CN112598073A - Power grid equipment image labeling method, electronic equipment and storage medium - Google Patents
Power grid equipment image labeling method, electronic equipment and storage medium Download PDFInfo
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- CN112598073A CN112598073A CN202011580835.0A CN202011580835A CN112598073A CN 112598073 A CN112598073 A CN 112598073A CN 202011580835 A CN202011580835 A CN 202011580835A CN 112598073 A CN112598073 A CN 112598073A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/40—Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/10—Interfaces, programming languages or software development kits, e.g. for simulating neural networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/94—Hardware or software architectures specially adapted for image or video understanding
Abstract
The invention discloses a power grid equipment image labeling method, electronic equipment and a storage medium, wherein the method comprises the following steps: loading the neural network algorithm model in a containerization manner to obtain a containerized loaded neural network algorithm model; and inputting the image to be labeled into the neural network algorithm model to obtain a labeled image. The invention has higher compatibility and can realize smooth operation on different devices.
Description
Technical Field
The invention relates to the technical field of image recognition, in particular to an image annotation method for power grid equipment, electronic equipment and a storage medium.
Background
In the traditional image labeling mode, after materials are led out, labeling is carried out manually, and the consumed time is long. In the related technology, there is a method of labeling by using a neural network, but the current neural network labeling method has low compatibility and different operation efficiency on different devices.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides the power grid equipment image labeling method, the electronic equipment and the storage medium, which have high compatibility and can realize smooth operation on different equipment.
According to the embodiment of the first aspect of the invention, the power grid equipment image annotation method comprises the following steps:
loading the neural network algorithm model in a containerization manner to obtain a containerized loaded neural network algorithm model;
and inputting the image to be labeled into the neural network algorithm model to obtain a labeled image.
The power grid equipment image annotation method provided by the embodiment of the invention at least has the following beneficial effects: firstly, the neural network algorithm model is loaded in a containerization mode to obtain the containerized loaded neural network algorithm model. And then inputting the image to be labeled into the neural network algorithm model to obtain a labeled image. Because the neural network algorithm model is loaded in a containerized mode, and then the image to be marked is input into the neural network algorithm model loaded in the containerized mode, and image recognition is carried out. When the neural network algorithm model for marking needs to be transferred to other equipment for use, the container can be directly transferred, so that the running environment of the neural network algorithm model for marking in different equipment is the same as the original environment, the compatibility is higher, and smooth running on different equipment can be realized.
According to some embodiments of the present invention, the inputting the image to be labeled into the neural network algorithm model to obtain a labeled image includes:
acquiring the image to be marked transmitted by a communication network;
and inputting the image to be labeled into the neural network algorithm model to obtain a labeled image.
According to some embodiments of the invention, further comprising:
and training the neural network algorithm model.
According to some embodiments of the invention, the training the neural network algorithm model comprises:
acquiring a marked image training set;
and inputting the image training set into the neural network algorithm model for training to obtain the trained neural network algorithm model.
According to some embodiments of the invention, further comprising:
and loading the training environment and the neural network algorithm model to be trained into a container through containerization.
According to some embodiments of the invention, the annotated image is an annotation result file in + json format.
According to some embodiments of the invention, the neural network algorithm model comprises one of: SSD, Yolo V3, Faster-Rcnn, mobileNet.
According to some embodiments of the invention, further comprising:
and transmitting the marked image to a display interface.
An electronic device according to an embodiment of the second aspect of the present invention includes:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the above-described power grid equipment image annotation method.
According to the storage medium of the embodiment of the third aspect of the invention, the storage medium is a computer-readable storage medium, and the computer-readable storage medium stores computer-executable instructions for causing a computer to execute the above-mentioned power grid equipment image annotation method.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic diagram of an electronic device according to an embodiment of the invention;
fig. 2 is a flowchart of an image annotation method for power grid equipment according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
Referring to FIG. 1, the components of the electronic device 100 include, but are not limited to, a memory 110 and a processor 120. The processor 120 is coupled to the memory 110 via a bus 130 and the database 160 is used to store data.
The electronic device 100 also includes an access device 140, the access device 140 enabling the electronic device 100 to communicate via one or more networks 150. Examples of such networks include the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the internet. The access device 1400 may include one or more of any type of network interface, e.g., a Network Interface Card (NIC), wired or wireless, such as an IEEE802.11 Wireless Local Area Network (WLAN) wireless interface, a worldwide interoperability for microwave access (Wi-MAX) interface, an ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a bluetooth interface, a Near Field Communication (NFC) interface, and so forth.
In some embodiments of the invention, the above-mentioned components of the electronic device 100 and other components not shown in fig. 1 may be connected to each other, for example by a bus. It should be understood that the block diagram of the electronic device shown in fig. 1 is for exemplary purposes only and is not intended to limit the scope of the present invention. Those skilled in the art may add or replace other components as desired. The electronic device 100 may be any type of electronic device 100, such as a computer, a smart phone, a smart tablet, and the like.
Wherein the processor 120 may perform the steps of the device status detection method shown in fig. 2. Fig. 2 shows a flowchart of a device status detection method according to an embodiment of the present invention, and referring to fig. 2, includes steps S100 to S300.
Step S100: and carrying out containerization loading on the neural network algorithm model to obtain the containerized loaded neural network algorithm model.
It is understood that training the neural network algorithm model is also included. The training of the neural network algorithm model comprises: acquiring a marked image training set; and inputting the image training set into the neural network algorithm model for training to obtain the trained neural network algorithm model.
For example, the materials continuously collected by the equipment are uniformly sent to the artificial intelligence platform through the issuing of the materials, and are uniformly managed by the artificial intelligence platform. And the artificial intelligence platform abolishes the abnormal data according to the size and type of the collected material to form a final data set to be standardized. After a data set to be marked is formed, the artificial intelligence platform extracts partial data according to a proportion and marks the partial data manually through information such as acquisition equipment, acquisition time, acquisition angles and the like of the data set to be marked according to a balancing principle so as to form a pre-training material containing most conditions, namely an image training set. The pre-training materials marked are loaded by using containerization technology, the training environment and the training algorithm are loaded, then the pre-training materials are transmitted to the container to be trained through the artificial intelligence platform, a preliminary pre-training model is formed through training, the whole process is uniformly managed by the artificial intelligence platform, and manual participation is not needed.
Step S200: and inputting the image to be labeled into the neural network algorithm model to obtain a labeled image.
It can be understood that, the inputting the image to be labeled into the neural network algorithm model to obtain the labeled image includes: acquiring the image to be marked transmitted by a communication network; and inputting the image to be labeled into the neural network algorithm model to obtain a labeled image. The annotated image is an annotation result file in a + json format. The neural network algorithm model comprises one of: SSD, Yolo V3, Faster-Rcnn, mobileNet. And transmitting the marked image to a display interface.
For example, the trained pre-training model is stored in an artificial intelligence platform, the model is loaded and operated through the artificial intelligence platform by a containerization technology, external http service is provided for the request response of the material, the input is the material to be labeled, and the output is the labeling result. And after the artificial intelligence platform obtains the labeling result, forming a labeling result file in a material + json format according to the noun of the input material and the labeling result, and forming uniform labeling data. Through the artificial intelligence platform, all the reverse labeling conditions of the pre-training model can be checked, the labeling results are uniformly displayed on the interface, and the labeling results are wrong or inaccurate, can be directly corrected on the labeling results and are input into the pre-training materials again, so that the pre-training materials are enriched. And marking the correct material of the pre-training data, directly marking and inputting the material into the pre-training material, continuously enriching the pre-training material, and finally completing marking of all data after multiple iterations. The method can be suitable for the deep learning algorithm SSD, the Yolo V3, the Faster-Rcnn, the mobileNet and the like, but is not limited to the methods, and can be used in the field of manual labeling.
Firstly, the neural network algorithm model is loaded in a containerization mode to obtain the containerized loaded neural network algorithm model. And then inputting the image to be labeled into the neural network algorithm model to obtain a labeled image. Because the neural network algorithm model is loaded in a containerized mode, and then the image to be marked is input into the neural network algorithm model loaded in the containerized mode, and image recognition is carried out. When the neural network algorithm model for marking needs to be transferred to other equipment for use, the container can be directly transferred, so that the running environment of the neural network algorithm model for marking in different equipment is the same as the original environment, the compatibility is higher, and smooth running on different equipment can be realized.
The embodiment of the invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used for enabling a computer to execute the power grid equipment image annotation method.
It should be noted that the technical solution of the computer-readable storage medium and the technical solution of the above-mentioned power grid equipment image annotation method belong to the same concept, and details that are not described in detail in the technical solution of the computer-readable storage medium can be referred to the description of the technical solution of the above-mentioned power grid equipment image annotation method.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and alterations to these embodiments may be made without departing from the principles and spirit of the invention, which is encompassed in the scope of the present application.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (10)
1. A power grid equipment image annotation method is characterized by comprising the following steps:
loading the neural network algorithm model in a containerization manner to obtain a containerized loaded neural network algorithm model;
and inputting the image to be labeled into the neural network algorithm model to obtain a labeled image.
2. The power grid equipment image annotation method of claim 1, wherein the inputting the image to be annotated into the neural network algorithm model to obtain an annotated image comprises:
acquiring the image to be marked transmitted by a communication network;
and inputting the image to be labeled into the neural network algorithm model to obtain a labeled image.
3. The power grid equipment image annotation method according to claim 1, further comprising:
and training the neural network algorithm model.
4. The power grid equipment image annotation method of claim 3, wherein said training of said neural network algorithm model comprises:
acquiring a marked image training set;
and inputting the image training set into the neural network algorithm model for training to obtain the trained neural network algorithm model.
5. The power grid equipment image annotation method of claim 4, further comprising:
and loading the training environment and the neural network algorithm model to be trained into a container through containerization.
6. The power grid equipment image annotation method according to claim 1, characterized in that: the annotated image is an annotation result file in a + json format.
7. The power grid equipment image annotation method according to claim 1, characterized in that: the neural network algorithm model comprises one of: SSD, Yolo V3, Faster-Rcnn, mobileNet.
8. The power grid equipment image annotation method according to claim 1, further comprising:
and transmitting the marked image to a display interface.
9. An electronic device, comprising:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the power grid device image annotation method of any one of claims 1 to 8.
10. A storage medium, which is a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions for causing a computer to execute the power grid equipment image annotation method according to any one of claims 1 to 8.
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