CN114120164A - Power transmission line pin state detection method and detection system - Google Patents
Power transmission line pin state detection method and detection system Download PDFInfo
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Abstract
The invention provides a method and a system for detecting the state of a pin of a power transmission line, wherein the detection method comprises the following steps: acquiring a power transmission line image containing a positioning catenary supporting device; the positioning catenary supporting device is provided with a plurality of joint components, and each joint component comprises a pin; inputting the power transmission line image into a pre-trained first neural network model to obtain position information of the joint assembly in the power transmission line image; extracting a joint component image from the power transmission line image according to the position information of the joint component in the power transmission line image; inputting the joint component image into a pre-trained second neural network model to obtain the position information of the pin in the joint component image; extracting a pin image from the joint assembly image according to the position information of the pin in the joint assembly image; and inputting the pin image into a pre-trained third neural network model to obtain a pin state result. The invention can improve the detection efficiency and accuracy of the pin state.
Description
Technical Field
The invention relates to the technical field of pin detection of power transmission lines, in particular to a method and a system for detecting the pin state of a power transmission line.
Background
At present, substation inspection is developing towards the direction of no humanization and intellectualization, and the scheme is the product developed by the target, but the existing substation has low detection accuracy rate aiming at the pin defect detection method of the power transmission line, because the image background is complicated, the pin is small in size and is easy to be shielded, and the state result of the pin is difficult to recognize at one time.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings in the prior art and provides a method and a system for detecting the pin state of a power transmission line.
One embodiment of the invention provides a method for detecting the state of a pin of a power transmission line, which comprises the following steps:
acquiring a power transmission line image containing a positioning catenary supporting device; the positioning catenary supporting device is provided with a plurality of joint components, and each joint component comprises a pin;
inputting the power transmission line image into a pre-trained first neural network model to obtain position information of the joint assembly in the power transmission line image;
extracting a joint component image from the power transmission line image according to the position information of the joint component in the power transmission line image;
inputting the joint component image into a pre-trained second neural network model to obtain the position information of the pin in the joint component image;
extracting the pin image from the joint component image according to the position information of the pin in the joint component image;
and inputting the pin image into a pre-trained third neural network model to obtain a state result of the pin.
Compared with the prior art, the method for detecting the state of the pin of the power transmission line comprises the steps of firstly detecting the position information of the joint component in the image of the power transmission line through the first neural network model to extract the image of the joint component, then detecting the position information of the pin in the image of the joint component through the second neural network model to extract the image of the pin, and then detecting the pin in the image of the pin through the third neural network model to obtain the state result of the pin, so that the efficiency and the accuracy of detecting the state of the pin can be improved.
Further, the first neural network model is obtained by training through the following steps:
acquiring a first training sample set; the first training sample set is a plurality of electric transmission line images marked with the joint component positions;
and training an initial first neural network model by using the first training sample set to obtain a trained first neural network model. By training the first neural network, the accuracy of detecting the position information of the joint assembly in the power transmission line image is improved.
Further, the second neural network model is obtained by training through the following steps:
acquiring a second training sample set; the second training sample set is a plurality of joint component images marked with the pin positions;
and training the initial second neural network model by using the second training sample set to obtain the trained second neural network model. By training the second neural network, accuracy of detecting the position information of the pin in the joint component image is improved.
Further, the third neural network model is obtained by training through the following steps:
acquiring a third training sample set; the third training sample set is a plurality of pin images which are subjected to state result labeling according to the shapes of the pins; wherein the status results include missing, loose, and normal;
and training an initial third neural network model by using the third training sample set to obtain a trained third neural network model. And the accuracy of the state result of the pin is improved by training the third neural network.
Further, the first neural network model is an R-FCN model, the second neural network model is a Yolov3 model, and the third neural network model is a VGG model. The accuracy of the detection result is improved through the cooperation of three different neural network models.
Further, the power transmission line image is shot by a holder camera on the unmanned aerial vehicle. The power transmission line image is acquired through the tripod head camera of the unmanned aerial vehicle, so that labor can be saved, and the image acquisition efficiency is improved.
The invention also provides a power transmission line pin state detection system, which comprises:
the power transmission line image acquisition module is used for acquiring a power transmission line image containing the positioning catenary supporting device; the positioning catenary supporting device is provided with a plurality of joint components, and each joint component comprises a pin;
the first identification module is used for inputting the power transmission line image into a pre-trained first neural network model to obtain the position information of the joint assembly in the power transmission line image;
the joint component image extraction module is used for extracting a joint component image from the power transmission line image according to the position information of the joint component in the power transmission line image;
the second recognition module is used for inputting the joint component image into a pre-trained second neural network model to obtain the position information of the pin in the joint component image;
the pin image extraction module is used for extracting the pin image from the joint component image according to the position information of the pin in the joint component image;
and the defect detection module is used for inputting the pin image to be detected into the pre-trained third neural network model to obtain the defect detection result of the pin.
Compared with the prior art, the power transmission line pin state detection system provided by the invention has the advantages that the position information of the joint component in the power transmission line image is detected through the first neural network model to extract the joint component image, then the position information of the pin in the joint component image is detected through the second neural network model to extract the pin image, and then the pin in the pin image is detected through the third neural network model to obtain the pin state result, so that the pin state detection efficiency and accuracy can be improved.
Further, the first neural network model is obtained by training through the following steps:
acquiring a first training sample set; the first training sample set is a plurality of electric transmission line images marked with the joint component positions;
and training an initial first neural network model by using the first training sample set to obtain a trained first neural network model. By training the first neural network, the accuracy of detecting the position information of the joint assembly in the power transmission line image is improved.
Further, the second neural network model is obtained by training through the following steps:
acquiring a second training sample set; the second training sample set is a plurality of joint component images marked with the pin positions;
and training the initial second neural network model by using the second training sample set to obtain the trained second neural network model. By training the second neural network, accuracy of detecting the position information of the pin in the joint component image is improved.
Further, the third neural network model is obtained by training through the following steps:
acquiring a third training sample set; the third training sample set is a plurality of pin images which are subjected to state result labeling according to the shapes of the pins; wherein the status results include missing, loose, and normal;
and training an initial third neural network model by using the third training sample set to obtain a trained third neural network model. And the accuracy of the state result of the pin is improved by training the third neural network.
In order that the invention may be more clearly understood, specific embodiments thereof will be described hereinafter with reference to the accompanying drawings.
Drawings
Fig. 1 is a flowchart of a method for detecting a pin status of a power transmission line according to an embodiment of the present invention.
Fig. 2 is a joint component position diagram of the power transmission line pin state detection method according to an embodiment of the present invention.
Fig. 3 is a pin position diagram of a pin state detection method for a power transmission line according to an embodiment of the present invention.
Fig. 4 is a block connection diagram of a power transmission line pin state detection system according to an embodiment of the present invention.
1. A power transmission line image acquisition module; 2. a first identification module; 3. a joint component image extraction module; 4. a second identification module; 5. a pin image extraction module; 6. and a defect detection module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Please refer to fig. 1, which is a flowchart of a method for detecting a pin status of a power transmission line according to an embodiment of the present invention, including the following steps:
s1, acquiring an image of the power transmission line containing the positioning catenary supporting device; wherein the positioning catenary support device is provided with a plurality of joint assemblies, which include pins.
The positioning catenary supporting device is used for supporting wires such as conducting wires and lightning conductors; the joint assembly further comprises a mast support, a thimble, a U-shaped ring and the like.
S2, inputting the electric transmission line image into a pre-trained first neural network model to obtain the position information of the joint assembly in the electric transmission line image.
The position information of the joint assembly in the power transmission line image is shown in fig. 2.
And S3, extracting a joint component image from the power transmission line image according to the position information of the joint component in the power transmission line image.
And S4, inputting the joint component image into a pre-trained second neural network model to obtain the position information of the pin in the joint component image.
Wherein the position information of the pin in the joint assembly image is shown in fig. 3.
And S5, extracting the pin image from the joint component image according to the position information of the pin in the joint component image.
And S6, inputting the pin image into a pre-trained third neural network model to obtain a state result of the pin.
The state result is determined according to the defect classification standard of the pin, and comprises three states of missing, loosening and normal. Wherein the missing state is that the pin is not detected in the corresponding joint component; the loosening state has different judgment standards according to different types of pins, and taking a cotter as an example, the opening angle of the cotter is smaller than a preset angle threshold value, which indicates that the cotter is in the loosening state; the normal state also has different judgment standards according to different pin types, taking a cotter as an example, the opening angle of the cotter is greater than or equal to the angle threshold value, which indicates that the cotter is in the normal state.
Wherein the first, second and third neural network models are each one of neural network models. A neural network is a complex network system formed by a large number of simple processing units widely connected to each other, reflects many basic features of an object, and is a highly complex nonlinear dynamical learning system. The neural network has the capabilities of large-scale parallel, distributed storage and processing, self-organization, self-adaptation and self-learning, and is particularly suitable for processing inaccurate and fuzzy information processing problems which need to consider many factors and conditions simultaneously.
At present, most of image detection research focuses on large targets such as equalizing rings, hanging plates and insulators, and pin-level defect identification is rarely concerned. The practical existing target detection method is very tricky in detecting pin defects in large-scale aerial transmission line images with complex backgrounds, because the image backgrounds are complex, and the pins are small in size and easy to block, so that the state results of the pins are difficult to recognize at one time.
Compared with the prior art, the method for detecting the state of the pin of the power transmission line comprises the steps of firstly detecting the position information of the joint component in the image of the power transmission line through the first neural network model to extract the image of the joint component, then detecting the position information of the pin in the image of the joint component through the second neural network model to extract the image of the pin, and then detecting the pin in the image of the pin through the third neural network model to obtain the state result of the pin, so that the efficiency and the accuracy of detecting the state of the pin can be improved.
In one possible embodiment, the first neural network model is trained by:
acquiring a first training sample set; the first training sample set is a plurality of electric transmission line images marked with the joint component positions;
and training an initial first neural network model by using the first training sample set to obtain a trained first neural network model.
Preferably, the first training sample set may be an image of the power transmission line after the joint component position labeling is performed on the network, or a photo shot by an unmanned aerial vehicle and subjected to the joint component position labeling. By training the first neural network, the accuracy of detecting the position information of the joint assembly in the power transmission line image is improved.
In one possible embodiment, the second neural network model is trained by:
acquiring a second training sample set; the second training sample set is a plurality of joint component images marked with the pin positions;
and training the initial second neural network model by using the second training sample set to obtain the trained second neural network model. By training the second neural network, accuracy of detecting the position information of the pin in the joint component image is improved.
In one possible embodiment, the third neural network model is trained by:
acquiring a third training sample set; the third training sample set is a plurality of pin images which are subjected to state result labeling according to the shapes of the pins; wherein the status results include missing, loose, and normal;
and training an initial third neural network model by using the third training sample set to obtain a trained third neural network model. And the accuracy of the state result of the pin is improved by training the third neural network.
In one possible embodiment, the first neural network model is an R-FCN model, the second neural network model is a YOLOV3 model, and the third neural network model is a VGG model. The accuracy of the detection result is improved through the cooperation of three different neural network models.
In a possible embodiment, the power transmission line image is obtained by shooting through a pan-tilt camera on the unmanned aerial vehicle. The power transmission line image is acquired through the tripod head camera of the unmanned aerial vehicle, so that labor can be saved, and the image acquisition efficiency is improved.
Referring to fig. 4, the present invention further provides a power transmission line pin status detection system, including:
the power transmission line image acquisition module 1 is used for acquiring a power transmission line image containing a positioning catenary supporting device; the positioning catenary supporting device is provided with a plurality of joint components, and each joint component comprises a pin;
the first identification module 2 is used for inputting the power transmission line image into a pre-trained first neural network model to obtain the position information of the joint assembly in the power transmission line image;
the joint component image extraction module 3 is used for extracting a joint component image from the power transmission line image according to the position information of the joint component in the power transmission line image;
the second recognition module 4 is used for inputting the joint component image into a pre-trained second neural network model to obtain the position information of the pin in the joint component image;
a pin image extraction module 5, configured to extract the pin image from the joint component image according to position information of the pin in the joint component image;
and the defect detection module 6 is used for inputting the pin image to be detected into the pre-trained third neural network model to obtain the defect detection result of the pin.
Wherein the first, second and third neural network models are each one of neural network models. A neural network is a complex network system formed by a large number of simple processing units widely connected to each other, reflects many basic features of an object, and is a highly complex nonlinear dynamical learning system. The neural network has the capabilities of large-scale parallel, distributed storage and processing, self-organization, self-adaptation and self-learning, and is particularly suitable for processing inaccurate and fuzzy information processing problems which need to consider many factors and conditions simultaneously.
Compared with the prior art, the power transmission line pin state detection system provided by the invention has the advantages that the position information of the joint component in the power transmission line image is detected through the first neural network model to extract the joint component image, then the position information of the pin in the joint component image is detected through the second neural network model to extract the pin image, and then the pin in the pin image is detected through the third neural network model to obtain the pin state result, so that the pin state detection efficiency and accuracy can be improved.
In one possible embodiment, the first neural network model is trained by:
acquiring a first training sample set; the first training sample set is a plurality of electric transmission line images marked with the joint component positions;
and training an initial first neural network model by using the first training sample set to obtain a trained first neural network model. By training the first neural network, the accuracy of detecting the position information of the joint assembly in the power transmission line image is improved.
In one possible embodiment, the second neural network model is trained by:
acquiring a second training sample set; the second training sample set is a plurality of joint component images marked with the pin positions;
and training the initial second neural network model by using the second training sample set to obtain the trained second neural network model. By training the second neural network, accuracy of detecting the position information of the pin in the joint component image is improved.
In one possible embodiment, the third neural network model is trained by:
acquiring a third training sample set; the third training sample set is a plurality of pin images which are subjected to state result labeling according to the shapes of the pins; wherein the status results include missing, loose, and normal;
and training an initial third neural network model by using the third training sample set to obtain a trained third neural network model. And the accuracy of the state result of the pin is improved by training the third neural network.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (10)
1. A method for detecting the state of a pin of a power transmission line is characterized by comprising the following steps:
acquiring a power transmission line image containing a positioning catenary supporting device; the positioning catenary supporting device is provided with a plurality of joint components, and each joint component comprises a pin;
inputting the power transmission line image into a pre-trained first neural network model to obtain position information of the joint assembly in the power transmission line image;
extracting a joint component image from the power transmission line image according to the position information of the joint component in the power transmission line image;
inputting the joint component image into a pre-trained second neural network model to obtain the position information of the pin in the joint component image;
extracting the pin image from the joint component image according to the position information of the pin in the joint component image;
and inputting the pin image into a pre-trained third neural network model to obtain a state result of the pin.
2. The method for detecting the state of the pin of the power transmission line according to claim 1, wherein the first neural network model is obtained by training through the following steps:
acquiring a first training sample set; the first training sample set is a plurality of electric transmission line images marked with the joint component positions;
and training an initial first neural network model by using the first training sample set to obtain a trained first neural network model.
3. The method for detecting the state of the pin of the power transmission line according to claim 1, wherein the second neural network model is obtained by training through the following steps:
acquiring a second training sample set; the second training sample set is a plurality of joint component images marked with the pin positions;
and training the initial second neural network model by using the second training sample set to obtain the trained second neural network model.
4. The method for detecting the state of the pin of the power transmission line according to claim 1, wherein the third neural network model is obtained by training through the following steps:
acquiring a third training sample set; the third training sample set is a plurality of pin images which are subjected to state result labeling according to the shapes of the pins; wherein the status results include missing, loose, and normal;
and training an initial third neural network model by using the third training sample set to obtain a trained third neural network model.
5. The method for detecting the state of the pin of the power transmission line according to claim 1, wherein: the first neural network model is an R-FCN model, the second neural network model is a YOLOV3 model, and the third neural network model is a VGG model.
6. The method for detecting the state of the pin of the power transmission line according to claim 1, wherein: the power transmission line image is obtained by shooting through a cloud deck camera on the unmanned aerial vehicle.
7. A power transmission line pin state detection system, comprising:
the power transmission line image acquisition module is used for acquiring a power transmission line image containing the positioning catenary supporting device; the positioning catenary supporting device is provided with a plurality of joint components, and each joint component comprises a pin;
the first identification module is used for inputting the power transmission line image into a pre-trained first neural network model to obtain the position information of the joint assembly in the power transmission line image;
the joint component image extraction module is used for extracting a joint component image from the power transmission line image according to the position information of the joint component in the power transmission line image;
the second recognition module is used for inputting the joint component image into a pre-trained second neural network model to obtain the position information of the pin in the joint component image;
the pin image extraction module is used for extracting the pin image from the joint component image according to the position information of the pin in the joint component image;
and the defect detection module is used for inputting the pin image to be detected into the pre-trained third neural network model to obtain the defect detection result of the pin.
8. The power transmission line pin status detection system of claim 7, wherein the first neural network model is trained by:
acquiring a first training sample set; the first training sample set is a plurality of electric transmission line images marked with the joint component positions;
and training an initial first neural network model by using the first training sample set to obtain a trained first neural network model.
9. The power transmission line pin status detection system of claim 7, wherein the second neural network model is trained by:
acquiring a second training sample set; the second training sample set is a plurality of joint component images marked with the pin positions;
and training the initial second neural network model by using the second training sample set to obtain the trained second neural network model.
10. The power transmission line pin status detection system of claim 7, wherein the third neural network model is trained by:
acquiring a third training sample set; the third training sample set is a plurality of pin images which are subjected to state result labeling according to the shapes of the pins; wherein the status results include missing, loose, and normal;
and training an initial third neural network model by using the third training sample set to obtain a trained third neural network model.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180174046A1 (en) * | 2016-12-15 | 2018-06-21 | Beijing Kuangshi Technology Co., Ltd. | Target detection method and device, neural network training method and device |
| CN110309865A (en) * | 2019-06-19 | 2019-10-08 | 上海交通大学 | A kind of unmanned plane patrolling power transmission lines pin defect system image-recognizing method |
| CN110569762A (en) * | 2019-08-27 | 2019-12-13 | 许昌许继软件技术有限公司 | pin falling detection method and device based on multistage neural network |
| CN111815623A (en) * | 2020-07-28 | 2020-10-23 | 南方电网数字电网研究院有限公司 | Power transmission line cotter pin missing identification method |
| CN113160170A (en) * | 2021-04-19 | 2021-07-23 | 国网山东省电力公司威海供电公司 | Pin-level fault identification and detection method, system, medium and equipment for power inspection |
-
2021
- 2021-09-28 CN CN202111143286.5A patent/CN114120164A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180174046A1 (en) * | 2016-12-15 | 2018-06-21 | Beijing Kuangshi Technology Co., Ltd. | Target detection method and device, neural network training method and device |
| CN110309865A (en) * | 2019-06-19 | 2019-10-08 | 上海交通大学 | A kind of unmanned plane patrolling power transmission lines pin defect system image-recognizing method |
| CN110569762A (en) * | 2019-08-27 | 2019-12-13 | 许昌许继软件技术有限公司 | pin falling detection method and device based on multistage neural network |
| CN111815623A (en) * | 2020-07-28 | 2020-10-23 | 南方电网数字电网研究院有限公司 | Power transmission line cotter pin missing identification method |
| CN113160170A (en) * | 2021-04-19 | 2021-07-23 | 国网山东省电力公司威海供电公司 | Pin-level fault identification and detection method, system, medium and equipment for power inspection |
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