CN113947564B - A method and system for image verification of low-voltage metering equipment in the power industry - Google Patents
A method and system for image verification of low-voltage metering equipment in the power industry Download PDFInfo
- Publication number
- CN113947564B CN113947564B CN202111012230.6A CN202111012230A CN113947564B CN 113947564 B CN113947564 B CN 113947564B CN 202111012230 A CN202111012230 A CN 202111012230A CN 113947564 B CN113947564 B CN 113947564B
- Authority
- CN
- China
- Prior art keywords
- image
- detected
- power
- quality
- standard
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/53—Querying
- G06F16/535—Filtering based on additional data, e.g. user or group profiles
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/55—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/5838—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using colour
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/5854—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using shape and object relationship
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30168—Image quality inspection
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Library & Information Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Facsimiles In General (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明公开了一种电力行业低压台区计量设备图像校验方法及系统，包括获取电力行业低压台区计量设备的待检测图像，将获取的图像输入到预先设定的电力图像质量分析模型中，进行图像质量分析，并输出最终的图像质量分析结果，然后判断图像的质量是否符合预设质量标准，若符合预设质量标准，则将待检测图像输入到云端进行识别，检测电力设备是否存在故障，若不符合预设质量检测标准，则重新获取待检测图像，通过在对待检测图像进行识别前，首先对待检测图像的质量进行判断，将低质量的待检测图像筛除，只对高质量的待检测图像进行识别，通过提高待检测图像的质量，使得电力设备的故障识别结果准确率得到了提升。
The present invention discloses an image verification method and system for low-voltage metering equipment in the electric power industry, comprising obtaining an image to be detected of a low-voltage metering equipment in the electric power industry, inputting the obtained image into a preset electric power image quality analysis model, performing image quality analysis, and outputting a final image quality analysis result, and then judging whether the quality of the image meets a preset quality standard. If it meets the preset quality standard, the image to be detected is input into a cloud for recognition to detect whether there is a fault in the electric power equipment. If it does not meet the preset quality detection standard, the image to be detected is re-acquired. Before recognizing the image to be detected, the quality of the image to be detected is first judged, low-quality images to be detected are screened out, and only high-quality images to be detected are recognized. By improving the quality of the image to be detected, the accuracy of the fault recognition result of the electric power equipment is improved.
Description
Technical Field
The invention belongs to the field of power equipment safety, and particularly relates to an image acquisition integrity verification method for low-voltage transformer area metering equipment in the power industry.
Background
With the development of science and technology, safety monitoring and fault analysis of the power equipment are mostly carried out through an intelligent detection system, and the adoption of the intelligent detection system saves powerful human resources, reduces the accident rate in the detection process of the power equipment, and simultaneously greatly improves the detection efficiency of the equipment. However, in the process of detecting equipment by the intelligent detection system, due to the fact that some column quality problems exist in the acquired images, the analysis result is inaccurate, and the problems of power equipment fault missing, false finding and the like are often caused.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a system for checking the image of low-voltage transformer area metering equipment in the power industry, which are used for improving the accuracy of fault identification of the power equipment by detecting the quality of the image before the fault detection of the image.
A first aspect of an embodiment of the present invention provides a method for verifying an image of a low-voltage transformer area metering device in a power industry, where the method includes:
Acquiring an image to be detected of low-voltage station area metering equipment in the power industry;
inputting an image to be detected of low-voltage station metering equipment in the power industry into the power image quality analysis model for quality analysis, and outputting an image quality analysis result;
Judging whether the image to be detected meets a preset quality standard or not based on the image quality analysis result;
and if the quality standard is met, carrying out cloud identification on the image to be detected.
A second aspect of an embodiment of the present invention provides an image verification system for a low-voltage transformer area metering device in a power industry, the system including:
the image acquisition module is used for acquiring an image to be detected of low-voltage transformer area metering equipment in the power industry;
the image analysis module is used for inputting an image to be detected of the low-voltage transformer area metering equipment in the power industry into the power image quality analysis model for quality analysis and outputting an image quality analysis result;
The result detection module is used for judging whether the image to be detected accords with a preset quality standard or not based on the image quality analysis result;
and if the quality standard is met, carrying out cloud identification on the image to be detected.
A third aspect of the embodiment of the present invention provides a terminal, where the terminal includes a processor and a memory, where the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the above-mentioned image verification method for a low-voltage area metering device in a power industry.
A fourth aspect of the embodiments of the present invention provides a computer readable storage medium having at least one program code stored therein, the at least one program code loaded and executed by a processor to implement the above-described image verification method for a low voltage transformer area metering device in an electric power industry.
The image verification method and system for the low-voltage transformer area metering equipment in the power industry have the following beneficial effects:
The invention discloses an image verification method and system for low-voltage area metering equipment in the power industry, comprising the steps of obtaining an image to be detected of the low-voltage area metering equipment in the power industry, inputting the obtained image into a preset power image quality analysis model, carrying out image quality analysis, outputting a final image quality analysis result, judging whether the quality of the image meets a preset quality standard, inputting the image to be detected into a cloud for identification if the quality of the image meets the preset quality standard, detecting whether the power equipment has faults or not, and re-obtaining the image to be detected if the quality of the image to be detected does not meet the preset quality detection standard.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is an overall flow chart of a method for verifying an image of a low-voltage transformer area metering device in the power industry;
FIG. 2 is a flowchart of a screening process for an initial image set;
FIG. 3 is a flow chart of an analysis process of model analysis results.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
The embodiment of the invention provides an image verification method for low-voltage transformer area metering equipment in the power industry, which comprises the following steps:
Acquiring an image to be detected of low-voltage station area metering equipment in the power industry;
inputting an image to be detected of low-voltage area metering equipment in the power industry into a power image quality analysis model for quality analysis, and outputting an image quality analysis result;
Judging whether the image to be detected meets a preset quality standard or not based on an image quality analysis result;
and if the quality standard is met, carrying out cloud identification on the image to be detected.
Referring to fig. 1, in this embodiment, an image to be detected of a low-voltage transformer area metering device in the power industry is firstly obtained through an image acquisition device, the obtained image is input into a preset power image quality analysis model for image quality analysis, a final image quality analysis result is output, then whether the quality of the image meets a preset quality standard is judged, if yes, the image to be detected is input into a cloud for identification, whether a fault exists in the power device is detected, if not, the image to be detected is acquired again, and in this embodiment, before the image to be detected is identified, the quality of the image to be detected is firstly judged, the low-quality image to be detected is screened out, only the high-quality image to be detected is identified, and by improving the quality of the image to be detected, the accuracy of the fault identification result of the power device is improved.
Based on the above method, the above power image quality analysis model includes:
acquiring an initial image set of low-voltage station area metering equipment in the power industry;
screening out error images in the initial image set, and forming a standard image set from the residual images;
inputting the standard image set into a network model for image target detection, and extracting power equipment in the standard image set;
Calculating the number of pixel points occupied by the power equipment and the positions of the pixel points based on the power equipment in the extracted standard image set;
Performing quality analysis on the image based on the number of pixels occupied by the power equipment and the positions of the pixels, and outputting a model analysis result;
Unified reading analysis is carried out on the standard data set by using OpenCV computer vision software, and a reading analysis result is output;
And combining the model training result and the reading analysis result into the final output of the power image quality analysis model.
In this embodiment, the sample image is further read and analyzed by penCV computer vision software, and the analysis result of the model is checked, so that the accuracy of the power image quality analysis model is improved. The method comprises the steps of obtaining an initial image set of low-voltage area metering equipment in the power industry, screening error images in the initial image set, forming a standard image set by residual images, inputting images in the standard image set into a network model for image target detection, extracting power equipment in the standard image set, wherein the network model can determine the type and the position of the power equipment, then calculating the proportion of the power equipment in the image and the position of the power equipment in the image based on the number of pixel points and the position of the pixel points in the area where the power equipment is located, further carrying out quality analysis on the image, outputting a model analysis result, carrying out unified reading analysis on the standard image set through OpenCV computer vision software, outputting a reading analysis result, checking the model analysis result, and taking the checked output result as a final output result of the network model to complete construction of the power image quality analysis model.
Referring to fig. 2, the step of screening the initial image set is as follows, based on the power image quality analysis model:
S1, converting images in an initial image set into images with the same size;
S2, randomly selecting an image from the initial image set, and acquiring the attribute of the selected image as a first attribute group;
S3, randomly selecting an image from the initial image set, acquiring the attribute of the selected image and the attribute group, comparing, if the comparison is passed, dividing the selected image into the attribute group passing the comparison, and if the comparison is not passed, taking the attribute of the selected image as a new attribute group;
S4, repeating the step S3 until all the images in the initial image set are compared;
And S5, selecting the attribute group with the largest number of pictures as a correct image, screening out error images in the rest attribute groups, and finishing the screening of the initial image set.
In this embodiment, image target detection is performed, and the initial image set needs to be screened, so that the error image in the initial image set is deleted. The method comprises the steps of firstly converting all images in an initial image set into the same size, randomly selecting one image from the initial image set, obtaining attributes of the selected image to serve as a first attribute group, sequentially comparing the image with the existing attribute groups, dividing the image into the existing attribute groups if the attributes are the same, dividing the image into new attribute groups if the attributes are not the same, sequentially selecting the image from the initial image set to perform attribute comparison, obtaining a plurality of attribute groups after all the comparison of the images in the initial image set is completed, selecting the attribute group with the largest image number as a standard attribute group, screening out error images in the rest attribute groups, and obtaining the image in the standard attribute group as the standard image set.
Based on the power image quality analysis model, the specific analysis process of the analysis result of the model is as follows:
extracting power equipment in the image by adopting a network model;
if the power equipment in the image is extracted, the pixel number occupied by the power equipment is obtained, and the ratio of the pixel number occupied by the power equipment to the total pixel number of the image is calculated;
if the ratio is within the preset threshold range, representing the image pixel point by a two-dimensional coordinate, and calculating the distance between the power equipment and the image edge;
If the distance between the power equipment and the image edge accords with the preset distance, acquiring the shape of the power equipment described by the edge pixel points of the power equipment, and calculating the similarity between the shape of the power equipment in the image and a preset template;
And if the similarity meets the requirement, outputting a model analysis result.
Referring to fig. 3, in this embodiment, a network model is used to extract power equipment in an image, specifically, an input channel of the network model includes a first branch inlet and a second branch inlet, the image is input into the first branch inlet, the network model identifies a type of the power equipment in the image and outputs a first result, the image is input into the second branch inlet, the network model sets three prediction frames to extract the position of the power equipment and outputs a second result, and the first result and the second result are spliced to extract the power equipment in the image. If the power equipment in the image is extracted, the number of pixel points occupied by the power equipment is obtained, the ratio of the number of pixel points occupied by the power equipment to the number of total pixel points of the image is calculated, the size of the power equipment in the image is determined, if the ratio is within a preset threshold range, the image pixel points are represented by two-position coordinates, the position of the power equipment in the image is obtained, the threshold range is 0.3-0.6 in the embodiment, if the position of the power equipment accords with a preset distance, the edge pixel points of the power equipment describe the shape of the power equipment, the similarity between the shape of the power equipment in the image and the preset template is calculated, if the position of the power equipment accords with the preset distance, the nearest distance between the center point of the power equipment and the image edge is calculated, if the calculated distance L meets L and is more than or equal to 0.5S+0.1M, the preset distance is met, S represents the side length of the power equipment, the similarity between the power equipment and the power equipment is judged to be the same as the image in the preset template, in the embodiment, the similarity between the power equipment and the power and the image can be judged to be the image after the similarity is more than 0.7, the image can be obtained again, and the analysis condition is not met.
Based on the power image quality analysis model, the specific process of performing unified reading analysis on the standard image set by using OpenCV computer vision software is as follows:
storing the standard image set under a unified directory, and numbering sample images in the standard image set according to the sequence;
Sequentially reading sample images based on sample image numbers, performing feature analysis, and classifying the sample images according to analysis results to obtain sample classification sets;
Numbering the sample classification sets, defining the number of sample images of each sample classification set, and stopping putting samples into the sample classification sets when the number of sample images reaches a specified value;
And taking the image features corresponding to the final classification set of the sample image as a reading analysis result.
In this embodiment, unified reading analysis is performed on the standard image set through OpenCV computer vision software, so as to obtain a reading analysis result. The standard image sets are stored under a unified directory, the storage range of the sample images is limited, the standard image sets are conveniently read by OpenCV computer vision software, then the sample images in the standard image sets are numbered according to the sequence, so that analysis results correspond to the sample images one by one, the subsequent statistics of the sample images is facilitated, the sample images are subjected to analysis results to obtain sample classification sets, each sample classification set corresponds to one image feature, the number of the sample images in the sample classification set is specified, when the number of the sample images reaches a specified value, the sample is stopped from being put into the sample classification set, and the image features corresponding to the finally obtained sample classification set are used as reading analysis results.
Based on the method, the image quality analysis result comprises whether the image comprises power equipment, whether the power equipment in the image is complete, the power equipment state in the image and the power equipment type in the image.
Further, the determining whether the image to be detected meets the preset quality standard includes:
Reading a quality analysis result of an image to be detected, and judging whether the image analysis result comprises all information;
if the image analysis result does not include all the image information, re-acquiring the image to be detected;
if the image analysis result comprises all the image information, judging that the image information accords with a preset standard;
if the image information does not accord with the preset standard, re-acquiring the image to be detected;
If the image information meets the preset standard, inputting the image to be detected into a cloud server for fault detection of low-voltage transformer area metering equipment in the power industry, and sending a fault analysis result to the mobile terminal.
In this embodiment, the subsequent operation is determined by determining whether the image to be detected meets the preset quality standard. Specifically, a quality analysis result of an image to be detected is read, and whether the image analysis result comprises all image information is judged, wherein the image quality analysis result comprises whether the image comprises power equipment, whether the power equipment in the image is complete, the power equipment state in the image and the power equipment type in the image. If the quality analysis result of the image to be detected does not contain all the image information, the image to be detected is re-acquired, otherwise, whether the image information of the image to be detected meets the preset standard is judged, in the embodiment, whether the type of the middle power equipment is manually judged, whether the defect exists in the image power equipment or not is judged, and whether the power equipment needs maintenance or not is judged. And if the image information of the image to be detected does not meet the preset standard, re-acquiring the image to be detected, otherwise, inputting the image to be detected into a cloud server for fault detection of low-voltage transformer area metering equipment in the power industry, and sending a fault analysis result to the mobile terminal.
The embodiment of the invention provides an image verification system of low-voltage transformer area metering equipment in the power industry, which comprises the following components:
the image acquisition module is used for acquiring an image to be detected of low-voltage transformer area metering equipment in the power industry;
the image analysis module is used for inputting an image to be detected of the low-voltage transformer area metering equipment in the power industry into the power image quality analysis model for quality analysis and outputting an image quality analysis result;
the result detection module is used for judging whether the image to be detected accords with a preset quality standard or not based on an image quality analysis result;
and if the quality standard is met, carrying out cloud identification on the image to be detected.
The image verification system for low-voltage area metering equipment in the power industry provided by the embodiment and the image verification method embodiment for low-voltage area metering equipment in the power industry provided by the embodiment belong to the same conception, and detailed implementation processes of the image verification system are detailed in the method embodiment and are not repeated here.
The embodiment of the invention provides a terminal, which comprises a processor and a memory, wherein at least one program code is stored in the memory, and the at least one program code is loaded and executed by the processor to realize the image checking method of the low-voltage station metering equipment in the power industry.
The terminal includes at least one processor, memory, a user interface, and at least one network interface. The various components in the terminal are coupled together by a bus system. It will be appreciated that a bus system is used to enable connected communications between these components.
The embodiment of the invention provides a computer readable storage medium, at least one program code is stored in the storage medium, and the at least one program code is loaded and executed by a processor to realize the image verification method of the low-voltage station metering equipment in the power industry.
It will be appreciated that the memory can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. The memory in the embodiment of the invention can store data to support the operation of the terminal. Examples of such data include any computer programs for operating on the terminal, such as an operating system and application programs. The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application may comprise various applications.
The present invention is not limited to the above-described specific embodiments, and various modifications may be made by those skilled in the art without inventive effort from the above-described concepts, and are within the scope of the present invention.
Claims (7)
1. The utility model provides a power industry low voltage district metering equipment image verification method which is characterized in that the method includes:
Acquiring an image to be detected of low-voltage station area metering equipment in the power industry;
inputting an image to be detected of low-voltage area metering equipment in the power industry into a power image quality analysis model for quality analysis, and outputting an image quality analysis result;
Judging whether the image to be detected meets a preset quality standard or not based on the image quality analysis result;
If the quality standard is met, carrying out cloud identification on the image to be detected;
The power image quality analysis model includes:
acquiring an initial image set of low-voltage station area metering equipment in the power industry;
screening out error images in the initial image set, and forming a standard image set from the rest images;
Inputting the standard image set into a network model for image target detection, and extracting power equipment in the standard image set;
Calculating the number of pixel points occupied by the power equipment and the positions of the pixel points based on the power equipment in the extracted standard image set;
Performing quality analysis on the image based on the number of pixels occupied by the power equipment and the positions of the pixels, and outputting a model analysis result;
Carrying out unified reading analysis on the standard data set by using OpenCV computer vision software, and outputting a reading analysis result;
combining the model training result and the reading analysis result into the final output of the power image quality analysis model;
the screening steps of the initial image set are as follows:
s1, converting images in the initial image set into images with the same size;
S2, randomly selecting an image from the initial image set, and acquiring the attribute of the selected image as a first attribute group, wherein the attribute of the image comprises an image name, an image format, an image resolution, an image size and an image color channel;
S3, randomly selecting an image from the initial image set, acquiring the attribute of the selected image and the attribute group, comparing, if the comparison is passed, dividing the selected image into the attribute group passing the comparison, and if the comparison is not passed, taking the attribute of the selected image as a new attribute group;
S4, repeating the step S3 until all the images in the initial image set are compared;
s5, selecting the attribute group with the largest number of pictures as a correct image, screening out error images in the rest attribute groups, and finishing the screening of an initial image set;
the specific analysis process of the model analysis result is as follows:
extracting power equipment in the image by adopting a network model;
if the power equipment in the image is extracted, the pixel number occupied by the power equipment is obtained, and the ratio of the pixel number occupied by the power equipment to the total pixel number of the image is calculated;
If the ratio is within the preset threshold range, representing the image pixel point by two-position coordinates, and acquiring the position of the power equipment in the image;
if the positions of the power equipment and the image edge accord with the preset distance, acquiring the shape of the power equipment described by the edge pixel points of the power equipment, and calculating the similarity between the shape of the power equipment in the image and a preset template;
And if the similarity meets the requirement, outputting a model analysis result.
2. The method according to claim 1, wherein the specific process of performing unified reading analysis on the standard image set by using OpenCV computer vision software is as follows:
storing the standard image set under a unified directory, and numbering sample images in the standard image set according to the sequence;
sequentially reading sample images based on sample image numbers, performing feature analysis, and classifying the sample images according to analysis results to obtain sample classification sets;
Numbering the sample classification sets, defining the number of sample images of each sample classification set, and stopping putting samples into the sample classification sets when the number of sample images reaches a specified value;
And taking the image features corresponding to the final classification set of the sample image as a reading analysis result.
3. The method of claim 1, wherein the image quality analysis results include whether the image includes a power device, whether the power device in the image is complete, the power device status in the image, and the power device type in the image.
4. The method of claim 1, wherein determining whether the image to be detected meets a preset quality criterion comprises:
Reading a quality analysis result of an image to be detected, and judging whether the image analysis result comprises all information;
if the image analysis result does not include all the image information, re-acquiring the image to be detected;
if the image analysis result comprises all the image information, judging that the image information accords with a preset standard;
if the image information does not accord with the preset standard, re-acquiring the image to be detected;
If the image information meets the preset standard, inputting the image to be detected into a cloud server for fault detection of low-voltage transformer area metering equipment in the power industry, and sending a fault analysis result to the mobile terminal.
5. An image verification system for low-voltage transformer area metering equipment in the power industry, which is characterized by comprising:
the image acquisition module is used for acquiring an image to be detected of low-voltage transformer area metering equipment in the power industry;
the image analysis module is used for inputting an image to be detected of the low-voltage transformer area metering equipment in the power industry into the power image quality analysis model for quality analysis and outputting an image quality analysis result;
The result detection module is used for judging whether the image to be detected accords with a preset quality standard or not based on the image quality analysis result;
and if the quality standard is met, carrying out cloud identification on the image to be detected.
6. A terminal comprising a processor and a memory, wherein the memory stores at least one program code, the at least one program code being loaded and executed by the processor to implement a power industry low voltage bay metering device image verification method as claimed in any one of claims 1 to 4.
7. A computer readable storage medium, wherein at least one program code is stored in the storage medium, and the at least one program code is loaded and executed by a processor to implement a method for checking an image of a low-voltage area metering device in a power industry according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111012230.6A CN113947564B (en) | 2021-08-31 | 2021-08-31 | A method and system for image verification of low-voltage metering equipment in the power industry |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111012230.6A CN113947564B (en) | 2021-08-31 | 2021-08-31 | A method and system for image verification of low-voltage metering equipment in the power industry |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113947564A CN113947564A (en) | 2022-01-18 |
| CN113947564B true CN113947564B (en) | 2025-04-15 |
Family
ID=79327755
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111012230.6A Active CN113947564B (en) | 2021-08-31 | 2021-08-31 | A method and system for image verification of low-voltage metering equipment in the power industry |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113947564B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115150929B (en) * | 2022-05-30 | 2023-07-07 | 荣耀终端有限公司 | Method, device and readable storage medium for adjusting antenna power |
| CN118153971B (en) * | 2024-05-11 | 2024-08-27 | 国网信通亿力科技有限责任公司 | Power consumption safety supervision method and system based on image recognition |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106646137A (en) * | 2016-12-28 | 2017-05-10 | 国网通用航空有限公司 | Method of detecting defect of power transmission line, defect detecting device, and defect detecting system |
| CN108829900A (en) * | 2018-07-31 | 2018-11-16 | 成都视观天下科技有限公司 | A kind of Research on face image retrieval based on deep learning, device and terminal |
| CN109241903A (en) * | 2018-08-30 | 2019-01-18 | 平安科技（深圳）有限公司 | Sample data cleaning method, device, computer equipment and storage medium |
| CN112766351A (en) * | 2021-01-12 | 2021-05-07 | 云南电网有限责任公司电力科学研究院 | Image quality evaluation method, system, computer equipment and storage medium |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5605570B2 (en) * | 2008-04-30 | 2014-10-15 | 日本電気株式会社 | Image quality evaluation method, image quality evaluation system, and program |
| US8675957B2 (en) * | 2010-11-18 | 2014-03-18 | Ebay, Inc. | Image quality assessment to merchandise an item |
| CN104809732B (en) * | 2015-05-07 | 2017-06-20 | 山东鲁能智能技术有限公司 | A kind of power equipment appearance method for detecting abnormality compared based on image |
| KR102782302B1 (en) * | 2019-09-20 | 2025-03-17 | 삼성전자주식회사 | Method and device to estimate location |
| CN111079792A (en) * | 2019-11-19 | 2020-04-28 | 全球能源互联网研究院有限公司 | Power equipment identification method and device |
-
2021
- 2021-08-31 CN CN202111012230.6A patent/CN113947564B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106646137A (en) * | 2016-12-28 | 2017-05-10 | 国网通用航空有限公司 | Method of detecting defect of power transmission line, defect detecting device, and defect detecting system |
| CN108829900A (en) * | 2018-07-31 | 2018-11-16 | 成都视观天下科技有限公司 | A kind of Research on face image retrieval based on deep learning, device and terminal |
| CN109241903A (en) * | 2018-08-30 | 2019-01-18 | 平安科技（深圳）有限公司 | Sample data cleaning method, device, computer equipment and storage medium |
| CN112766351A (en) * | 2021-01-12 | 2021-05-07 | 云南电网有限责任公司电力科学研究院 | Image quality evaluation method, system, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113947564A (en) | 2022-01-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US12125193B2 (en) | Method for detecting defect in products and electronic device using method | |
| WO2024208102A1 (en) | Deep-learning-based target detection method for defects of image of inner side of commutator | |
| CN111832657B (en) | Text recognition method, device, computer equipment and storage medium | |
| US12154261B2 (en) | Image defect detection method, electronic device and readable storage medium | |
| CN112668640B (en) | Text image quality evaluation method, device, equipment and medium | |
| CN112446363A (en) | Image splicing and de-duplication method and device based on video frame extraction | |
| CN114387451B (en) | Training method, device and medium for abnormal image detection model | |
| CN113516148B (en) | Image processing method, device, equipment and storage medium based on artificial intelligence | |
| CN113947564B (en) | A method and system for image verification of low-voltage metering equipment in the power industry | |
| CN111539456A (en) | Target identification method and device | |
| CN117011216A (en) | Defect detection method and device, electronic equipment and storage medium | |
| CN114943875A (en) | Visual analysis method for cable element identification | |
| CN117853397A (en) | Image tampering detection and positioning method and system based on multi-level feature learning | |
| CN112883959A (en) | Method, device, equipment and storage medium for detecting integrity of identity card license | |
| CN112052702A (en) | Method and device for identifying two-dimensional code | |
| US11605210B2 (en) | Method for optical character recognition in document subject to shadows, and device employing method | |
| CN116189063B (en) | Key frame optimization method and device for intelligent video monitoring | |
| CN114005019A (en) | A kind of remake image recognition method and related equipment | |
| CN112215222A (en) | License plate recognition method, device, equipment and storage medium | |
| US12367429B2 (en) | Method and device for optimizing deep learning model conversion, and storage medium | |
| CN112308061B (en) | License plate character recognition method and device | |
| CN111931721B (en) | Method and device for detecting color and number of annual inspection label and electronic equipment | |
| CN114373116A (en) | A method, device, equipment and product for screening operation and maintenance on-site images | |
| CN110969640A (en) | Video image segmentation method, terminal device and computer-readable storage medium | |
| CN114187625A (en) | Video detection method based on video source automatic detection technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |