CN111695493A - Method and system for detecting hidden danger of power transmission line - Google Patents
Method and system for detecting hidden danger of power transmission line Download PDFInfo
- Publication number
- CN111695493A CN111695493A CN202010523327.2A CN202010523327A CN111695493A CN 111695493 A CN111695493 A CN 111695493A CN 202010523327 A CN202010523327 A CN 202010523327A CN 111695493 A CN111695493 A CN 111695493A
- Authority
- CN
- China
- Prior art keywords
- detecting
- detection
- transmission line
- hidden danger
- power transmission
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 230000005540 biological transmission Effects 0.000 title claims abstract description 32
- 238000001514 detection method Methods 0.000 claims abstract description 65
- 238000010276 construction Methods 0.000 claims abstract description 17
- 239000000779 smoke Substances 0.000 claims abstract description 6
- 238000012545 processing Methods 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 5
- 238000005457 optimization Methods 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000012806 monitoring device Methods 0.000 description 3
- 241000272525 Anas platyrhynchos Species 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012423 maintenance 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
- 238000012544 monitoring process Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000000007 visual effect 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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/047—Probabilistic or stochastic 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
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention provides a method and a system for detecting hidden troubles of a power transmission line, wherein the method comprises the steps of extracting basic characteristics of a monitored image and searching for a candidate area; detecting construction machinery, a firework target and a ground wire foreign matter on the candidate area respectively; and combining the detection results to obtain a detection conclusion. Based on the fact that the hidden danger targets of the power transmission line channel are various, the recognition performance of three frame regression methods including Cascade R-CNN, fast R-CNN and FCOS to each hidden danger target is tested by combining the characteristics of three targets of construction machinery, smoke, fire and ground wire foreign matters, the reference detection algorithm of the three hidden danger targets is determined, on the basis, the targeted optimization is performed on each algorithm, and compared with the traditional power transmission line channel hidden danger target detection algorithm adopting a single general algorithm, the recognition accuracy of each hidden danger target is improved. On the basis of ensuring the target detection precision, the detection speed is improved, and the hardware power consumption is reduced.
Description
Technical Field
The invention relates to the technical field of comprehensive protection of power transmission lines, in particular to a method and a system for detecting hidden dangers of power transmission lines.
Background
With the large-scale installation of the power transmission line channel monitoring device, the line patrol personnel can obtain the live condition of the power transmission line channel in real time without going out, the work load of going out patrol is reduced, and the operation and maintenance cost of the power transmission line is reduced. 5.5 million sets of transmission line channel visual monitoring devices are installed in the Shandong power grid province, about 240 million pictures are generated every day, in order to reduce the workload of manual reading, the artificial intelligent image recognition technology is applied to automatic recognition of hidden danger outside the transmission line channel, and the number of the pictures needing manual reading is greatly reduced.
The existing detection algorithm for detecting the hidden danger outside the transmission line channel mainly has two defects: all hidden danger types are detected by adopting a single and universal algorithm, the characteristics of different hidden danger types cannot be fully detected, and the accuracy is to be improved; aiming at the same picture, different algorithms are used for processing in a serial mode, so that the comprehensive detection accuracy of the hidden danger is improved to a certain extent, but the hidden danger identification efficiency is greatly reduced.
Disclosure of Invention
The invention provides a method and a system for detecting hidden dangers of a power transmission line, which are used for solving the problems of insufficient identification of the types of the existing hidden dangers and low identification efficiency.
In order to achieve the purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides a method for detecting hidden troubles of a power transmission line, which comprises the following steps:
extracting basic characteristics of the monitored image and searching for a candidate area;
detecting construction machinery, a firework target and a ground wire foreign matter on the candidate area respectively;
and combining the detection results to obtain a detection conclusion.
Further, the base features include edge features, texture features, and entity attribute features.
Further, a Cascade R-CNN frame regression method is adopted for detection of the construction machinery.
Further, the detection of the construction machine by adopting a Cascade R-CNN frame regression method comprises the following steps:
recalculating the anchor point size and the intersection ratio;
and (4) adopting a multi-scale training network model, inputting a test network, and selecting the optimal size of the test set from the training network.
Further, a Fast R-CNN frame regression method optimized by Soft-NMS is adopted to detect the smoke and fire target.
Further, the detection of the firework target by adopting the Fast R-CNN frame regression method optimized by Soft-NMS comprises the following steps:
and generating a plurality of detection frames around the target, and combining the detection frames by adopting a SoftNMS method.
Further, detecting the foreign matters of the conducting wire and the ground wire by adopting an image duck-filling enhanced FCOS frame regression method.
Further, ResNeXt-101ResNeXt is adopted as the extraction network of the basic characteristics.
A second aspect of the present invention provides a system for detecting hidden troubles of a power transmission line, where the system includes:
the image preprocessing unit is used for extracting the basic characteristics of the monitored image and searching for a candidate area;
the detection processing unit is used for respectively detecting the construction machinery, the firework target and the foreign matter of the ground wire on the candidate area;
and the result merging unit is used for merging the detection results to obtain a detection conclusion.
Further, the detection processing unit includes:
the first detection module is used for detecting the construction machinery by adopting a Cascade R-CNN frame regression method;
the second detection module is used for detecting the smoke and fire target by adopting a Fast R-CNN frame regression method optimized by Soft-NMS;
and the third detection module is used for detecting the foreign matters of the ground wires by adopting an image duck-filling enhanced FCOS frame regression method.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
based on the fact that the hidden danger targets of the power transmission line channel are various, the recognition performance of three frame regression methods including Cascade R-CNN, fast R-CNN and FCOS to each hidden danger target is tested by combining the characteristics of three targets of construction machinery, smoke, fire and ground wire foreign matters, the reference detection algorithm of the three hidden danger targets is determined, on the basis, the targeted optimization is performed on each algorithm, and compared with the traditional power transmission line channel hidden danger target detection algorithm adopting a single general algorithm, the recognition accuracy of each hidden danger target is improved. On the basis of ensuring the target detection precision, the detection speed is improved, and the hardware power consumption is reduced.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow diagram of an embodiment of the method of the present invention;
fig. 3 is a schematic diagram of the system of the present invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
As shown in fig. 1 and 2, the invention provides a method for detecting hidden troubles of a power transmission line, which comprises the following steps:
s1, extracting the basic features of the monitored image and searching for a candidate area;
s2, detecting construction machinery, firework targets and foreign matters of the ground wires on the candidate areas respectively;
and S3, combining the detection results to obtain a detection conclusion.
In step S1, the monitoring device is returned to the monitoring image input feature extraction network of the data analysis server; and (3) adopting a ResNeXt-101ResNeXt feature extraction network to extract basic features such as edge features, texture features, entity attributes and the like, and searching a predefined number of candidate areas possibly containing the target.
The method comprises the steps of acquiring images in and around a transmission line channel in real time through a camera device arranged on a transmission line tower, performing targeted preprocessing on the images to remove mean value normalization, and then inputting the images into a feature extraction network.
In step S2, multi-tasking target detection is performed on the candidate regions: detecting the construction machinery by using an anchor point optimized Cascade R-CNN frame regression method; detecting a firework target by using a fast R-CNN frame regression method optimized by Soft-NMS; and detecting the foreign matters of the conducting wire and the ground wire by using an image duck-filling enhanced FCOS frame regression method.
The Cascade R-CNN frame regression method in the embodiment of the invention is optimized as follows: recalculating the anchor point size and the scale, modifying the anchor point size to be [ 32, 64, 128, 224, 384 ], the aspect ratio to be [ 0.4, 0.9, 2.1 ], and the intersection ratio of the recalculated anchor point sizes to be 0.68; and (3) adopting a multi-scale training network model, wherein the input sizes of the multi-scale training network are [600,1000], [800,1333], [1000 and 1600], and the input size of the testing network is selected from three input sizes of the training network to be optimal in the test set.
The optimization of the Faster R-CNN frame regression method by using Soft-NMS is as follows: because pyrotechnic objects do not have distinct contour boundaries and are fragmented, it is easy to generate multiple detection frames, especially also overlapping contained detection frames, near an object using fast R-CNN. The detection frames are combined by adopting a SoftNMS method, so that false detection can be effectively reduced, and the recall ratio of the detection of the firework target is improved.
The optimization of the FCOS frame regression method using image duck-filling enhancement is as follows: targets which can cause false detection are randomly filled in the marked image to manufacture duck forced-feeding images, and when 5 false detection targets of the duck forced-feeding are filled in each image, the recall ratio of the model can be effectively improved, and the false detection is reduced.
The method selects proper algorithms for different types of hidden danger targets and optimizes the algorithms, so that the identification accuracy of the hidden danger targets is improved.
In step S3, three different types of detection results are fused, so that feature extraction is realized by sharing the ResNeXt-101 network, and frame regression is performed by three frame regression networks of Cascade R-CNN, Faster R-CNN and FCOS, so that on the basis of ensuring target detection precision, the detection speed is improved, and the hardware power consumption is reduced.
As shown in fig. 3, the system for detecting hidden troubles of a power transmission line of the present invention includes an image preprocessing unit, a detection processing unit, and a result merging unit.
The image preprocessing unit is used for extracting basic features of the monitored image and searching for a candidate area; the detection processing unit is used for respectively detecting the construction machinery, the firework target and the ground wire foreign matter on the candidate area; and the result merging unit is used for merging the detection results to obtain a detection conclusion.
The detection processing unit comprises a first detection module, a second detection module and a third detection module.
The first detection module detects the construction machinery by adopting a Cascade R-CNN frame regression method; the second detection module adopts a Fast R-CNN frame regression method optimized by Soft-NMS to detect the smoke and fire target; and the third detection module detects the foreign matters of the ground wires by adopting an image duck-filling enhanced FCOS frame regression method.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A method for detecting hidden troubles of a power transmission line is characterized by comprising the following steps:
extracting basic characteristics of the monitored image and searching for a candidate area;
detecting construction machinery, a firework target and a ground wire foreign matter on the candidate area respectively;
and combining the detection results to obtain a detection conclusion.
2. The method for detecting the hidden danger of the power transmission line according to claim 1, wherein the basic features comprise edge features, texture features and entity attribute features.
3. The method for detecting the hidden danger of the power transmission line according to claim 1, wherein a Cascade R-CNN frame regression method is adopted for detecting the construction machinery.
4. The method for detecting the hidden danger of the power transmission line according to claim 3, wherein the detection of the construction machinery by adopting a Cascade R-CNN frame regression method comprises the following steps:
recalculating the anchor point size and the intersection ratio;
and (4) adopting a multi-scale training network model, inputting a test network, and selecting the optimal size of the test set from the training network.
5. The method for detecting the hidden danger of the power transmission line according to claim 1, wherein a Soft-NMS optimized FastR-CNN frame regression method is adopted for detecting the firework target.
6. The method for detecting the hidden danger of the power transmission line according to claim 5, wherein the detection of the firework target by adopting a Fast R-CNN frame regression method optimized by Soft-NMS comprises the following steps:
and generating a plurality of detection frames around the target, and combining the detection frames by adopting a SoftNMS method.
7. The method for detecting the hidden danger of the power transmission line according to claim 1, wherein an image duck-filling enhanced FCOS frame regression method is adopted to detect the foreign matters of the conducting wire and the ground wire.
8. The method for detecting the hidden danger of the power transmission line according to claim 1, wherein ResNeXt-101ResNeXt is adopted as an extraction network of the basic characteristics.
9. A detection system for hidden danger of a power transmission line is characterized by comprising:
the image preprocessing unit is used for extracting the basic characteristics of the monitored image and searching for a candidate area;
the detection processing unit is used for respectively detecting the construction machinery, the firework target and the foreign matter of the ground wire on the candidate area;
and the result merging unit is used for merging the detection results to obtain a detection conclusion.
10. The system for detecting the hidden danger of the power transmission line according to claim 9, wherein the detection processing unit comprises:
the first detection module is used for detecting the construction machinery by adopting a Cascade R-CNN frame regression method;
the second detection module is used for detecting the smoke and fire target by adopting a Fast R-CNN frame regression method optimized by Soft-NMS;
and the third detection module is used for detecting the foreign matters of the ground wires by adopting an image duck-filling enhanced FCOS frame regression method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010523327.2A CN111695493B (en) | 2020-06-10 | 2020-06-10 | Method and system for detecting hidden danger of power transmission line |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010523327.2A CN111695493B (en) | 2020-06-10 | 2020-06-10 | Method and system for detecting hidden danger of power transmission line |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111695493A true CN111695493A (en) | 2020-09-22 |
| CN111695493B CN111695493B (en) | 2023-12-08 |
Family
ID=72480136
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010523327.2A Active CN111695493B (en) | 2020-06-10 | 2020-06-10 | Method and system for detecting hidden danger of power transmission line |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111695493B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112613453A (en) * | 2020-12-29 | 2021-04-06 | 国网山东省电力公司建设公司 | Method and system for checking violation of regulations on construction site of electric power infrastructure |
| CN113610061A (en) * | 2021-09-30 | 2021-11-05 | 国网浙江省电力有限公司电力科学研究院 | Method and system for identifying unstressed conducting wire based on target detection and residual error network |
| CN114024574A (en) * | 2021-12-31 | 2022-02-08 | 山东信通电子股份有限公司 | Hidden danger state identification method and equipment for power transmission line |
| CN114792300A (en) * | 2022-01-27 | 2022-07-26 | 河南大学 | Multi-scale attention X-ray broken needle detection method |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108664997A (en) * | 2018-04-20 | 2018-10-16 | 西南交通大学 | High iron catenary equipotential line defective mode detection method based on cascade Faster R-CNN |
| CN109447018A (en) * | 2018-11-08 | 2019-03-08 | 天津理工大学 | A kind of road environment visual perception method based on improvement Faster R-CNN |
| CN109977921A (en) * | 2019-04-11 | 2019-07-05 | 广东电网有限责任公司 | A kind of transmission line of electricity perils detecting method |
| CN110543986A (en) * | 2019-08-27 | 2019-12-06 | 广东电网有限责任公司 | Intelligent monitoring system and monitoring method for external hidden danger of power transmission line |
| CN110675395A (en) * | 2019-09-29 | 2020-01-10 | 智洋创新科技股份有限公司 | Intelligent on-line monitoring method for power transmission line |
| CN110827251A (en) * | 2019-10-30 | 2020-02-21 | 江苏方天电力技术有限公司 | Power transmission line locking pin defect detection method based on aerial image |
| CN110991264A (en) * | 2019-11-12 | 2020-04-10 | 浙江鸿泉车联网有限公司 | Front vehicle detection method and device |
| CN111178197A (en) * | 2019-12-19 | 2020-05-19 | 华南农业大学 | Mass R-CNN and Soft-NMS fusion based group-fed adherent pig example segmentation method |
-
2020
- 2020-06-10 CN CN202010523327.2A patent/CN111695493B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108664997A (en) * | 2018-04-20 | 2018-10-16 | 西南交通大学 | High iron catenary equipotential line defective mode detection method based on cascade Faster R-CNN |
| CN109447018A (en) * | 2018-11-08 | 2019-03-08 | 天津理工大学 | A kind of road environment visual perception method based on improvement Faster R-CNN |
| CN109977921A (en) * | 2019-04-11 | 2019-07-05 | 广东电网有限责任公司 | A kind of transmission line of electricity perils detecting method |
| CN110543986A (en) * | 2019-08-27 | 2019-12-06 | 广东电网有限责任公司 | Intelligent monitoring system and monitoring method for external hidden danger of power transmission line |
| CN110675395A (en) * | 2019-09-29 | 2020-01-10 | 智洋创新科技股份有限公司 | Intelligent on-line monitoring method for power transmission line |
| CN110827251A (en) * | 2019-10-30 | 2020-02-21 | 江苏方天电力技术有限公司 | Power transmission line locking pin defect detection method based on aerial image |
| CN110991264A (en) * | 2019-11-12 | 2020-04-10 | 浙江鸿泉车联网有限公司 | Front vehicle detection method and device |
| CN111178197A (en) * | 2019-12-19 | 2020-05-19 | 华南农业大学 | Mass R-CNN and Soft-NMS fusion based group-fed adherent pig example segmentation method |
Non-Patent Citations (2)
| Title |
|---|
| 段仲静: "深度学习目标检测方法及主流框架综述" * |
| 陈良琴;唐海城;肖新华;: "基于深度学习的输电线路风险预警识别研究" * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112613453A (en) * | 2020-12-29 | 2021-04-06 | 国网山东省电力公司建设公司 | Method and system for checking violation of regulations on construction site of electric power infrastructure |
| CN113610061A (en) * | 2021-09-30 | 2021-11-05 | 国网浙江省电力有限公司电力科学研究院 | Method and system for identifying unstressed conducting wire based on target detection and residual error network |
| CN114024574A (en) * | 2021-12-31 | 2022-02-08 | 山东信通电子股份有限公司 | Hidden danger state identification method and equipment for power transmission line |
| CN114024574B (en) * | 2021-12-31 | 2022-04-01 | 山东信通电子股份有限公司 | Hidden danger state identification method and equipment for power transmission line |
| CN114792300A (en) * | 2022-01-27 | 2022-07-26 | 河南大学 | Multi-scale attention X-ray broken needle detection method |
| CN114792300B (en) * | 2022-01-27 | 2024-02-20 | 河南大学 | X-ray broken needle detection method based on multi-scale attention |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111695493B (en) | 2023-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111695493B (en) | Method and system for detecting hidden danger of power transmission line | |
| CN111797890A (en) | Method and system for detecting defects of power transmission line equipment | |
| Li et al. | Sewer pipe defect detection via deep learning with local and global feature fusion | |
| CN111582235A (en) | Alarm method, system and equipment for monitoring abnormal events in station in real time | |
| CN111199213B (en) | Method and device for detecting defects of equipment for transformer substation | |
| CN111862013A (en) | Insulator detection method, device and equipment based on deep convolutional neural network | |
| CN113298077A (en) | Transformer substation foreign matter identification and positioning method and device based on deep learning | |
| CN115861173A (en) | Automatic detection system and method for accuracy of optical splitter resources based on digital twin and AI | |
| CN111626104B (en) | Cable hidden trouble point detection method and device based on unmanned aerial vehicle infrared thermal image | |
| CN115100546A (en) | Mobile-based small target defect identification method and system for power equipment | |
| CN112907734B (en) | TEDS fault detection method based on virtual CRH380A model and deep learning | |
| CN114724091A (en) | Method and device for identifying foreign matters on transmission line wire | |
| CN113518205A (en) | Video patrol processing method based on AI analysis | |
| CN112464928B (en) | Digital meter reading identification method, device, equipment and storage medium | |
| CN117114420B (en) | Image recognition-based industrial and trade safety accident risk management and control system and method | |
| Li et al. | Transmission line pin detection based on improved SSD | |
| CN116012785A (en) | Fire level determining method, device, equipment and medium | |
| CN117934367A (en) | Image processing method, device, electronic equipment and storage medium | |
| CN117649374A (en) | Intelligent detection method and device for pin loss of transformer substation | |
| CN117764975A (en) | Image detection method, device, equipment and medium | |
| CN116664487A (en) | Composite insulator defect detection system and method based on deep learning | |
| CN115700819A (en) | Vehicle detection system based on improved YOLOv5 | |
| CN116679754A (en) | Unmanned aerial vehicle-based power fault inspection method and device, medium and electronic equipment | |
| CN114429447A (en) | Transmission line defect detection method and device | |
| CN117218386A (en) | Distribution line image processing method and device, electronic equipment and storage medium |
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 |