are input into the target super-resolution reconstruction model, and the target super-resolution reconstruction model outputs the reconstructed image.

2. The method for super-resolution reconstruction of infrared images for power transmission and transformation equipment inspection according to claim 1, characterized in that, The preprocessing of the infrared images from the inspection of the at least one power transmission and transformation equipment to obtain at least one low-resolution infrared image includes: Infrared images of power transmission and transformation equipment inspection The image is divided into multiple regions. For each region, a different convolution kernel is applied for blurring. The blurred regions are then merged back into the first low-resolution image. Infrared images of power transmission and transformation equipment inspection Perform Fourier transform on the infrared images of power transmission and transformation equipment inspection. Transforming from the time domain to the frequency domain, and examining infrared images of power transmission and transformation equipment in the frequency domain. Downsampling is performed, and the frequency domain image is converted back to the time domain using inverse Fourier transform to obtain a second low-resolution image; The first low-resolution image and the second low-resolution image are fused together to generate an infrared image for the inspection of the power transmission and transformation equipment. The corresponding low-resolution image, wherein the expression for the low-resolution image is: ， In the formula, Infrared images for inspecting power transmission and transformation equipment The corresponding low-resolution image, The contribution of the first low-resolution image obtained through region adaptive blurring operation. Let the convolution kernel for the i-th region be of size . or The size of the convolution kernel is adjusted according to the complexity of the region. For the i-th region, This indicates that for the i-th region Apply the i-th convolution kernel Perform two-dimensional convolution operations. For the number of regions, This is a two-dimensional inverse Fourier transform, used to convert frequency domain information back to the time domain to generate low-resolution images. For filters, For the nonlinear transformation function of sound suppression, This is the second low-resolution image obtained through Fourier transform and downsampling.

3. The method for super-resolution reconstruction of infrared images of power transmission and transformation equipment according to claim 1, characterized in that, The super-resolution reconstruction network includes: Input layer; Dynamic region-aware residual blocks connected to the input layer; Deformable compressed pixel convolutional layer connected to the dynamic region-aware residual block; The output layer connected to the deformable pixel convolutional layer; A discriminator connected to the output layer.

4. The method for super-resolution reconstruction of infrared images of power transmission and transformation equipment according to claim 3, characterized in that, in, The expression for the loss function of the discriminator in the target super-resolution reconstruction model is as follows: ， ， ， ， ， In the formula, Let the loss function be that of the discriminator. The feature similarity loss function is... As the weight for deformation loss, For texture loss weights, The total number of feature layers. For structural loss weights, For the loss of deformation smoothness, Multi-scale texture consistency loss measures the texture of generated and real images at different scales. Spatial consistency loss is used to measure the spatial consistency between the generated image and the real image. To generate the image in the first The feature map of the layer represents the first layer. Feature information extracted from layers, For the real image in the first The feature map of the layer represents the high-level feature information of the real image. For a custom feature correlation loss function, it represents the sum of the features of the generated image and the real image at the 1st... The features extracted from the layers are compared. Given the square of the L2 norm, calculate the distance and difference between feature maps. For the first The channel offset in pixels The spatial gradient at a given location is used to adjust the convolution sampling position of each pixel. To use the Sobel operator to generate the image at the 1st... Texture information at the layer scale To use the Sobel operator on real images In the Texture information at the layer scale To generate the image in the first Texture information at the layer scale This is a scale-level index, representing different image spatial scales. For the real image in the first Texture information at the layer scale Images generated for super-resolution, For the original high-resolution image, To calculate the pixel position of the generated image gradient at, To calculate the pixel location of the real image gradient at, For each pixel position Summation, To generate an image and real images At pixel position The difference at that location represents a pixel-level error.

5. The method for super-resolution reconstruction of infrared images of power transmission and transformation equipment according to claim 3, characterized in that, The dynamic region-aware residual block contains a region classifier, a convolutional kernel generator, and a basic convolutional layer connected in sequence. The region classifier is used to calculate the probability distribution of each channel, expressed as: ， ， ， In the formula, The output of the region classifier represents the pixel. Category The probability, This indicates summing over 64 feature channels. For the first The channel weights are responsible for generating classification scores based on the channel features. For from the first The first layer Feature map transmitted from the channel. For bias terms, Corresponding to pixels The region mask, representing the category. The probability distribution, Let be the classification score of the r-th class at position p; The convolution kernel generator performs global average pooling on each channel of the image and passes the global information to the fully connected layer to generate the corresponding dynamic convolution kernel.

6. The method for super-resolution reconstruction of infrared images of power transmission and transformation equipment according to claim 3, characterized in that, The deformable subpixel convolutional layer includes an offset convolutional layer, a convolutional layer, and a pixel rearrangement layer connected in sequence. The specific execution process of the deformable pixel convolutional layer includes: The offset is generated through an offset convolutional layer, expressed as follows: ， ， In the formula, For in pixels Place, No. The channel offset, generated by the offset convolutional layer, indicates the degree of offset of each pixel during sampling. The weights are the kernel weights for offset prediction, representing the parameters of the convolution operation that generates the offsets. For the input feature map, For convolution operations, the input feature map is... Convolutional kernel weights with offset prediction Perform element-wise weighted summation to generate the output feature for each position. Channel index representing offset The value ranges from 1 to 18, representing 18 offset channels.

7. A super-resolution reconstruction system for infrared images of power transmission and transformation equipment inspection, characterized in that, include: The acquisition module is configured to acquire at least one infrared image of a power transmission and transformation equipment inspection, and to preprocess the infrared image of the at least one power transmission and transformation equipment inspection to obtain at least one low-resolution infrared image. The construction module is configured to build a super-resolution reconstruction network based on dynamic region-aware residual blocks and variable sub-pixel convolution, and input the at least one low-resolution infrared image as a training set into the super-resolution reconstruction network for training to obtain the target super-resolution reconstruction model. The output module is configured to input the acquired real-time infrared images of power transmission and transformation equipment inspection into the target super-resolution reconstruction model, and the target super-resolution reconstruction model outputs the reconstructed image.

8. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the method described in any one of claims 1 to 6.

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