CN112906454A - Power transmission channel AI visual monitoring method and device - Google Patents
Power transmission channel AI visual monitoring method and device Download PDFInfo
- Publication number
- CN112906454A CN112906454A CN202011526310.9A CN202011526310A CN112906454A CN 112906454 A CN112906454 A CN 112906454A CN 202011526310 A CN202011526310 A CN 202011526310A CN 112906454 A CN112906454 A CN 112906454A
- Authority
- CN
- China
- Prior art keywords
- image
- hidden danger
- data
- power transmission
- danger object
- 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.)
- Pending
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 50
- 238000012544 monitoring process Methods 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000000007 visual effect Effects 0.000 title claims abstract description 24
- 238000010801 machine learning Methods 0.000 claims abstract description 10
- 238000012549 training Methods 0.000 claims abstract description 10
- 238000012806 monitoring device Methods 0.000 claims abstract description 9
- 238000001514 detection method Methods 0.000 claims description 7
- 210000002569 neuron Anatomy 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000003709 image segmentation Methods 0.000 claims description 4
- 239000013598 vector Substances 0.000 claims description 3
- 238000013527 convolutional neural network Methods 0.000 claims description 2
- 238000012800 visualization Methods 0.000 claims description 2
- 238000007689 inspection Methods 0.000 abstract description 9
- 238000010276 construction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000002567 autonomic effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- 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
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
- G06Q10/063114—Status monitoring or status determination for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Evolutionary Computation (AREA)
- General Business, Economics & Management (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Multimedia (AREA)
- Entrepreneurship & Innovation (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Tourism & Hospitality (AREA)
- Artificial Intelligence (AREA)
- Marketing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a visual monitoring method and a visual monitoring device for a power transmission channel AI, wherein the visual monitoring method for the power transmission channel AI comprises the following steps: the method comprises the steps of obtaining images of a three-dimensional area in a certain range of a power transmission line, receiving the images, inputting the images into a model, and determining whether hidden danger objects exist, wherein the model is obtained by using multiple groups of data through machine learning training, the multiple groups of data comprise first-class data and second-class data, and each group of data in the first-class data comprises: the method comprises the steps of (1) identifying an image of a hidden danger object and a label for marking the image to comprise the hidden danger object; each set of data in the second class of data includes: the image without the hidden danger object and the label for marking the image without the hidden danger object send out alarm and play the image with the hidden danger object under the condition that the hidden danger object exists in the image, and the inspection personnel can be timely reminded by automatically identifying, judging and sending out alarm to the external broken engineering machinery, so that the inspection personnel is not required to observe the display picture of the monitoring system at every moment.
Description
Technical Field
The invention relates to the technical field of power transmission channel monitoring, in particular to a power transmission channel AI visual monitoring method and device.
Background
The transmission line is mainly divided into an overhead line and a buried line. The external force damage to the overhead line is mainly caused by the lifting machinery (such as a crane, a pump truck, a ship crane and the like) for mobile operation, and the external force damage to the buried line is mainly caused by the geotechnical machinery (such as an excavator, a pipe jacking machine, a drilling machine and the like) for mobile operation. A three-dimensional area in a certain range around the constructed power transmission line is called as a power transmission channel. Therefore, whether real-time monitoring possibly causes the outer broken engineering machinery to enter the power transmission channel or not is convenient for patrolmen to timely arrive at the site for management and control.
At present, the monitoring of the power transmission line is mainly that image acquisition equipment is arranged near the power transmission channel to acquire images near the power transmission channel and upload the images to a monitoring system, the monitoring system displays the images for inspection by inspection personnel, but the monitoring mode needs the inspection personnel to stare at a display interface of the monitoring system in real time, so that the moving range of the inspection personnel is limited, because if the sight of the inspection personnel leaves the display interface monitored by the monitoring system and the engineering machinery which is broken at the outside just enters the power transmission channel in the period of time, the inspection personnel cannot find and react in time, so that the power transmission channel is damaged, and the loss is caused.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above and/or other problems occurring in the conventional power transmission path monitoring.
Therefore, the invention aims to provide a visual monitoring method and device for an AI (electric power transmission) channel, which can timely remind patrolmen by automatically identifying, judging and alarming the broken engineering machinery and do not need to observe the display picture of a monitoring system every moment.
To solve the above technical problem, according to an aspect of the present invention, the present invention provides the following technical solutions:
a visual monitoring method for a power transmission channel AI comprises the following steps:
acquiring an image of a three-dimensional area in a certain range of the power transmission line;
receiving an image and inputting the image into a model to determine whether a hidden danger object exists, wherein the model is obtained by using multiple groups of data through machine learning training, the multiple groups of data comprise first-class data and second-class data, and each group of data in the first-class data comprises: the method comprises the steps of (1) identifying an image of a hidden danger object and a label for marking the image to comprise the hidden danger object; each set of data in the second class of data includes: the method comprises the steps of obtaining an image which does not contain the hidden danger object and a label for identifying that the image does not contain the hidden danger object;
and under the condition that the hidden danger object exists in the image, giving an alarm and playing the image containing the hidden danger object.
As a preferred embodiment of the power transmission channel AI visual monitoring method according to the present invention, the specific steps of obtaining an image and inputting the image into a model to determine whether there is a hidden danger object are as follows:
receiving an image;
dividing the received image into a plurality of consecutive short images according to a time axis;
a plurality of consecutive short images are input into the model to determine whether a hidden object is present.
As a preferred embodiment of the power transmission channel AI visual monitoring method according to the present invention, when there is a hidden danger object in the image, the specific steps of sending an alarm and playing the image containing the hidden danger object are as follows: and under the condition that the hidden trouble object exists in the short image, giving an alarm, and playing the short image containing the hidden trouble object.
As a preferred embodiment of the power transmission channel AI visual monitoring method according to the present invention, the specific steps of inputting the image into the model to determine whether there is a hidden danger object are as follows:
inputting the short image into an input layer, and butting the first convolution layer;
the first convolution layer performs convolution operation on an image in an input short image to obtain three feature maps, wherein each feature map corresponds to a convolution kernel, and the feature quantity generated by the convolution operation can be freely set;
the first downsampling layer downsamples the input three feature maps to obtain three feature maps which are half of the original feature map;
the second convolution layer performs convolution operation on the three feature maps obtained through the downsampling operation of the first downsampling layer to obtain five feature maps;
the second lower convolution layer carries out downsampling operation on the five feature maps to obtain five feature maps which are one of the original five feature maps;
and after each neuron of the full-connection layer is fully connected with each neuron of the five feature maps of the previous layer, the output layer calculates the fully-connected feature vectors to obtain a classification prediction result.
A visual monitoring device of transmission channel AI, comprising:
the image acquisition unit is used for acquiring an image of a three-dimensional area in a certain range of the power transmission line;
the image processing unit is used for receiving images and inputting the images into a model to determine whether a hidden danger object exists, wherein the model is obtained by using multiple groups of data through machine learning training, the multiple groups of data comprise first-class data and second-class data, and each group of data in the first-class data comprises: the method comprises the steps of (1) identifying an image of a hidden danger object and a label for marking the image to comprise the hidden danger object; each set of data in the second class of data includes: the method comprises the steps of obtaining an image which does not contain the hidden danger object and a label for identifying that the image does not contain the hidden danger object;
and the alarm playing unit is used for sending an alarm and playing the image containing the hidden trouble object under the condition that the hidden trouble object exists in the image.
As a preferable aspect of the power transmission channel AI visual monitoring apparatus according to the present invention, the image processing unit includes:
the receiving module is used for receiving and receiving the images;
the image segmentation module is used for segmenting the received image into a plurality of consecutive short images according to a time axis;
and the image detection module is used for inputting a plurality of consecutive short images into the model to determine whether a hidden danger object exists.
As a preferable aspect of the power transmission channel AI visual monitoring apparatus according to the present invention, the alarm playing unit includes:
the alarm module is used for giving an alarm under the condition that a hidden danger object exists in the short image;
and the playing module plays the short images containing the hidden danger objects under the condition that the hidden danger objects exist in the short images.
As a preferable scheme of the power transmission channel AI visual monitoring device, a convolutional neural network algorithm is built in the image detection module.
Compared with the prior art, the invention has the beneficial effects that: the device carries out machine learning training in advance through the image of the engineering machine that will break outward and obtains the model storage that corresponds, when the device carries out image monitoring near the transmission channel, the device contrast in the image input that the terminal that the image was gathered obtains the model that machine learning training, whether have the engineering machine that has hidden danger in the discernment image that can be autonomic, and independently send out the police dispatch newspaper, compare in traditional transmission channel collection equipment, can external broken engineering machine automatic identification, judge and automatic alarm of sending, timely warning inspection personnel, the display picture of the observation monitored control system that need not inspect personnel every moment. Great convenience is brought to the patrol personnel.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise. Wherein:
fig. 1 is a flow chart of a power transmission channel AI visualization monitoring method provided by the present invention;
fig. 2 is a system block diagram of a power transmission channel AI visual monitoring device provided by the invention;
fig. 3 is a schematic diagram of a deep convolutional network structure provided in the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Next, the present invention will be described in detail with reference to the drawings, wherein for convenience of illustration, the cross-sectional view of the device structure is not enlarged partially according to the general scale, and the drawings are only examples, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The invention provides a visual monitoring method and a visual monitoring device for an AI (electric power transmission) channel, which can remind patrolmen in time by automatically identifying, judging and alarming externally broken engineering machinery, and do not need to observe display pictures of a monitoring system every moment, thereby bringing great convenience to the patrolmen.
Embodiment mode 1
Referring to fig. 1, the steps of the visualized monitoring method for the power transmission channel AI are as follows:
s10: in the embodiment, a plurality of CCD image sensors can be sequentially arranged in the vicinity of the power transmission line at equal intervals to acquire the images of the three-dimensional area in the certain range of the power transmission line;
s20 receiving an image and inputting the image into a model to determine whether there is a hidden danger object, where the model is obtained by machine learning training using multiple sets of data, the multiple sets of data include a first type of data and a second type of data, and each set of data in the first type of data includes: the method comprises the steps of (1) identifying an image of a hidden danger object and a label for marking the image to comprise the hidden danger object; each set of data in the second class of data includes: the system comprises an image without hidden danger objects and a label for identifying that the image does not contain hidden danger objects. Specifically, receiving an image; dividing the received image into a plurality of consecutive short images according to a time axis; a plurality of consecutive short images are input into the model to determine whether a hidden object is present. With reference to fig. 3, the specific steps of inputting the image into the model to determine whether there is a hidden danger object are as follows:
inputting the short image into an input layer, and butting the first convolution layer;
the first convolution layer performs convolution operation on an image in an input short image to obtain three feature maps, wherein each feature map corresponds to a convolution kernel, and the feature quantity generated by the convolution operation can be freely set;
the first downsampling layer downsamples the input three feature maps to obtain three feature maps which are half of the original feature map;
the second convolution layer performs convolution operation on the three feature maps obtained through the downsampling operation of the first downsampling layer to obtain five feature maps;
the second lower convolution layer carries out downsampling operation on the five feature maps to obtain five feature maps which are one of the original five feature maps;
and after each neuron of the full-connection layer is fully connected with each neuron of the five feature maps of the previous layer, the output layer calculates the fully-connected feature vectors to obtain a classification prediction result.
S30: and under the condition that the hidden danger object exists in the image, giving an alarm and playing the image containing the hidden danger object. Specifically, when a hidden trouble object exists in the short image, an alarm is given, and the short image containing the hidden trouble object is played.
In the present embodiment, the hidden danger object refers to a construction machine that may cause an external damage to a power transmission channel, for example: crane, tower crane and other construction machines.
Embodiment mode 2
Referring to fig. 2, the visualized monitoring device for the power transmission channel AI includes:
the image acquisition unit is used for acquiring an image of a three-dimensional area in a certain range of the power transmission line;
the image processing unit is used for receiving images and inputting the images into a model to determine whether a hidden danger object exists, wherein the model is obtained by using multiple groups of data through machine learning training, the multiple groups of data comprise first-class data and second-class data, and each group of data in the first-class data comprises: the method comprises the steps of (1) identifying an image of a hidden danger object and a label for marking the image to comprise the hidden danger object; each set of data in the second class of data includes: the method comprises the steps of obtaining an image which does not contain the hidden danger object and a label for identifying that the image does not contain the hidden danger object;
and the alarm playing unit is used for sending an alarm and playing the image containing the hidden trouble object under the condition that the hidden trouble object exists in the image.
The image processing unit comprises a receiving module, an image segmentation module and an image detection module.
The device comprises a receiving module used for receiving a received image, an image segmentation module used for segmenting the received image into a plurality of consecutive short images according to a time axis, and an image detection module used for inputting the plurality of consecutive short images into a model to determine whether a hidden danger object exists.
The alarm playing unit comprises an alarm module and a playing module, wherein the alarm module is used for giving an alarm under the condition that a hidden danger object exists in the short image; the playing module is used for playing the short images containing the hidden trouble objects under the condition that the hidden trouble objects exist in the short images. The image detection module is internally provided with a convolution neural network algorithm.
The device inputs the image acquired by the image acquisition terminal into the model acquired by the machine learning training for comparison when the device monitors the image near the power transmission channel, can autonomously identify whether the engineering machinery with hidden danger exists in the image and autonomously send out an alarm. Great convenience is brought to the patrol personnel.
While the invention has been described above with reference to an embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the various features of the disclosed embodiments of the invention may be used in any combination, provided that no structural conflict exists, and the combinations are not exhaustively described in this specification merely for the sake of brevity and resource conservation. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (8)
1. A visual monitoring method for a power transmission channel AI is characterized by comprising the following steps:
acquiring an image of a three-dimensional area in a certain range of the power transmission line;
receiving an image and inputting the image into a model to determine whether a hidden danger object exists, wherein the model is obtained by using multiple groups of data through machine learning training, the multiple groups of data comprise first-class data and second-class data, and each group of data in the first-class data comprises: the method comprises the steps of (1) identifying an image of a hidden danger object and a label for marking the image to comprise the hidden danger object; each set of data in the second class of data includes: the method comprises the steps of obtaining an image which does not contain the hidden danger object and a label for identifying that the image does not contain the hidden danger object;
and under the condition that the hidden danger object exists in the image, giving an alarm and playing the image containing the hidden danger object.
2. The visual monitoring method for the AI of the power transmission channel according to claim 1, characterized in that the specific steps of obtaining the image and inputting the image into the model to determine whether there is a hidden danger object are as follows:
receiving an image;
dividing the received image into a plurality of consecutive short images according to a time axis;
a plurality of consecutive short images are input into the model to determine whether a hidden object is present.
3. The visual monitoring method for the AI of the power transmission channel according to claim 2, characterized in that, when there is a hidden danger object in the image, the specific steps of sending an alarm and playing the image containing the hidden danger object are as follows: and under the condition that the hidden trouble object exists in the short image, giving an alarm, and playing the short image containing the hidden trouble object.
4. The method for visually monitoring the AI of the power transmission channel according to claim 2 or 3, wherein the specific steps of inputting the image into the model to determine whether there is a hidden danger object are as follows:
inputting the short image into an input layer, and butting the first convolution layer;
the first convolution layer performs convolution operation on an image in an input short image to obtain three feature maps, wherein each feature map corresponds to a convolution kernel, and the feature quantity generated by the convolution operation can be freely set;
the first downsampling layer downsamples the input three feature maps to obtain three feature maps which are half of the original feature map;
the second convolution layer performs convolution operation on the three feature maps obtained through the downsampling operation of the first downsampling layer to obtain five feature maps;
the second lower convolution layer carries out downsampling operation on the five feature maps to obtain five feature maps which are one of the original five feature maps;
and after each neuron of the full-connection layer is fully connected with each neuron of the five feature maps of the previous layer, the output layer calculates the fully-connected feature vectors to obtain a classification prediction result.
5. An apparatus for performing a power transmission channel AI visual monitoring method according to claim 1, characterized in that it comprises:
the image acquisition unit is used for acquiring an image of a three-dimensional area in a certain range of the power transmission line;
the image processing unit is used for receiving images and inputting the images into a model to determine whether a hidden danger object exists, wherein the model is obtained by using multiple groups of data through machine learning training, the multiple groups of data comprise first-class data and second-class data, and each group of data in the first-class data comprises: the method comprises the steps of (1) identifying an image of a hidden danger object and a label for marking the image to comprise the hidden danger object; each set of data in the second class of data includes: the method comprises the steps of obtaining an image which does not contain the hidden danger object and a label for identifying that the image does not contain the hidden danger object;
and the alarm playing unit is used for sending an alarm and playing the image containing the hidden trouble object under the condition that the hidden trouble object exists in the image.
6. The visual monitoring device for power transmission channel AI according to claim 5, characterized in that the image processing unit comprises:
the receiving module is used for receiving and receiving the images;
the image segmentation module is used for segmenting the received image into a plurality of consecutive short images according to a time axis;
and the image detection module is used for inputting a plurality of consecutive short images into the model to determine whether a hidden danger object exists.
7. The visualization monitoring device for an AI power transmission channel according to claim 6, characterized in that said alarm playback unit comprises:
the alarm module is used for giving an alarm under the condition that a hidden danger object exists in the short image;
and the playing module plays the short images containing the hidden danger objects under the condition that the hidden danger objects exist in the short images.
8. The visual monitoring device for the AI of the power transmission channel according to claim 6 or 7, characterized in that the image detection module is provided with a convolutional neural network algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011526310.9A CN112906454A (en) | 2020-12-22 | 2020-12-22 | Power transmission channel AI visual monitoring method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011526310.9A CN112906454A (en) | 2020-12-22 | 2020-12-22 | Power transmission channel AI visual monitoring method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112906454A true CN112906454A (en) | 2021-06-04 |
Family
ID=76111592
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011526310.9A Pending CN112906454A (en) | 2020-12-22 | 2020-12-22 | Power transmission channel AI visual monitoring method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112906454A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114004519A (en) * | 2021-11-05 | 2022-02-01 | 国网山东省电力公司青岛供电公司 | Power distribution visualization system and method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107729807A (en) * | 2017-09-05 | 2018-02-23 | 南京理工大学 | Integrated external force damage prevention target identification and intelligent early-warning system |
| JP2018074757A (en) * | 2016-10-28 | 2018-05-10 | 株式会社東芝 | Patrol inspection system, information processing apparatus, and patrol inspection control program |
| KR101897923B1 (en) * | 2017-09-20 | 2018-10-31 | 한국전력공사 | System for providing identify target object based on neural network learning model, method thereof, and computer readable storage having the same |
| CN110705414A (en) * | 2019-09-24 | 2020-01-17 | 智洋创新科技股份有限公司 | Power transmission line construction machinery hidden danger detection method based on deep learning |
| CN111401437A (en) * | 2020-03-13 | 2020-07-10 | 智洋创新科技股份有限公司 | Deep learning-based power transmission channel hidden danger early warning grade analysis method |
| CN111444809A (en) * | 2020-03-23 | 2020-07-24 | 华南理工大学 | Power transmission line abnormal target detection method based on improved YO L Ov3 |
-
2020
- 2020-12-22 CN CN202011526310.9A patent/CN112906454A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2018074757A (en) * | 2016-10-28 | 2018-05-10 | 株式会社東芝 | Patrol inspection system, information processing apparatus, and patrol inspection control program |
| CN107729807A (en) * | 2017-09-05 | 2018-02-23 | 南京理工大学 | Integrated external force damage prevention target identification and intelligent early-warning system |
| KR101897923B1 (en) * | 2017-09-20 | 2018-10-31 | 한국전력공사 | System for providing identify target object based on neural network learning model, method thereof, and computer readable storage having the same |
| CN110705414A (en) * | 2019-09-24 | 2020-01-17 | 智洋创新科技股份有限公司 | Power transmission line construction machinery hidden danger detection method based on deep learning |
| CN111401437A (en) * | 2020-03-13 | 2020-07-10 | 智洋创新科技股份有限公司 | Deep learning-based power transmission channel hidden danger early warning grade analysis method |
| CN111444809A (en) * | 2020-03-23 | 2020-07-24 | 华南理工大学 | Power transmission line abnormal target detection method based on improved YO L Ov3 |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114004519A (en) * | 2021-11-05 | 2022-02-01 | 国网山东省电力公司青岛供电公司 | Power distribution visualization system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104535356B (en) | A kind of roller steel wire rope rope fault monitoring method and system based on machine vision | |
| CN107168294B (en) | Unmanned inspection monitoring method for thermal power water system equipment | |
| CN102759347B (en) | Online in-process quality control device and method for high-speed rail contact networks and composed high-speed rail contact network detection system thereof | |
| CN111612018A (en) | Underground pipe gallery monitoring method and system based on multi-source heterogeneous data fusion | |
| CN115373403B (en) | Inspection service system for construction machinery equipment | |
| CN111008561A (en) | Livestock quantity determination method, terminal and computer storage medium | |
| CN110887462A (en) | Unmanned aerial vehicle inspection method and system and power transmission line inspection method and system | |
| CN114565845A (en) | Intelligent inspection system for underground tunnel | |
| CN112906454A (en) | Power transmission channel AI visual monitoring method and device | |
| CN115082813A (en) | Detection method, unmanned aerial vehicle, detection system and medium | |
| CN109614873A (en) | Train safety sign detection method, server and storage medium neural network based | |
| CN110411686B (en) | Bridge static and dynamic image holographic property health monitoring and diagnosis method and system | |
| CN204405562U (en) | Strong magnetic-online nondestructive inspection system of image associating wire rope | |
| CN114613540B (en) | Cable anti-external-damage monitoring method, system, device and storage medium | |
| CN116151557A (en) | Visual management method and device for inspection equipment and data cockpit system | |
| CN110017777A (en) | A kind of wire rope lay pitch Real-time and Dynamic Detection appraisal procedure | |
| CN114034972B (en) | Intelligent cable fault determining method and device based on image data | |
| CN110782431B (en) | High-voltage wire icing area detection method based on deep learning | |
| CN115100546A (en) | Mobile-based small target defect identification method and system for power equipment | |
| CN114120109A (en) | Belt longitudinal tearing detection method based on neural network | |
| CN112211793A (en) | Wind turbine generator fault automatic identification method based on video image analysis | |
| CN116425035B (en) | Intelligent analysis and processing method and system for building construction safety based on data analysis | |
| CN115331085B (en) | Bogie spring supporting plate detection method based on deep learning | |
| CN117498225B (en) | Unmanned aerial vehicle intelligent power line inspection system | |
| CN110895315B (en) | Terminal insertion quality monitoring system |
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 |