CN114723271A - Power transmission project quality detection method and system based on image recognition - Google Patents
Power transmission project quality detection method and system based on image recognition Download PDFInfo
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Abstract
The invention provides a power transmission project quality detection method and system based on image recognition, wherein the method comprises the steps that an unmanned aerial vehicle flies autonomously after taking off, and point clouds are obtained by scanning a power transmission line project; comparing the point cloud data with the trained first recognition model to obtain a construction progress corresponding to the current point cloud data, calling a corresponding preset route under the current construction progress, and acquiring an image of a target point; and comparing the second identification models of the key points in the image to obtain the quality condition of the current power transmission project. The unmanned aerial vehicle is used as a mobile platform, visible light images and point cloud data are automatically acquired, and efficient acquisition of information of a construction site is realized; the difference between the engineering entity and the recognition model is automatically judged through the AI technology integrated by the front-end computing unit, so that the intelligent management of the whole process of the power transmission engineering is realized; in addition, the follow-up construction progress is intelligently predicted based on the analysis and induction of the existing data.
Description
Technical Field
The invention relates to the technical field of transmission project quality and progress analysis, in particular to a transmission project quality detection method and system based on image recognition.
Background
Accurate monitoring of quality and progress in the power transmission engineering construction process is vital to guaranteeing safe and efficient completion of engineering construction, most of using means at the present stage are manual field verification, the method is easily influenced by factors such as environment and personnel subjectivity, real and effective information is difficult to feed back, and relevant predictive decision making is seriously influenced.
In recent years, AI technology is increasingly appearing in daily life of people, and application of advanced technologies such as automatic driving and intelligent logistics is making life and working modes of people improved profoundly. Unmanned aerial vehicles are increasingly perfected as a mobile platform with high maneuverability, and related application technologies are increasingly perfected. The automatic analysis function of combining the edge computing technology and the high maneuverability of the unmanned aerial vehicle can realize the intelligent analysis and judgment of the quality and progress of the power transmission project without the need of personnel to arrive at the construction site, and the purpose of remotely surveying the construction site is achieved.
Disclosure of Invention
The invention provides a power transmission project quality detection method and system based on image recognition, which are used for solving the problem that a verification result of project quality is not attractive in the existing power transmission project construction process.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a power transmission project quality detection method based on image recognition, which comprises the following steps:
the unmanned aerial vehicle flies autonomously after taking off and acquires point cloud through scanning power transmission line engineering;
comparing the point cloud data with the trained first recognition model to obtain a construction progress corresponding to the current point cloud data, calling a corresponding preset route under the current construction progress, and acquiring an image of a target point;
and comparing the second identification models of the key points in the image to obtain the quality condition of the current power transmission project.
Further, the training process of the first target recognition model specifically includes:
determining the tower type of the power transmission line and each milestone node in the construction process, and carrying out point cloud data acquisition on the line towers under different milestone nodes to construct a point cloud sample library;
classifying and labeling the point cloud data according to the construction progress to obtain an engineering progress data set;
and carrying out convolutional neural network training on the project progress data set to obtain a first recognition model based on project progress recognition.
Further, the classification includes classification based on project time period, and the labeling includes labeling of different project tower types.
Further, the preset route is planned through the point cloud data of the line towers under different milestone nodes and the position data of the target point, and a route file is obtained.
Further, the airline file, the first recognition model, and the second recognition model are all deployed on the drone.
Further, the training process of the second target recognition model specifically includes:
defining the tower type of the power transmission line and each milestone node in the construction process, and carrying out image acquisition on target points under different milestone nodes to construct an image sample library;
labeling key points in the image data to obtain a quality judgment data set;
and performing convolutional neural network training on the quality discrimination data set to obtain a second recognition model based on engineering quality recognition.
Further, the marking of the key points in the image data comprises marking of a tower foundation, a tower plate, hanging points at two ends of an insulator and hanging points of a ground wire.
The invention provides a transmission engineering quality detection system based on image recognition, which comprises an unmanned aerial vehicle, a comparison processing unit and a quality discrimination unit,
the unmanned aerial vehicle flies autonomously after taking off and acquires point cloud through scanning power transmission line engineering;
the comparison processing unit is used for comparing the trained first identification model based on the point cloud data to obtain a construction progress corresponding to the current point cloud data, calling a corresponding preset air route under the current construction progress and acquiring an image of a target point;
and the quality judgment unit obtains the quality condition of the current power transmission project according to the comparison of the second identification models of the key points in the image.
Further, the system also comprises a first model training unit, wherein the first model training unit is trained on the basis of a convolutional neural network to obtain a first recognition model;
the first model training unit includes:
the first information acquisition subunit defines the tower type of the power transmission line and each milestone node in the construction process, and carries out point cloud data acquisition on the line towers under different milestone nodes to construct a point cloud sample library;
the first data processing subunit classifies and labels the point cloud data according to the construction progress to obtain an engineering progress data set;
and the first training subunit performs convolutional neural network training on the engineering progress data set to obtain a first recognition model based on engineering progress recognition.
Further, the system also comprises a second model training unit, wherein the second model training unit is trained on the basis of a convolutional neural network to obtain a second recognition model;
the second model training unit includes:
the second information acquisition subunit defines the tower type of the power transmission line and each milestone node in the construction process, and acquires images of target points under different milestone nodes to construct an image sample library;
the second data processing subunit labels key points in the image data to obtain a quality judgment data set;
and the second training subunit performs convolutional neural network training on the quality discrimination data set to obtain a second recognition model based on engineering quality recognition.
The power transmission project quality detection system according to the second aspect of the present invention can achieve the methods according to the first aspect and the respective implementation manners of the first aspect, and achieves the same effects.
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:
the unmanned aerial vehicle is used as a mobile platform, visible light images and point cloud data are automatically acquired, and efficient acquisition of information of a construction site is realized; the difference between the engineering entity and the recognition model is automatically judged through the AI technology integrated by the front-end computing unit, so that the intelligent management of the whole process of the power transmission engineering is realized; in addition, based on the analysis and conclusion of the existing data, the follow-up construction progress is intelligently predicted, compared with the manual detection mode in the prior art, the result is more objective and accurate, the illegal behaviors and the defect problems in the construction process are identified and alarmed, and convenience is brought to power transmission line engineering quality inspection personnel.
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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 an embodiment of the method of the present invention;
FIG. 2 is a flow chart of a specific implementation of the embodiment of the present invention;
FIG. 3 is a schematic diagram of a training process of a neural network model according to an embodiment of the method of the present invention;
fig. 4 is a schematic structural diagram of an embodiment 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, an embodiment of the present invention provides a power transmission engineering quality detection method based on image recognition, where the detection method includes the following steps:
s1, the unmanned aerial vehicle flies autonomously after taking off, and point clouds are obtained by scanning the power transmission line project;
s2, comparing the point cloud data with the trained first recognition model to obtain a construction progress corresponding to the current point cloud data, calling a corresponding preset air route under the current construction progress, and acquiring an image of a target point;
and S3, comparing the second identification models of the key points in the image to obtain the quality condition of the current power transmission project.
As shown in fig. 2, unmanned aerial vehicle takes off and flies to building the circuit top independently, unmanned aerial vehicle scans current shaft tower point cloud through airborne LiDAR, airborne AI calculation module carries out analysis and judgment to the construction progress based on first identification model, obtain corresponding construction progress, record that AI calculation module calls the course line that is fit for current construction progress and patrol and examine the shooting, airborne AI calculation module carries out analysis and judgment to construction quality based on second identification model, with construction progress and quality report output, unmanned aerial vehicle returns a journey after accomplishing the task.
As shown in fig. 3, the training ideas of the first recognition model and the second recognition model are similar, specifically, the method includes defining a tower type of the power transmission line and milestone nodes in the construction process, acquiring point cloud of the line tower and image data of key points under the milestone nodes, planning a route according to the point cloud under each milestone node based on the image data, acquiring a route file, labeling and training the point cloud and the image based on the image data, acquiring a model, and deploying the model and the route on the recording AI module.
Specifically, the training process of the first target recognition model specifically includes:
defining the tower type of the power transmission line and each milestone node in the construction process, carrying out LiDAR point cloud data acquisition on the line towers under different milestone nodes, constructing a point cloud sample library, and requiring good point cloud data precision;
classifying and marking the point cloud data according to construction progress to obtain an engineering progress data set, wherein the classification comprises classification based on construction period time stages, and the marking comprises marking of tower types in different construction periods;
and carrying out convolutional neural network training on the project progress data set to obtain a first recognition model based on project progress recognition. The model was trained using the YOLO V5 algorithm.
Planning a preset route through the point cloud data of the line towers under different milestone nodes and the position data of the target point to obtain a route file.
The airline file, the first recognition model and the second recognition model are all deployed on an AI module of the unmanned aerial vehicle.
The airline planning in the airline file is set based on the construction progress, for example, when a tower just starts to build a foundation, only whether the tower foot is in compliance needs to be shot, the airplane can carry out zoom shooting above the airline channel, the height does not need to be reduced, and therefore safety is guaranteed. For example, after the tower is completed, hardware hanging points need to be shot, the route strategy is changed into fine routing inspection at the moment, the hanging points need to be shot on two sides of the route, and a zoom shooting mode can be adopted at the moment, so that single-machine-position multi-angle shooting is realized, and accidents caused by the fact that an airplane flies into the tower bed are avoided.
The AI module depends on AI computing module hardware, such as the wonderful calculation of the great Xinjiang, the intelligent box of science and technology, and the like, but the AI computing module of the GPU core can be adopted for image analysis and computation.
The training process of the second target recognition model specifically comprises the following steps:
defining the tower type of the power transmission line and each milestone node in the construction process, carrying out image acquisition on target points under different milestone nodes, and constructing an image sample library, wherein the image is required to be clear and consistent with the resolution in practical application;
marking key points in the image data to obtain a quality judgment data set, wherein the marking of the key points in the image data comprises marking of a tower foundation, a tower plate, hanging points at two ends of an insulator and hanging points of a ground wire;
and performing convolutional neural network training on the quality discrimination data set to obtain a second recognition model based on engineering quality recognition.
As shown in fig. 4, an embodiment of the present invention further provides a power transmission project quality detection system based on image recognition, where the system includes an unmanned aerial vehicle 1, a comparison processing unit 2, and a quality determination unit 3.
The unmanned aerial vehicle 1 flies autonomously after taking off and acquires point cloud through scanning power transmission line engineering; the comparison processing unit 2 compares the trained first recognition model based on the point cloud data to obtain a construction progress corresponding to the current point cloud data, calls a corresponding preset air route under the current construction progress, and acquires an image of a target point; and the quality judgment unit 3 obtains the quality condition of the current power transmission project according to the comparison of the second identification models of the key points in the image.
The system also comprises a first model training unit 4, wherein the first model training unit is trained on the basis of a convolutional neural network to obtain a first recognition model;
the first model training unit 4 comprises a first information acquisition sub-unit 41, a first data processing sub-unit 42 and a first training sub-unit 43.
The first information acquisition subunit 41 defines the tower type of the power transmission line and each milestone node in the construction process, and performs point cloud data acquisition on the line towers under different milestone nodes to construct a point cloud sample library; the first data processing subunit 42 classifies and labels the point cloud data according to the construction progress to obtain an engineering progress data set; the first training subunit 43 performs convolutional neural network training on the engineering progress data set to obtain a first recognition model based on engineering progress recognition.
The system also comprises a second model training unit 5, wherein the second model training unit is trained on the basis of a convolutional neural network to obtain a second recognition model;
the second model training unit 5 includes a second information acquisition subunit 51, a second data processing subunit 52, and a second training subunit 53.
The second information acquisition subunit 51 defines the tower type of the power transmission line and each milestone node in the construction process, and performs image acquisition on target points under different milestone nodes to construct an image sample library; the second data processing subunit 52 labels the key points in the image data to obtain a quality judgment data set; the second training subunit 53 performs convolutional neural network training on the quality discrimination data set to obtain a second recognition model based on engineering quality recognition.
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 power transmission project quality detection method based on image recognition is characterized by comprising the following steps:
the unmanned aerial vehicle flies autonomously after taking off and acquires point cloud through scanning power transmission line engineering;
comparing the point cloud data with the trained first recognition model to obtain a construction progress corresponding to the current point cloud data, calling a corresponding preset route under the current construction progress, and acquiring an image of a target point;
and comparing according to the second identification model of the key points in the image to obtain the quality condition of the current power transmission project.
2. The method for detecting the quality of the power transmission project based on the image recognition as claimed in claim 1, wherein the training process of the first target recognition model is specifically as follows:
determining the tower type of the power transmission line and each milestone node in the construction process, and carrying out point cloud data acquisition on the line towers under different milestone nodes to construct a point cloud sample library;
classifying and labeling the point cloud data according to the construction progress to obtain an engineering progress data set;
and carrying out convolutional neural network training on the project progress data set to obtain a first recognition model based on project progress recognition.
3. The method of claim 2, wherein the classification includes classification based on time period of the project, and the labeling includes labeling of tower types in different projects.
4. The image recognition-based transmission project quality detection method according to claim 2, wherein the preset route is planned through line tower point cloud data and target point position data under different milestone nodes to obtain a route file.
5. The image recognition-based transmission project quality detection method according to claim 4, wherein the route file, the first recognition model and the second recognition model are all deployed on an unmanned aerial vehicle.
6. The method for detecting the quality of the power transmission project based on the image recognition as claimed in claim 1, wherein the training process of the second target recognition model is specifically as follows:
defining the tower type of the power transmission line and each milestone node in the construction process, and carrying out image acquisition on target points under different milestone nodes to construct an image sample library;
marking key points in the image data to obtain a quality judgment data set;
and performing convolutional neural network training on the quality discrimination data set to obtain a second recognition model based on engineering quality recognition.
7. The image recognition-based transmission project quality detection method according to claim 6, wherein the labeling of the key points in the image data includes labeling of a tower foundation, a tower plate, insulator both-end hanging points, and ground wire hanging points.
8. A power transmission project quality detection system based on image recognition is characterized by comprising an unmanned aerial vehicle, a comparison processing unit and a quality judging unit,
the unmanned aerial vehicle flies autonomously after taking off and acquires point cloud through scanning power transmission line engineering;
the comparison processing unit is used for comparing the trained first identification model based on the point cloud data to obtain a construction progress corresponding to the current point cloud data, calling a corresponding preset air route under the current construction progress and acquiring an image of a target point;
and the quality judgment unit obtains the quality condition of the current power transmission project according to the comparison of the second identification models of the key points in the image.
9. The system according to claim 8, wherein the system further comprises a first model training unit, wherein the first model training unit is trained based on a convolutional neural network to obtain a first recognition model;
the first model training unit includes:
the first information acquisition subunit defines the tower type of the power transmission line and each milestone node in the construction process, and carries out point cloud data acquisition on the line towers under different milestone nodes to construct a point cloud sample library;
the first data processing subunit classifies and labels the point cloud data according to the construction progress to obtain an engineering progress data set;
and the first training subunit performs convolutional neural network training on the engineering progress data set to obtain a first recognition model based on engineering progress recognition.
10. The system for detecting the quality of the power transmission project based on the image recognition as claimed in claim 8, wherein the system further comprises a second model training unit, the second model training unit is trained based on a convolutional neural network to obtain a second recognition model;
the second model training unit includes:
the second information acquisition subunit defines the tower type of the power transmission line and each milestone node in the construction process, and acquires images of target points under different milestone nodes to construct an image sample library;
the second data processing subunit labels key points in the image data to obtain a quality judgment data set;
and the second training subunit performs convolutional neural network training on the quality discrimination data set to obtain a second recognition model based on engineering quality recognition.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115660262A (en) * | 2022-12-29 | 2023-01-31 | 网思科技股份有限公司 | Intelligent engineering quality inspection method, system and medium based on database application |
| CN116797406A (en) * | 2023-06-29 | 2023-09-22 | 华腾建信科技有限公司 | Engineering data processing method and system capable of automatically generating visual progress |
| CN116962649A (en) * | 2023-09-19 | 2023-10-27 | 安徽送变电工程有限公司 | Image monitoring and adjusting system and line construction model |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115660262A (en) * | 2022-12-29 | 2023-01-31 | 网思科技股份有限公司 | Intelligent engineering quality inspection method, system and medium based on database application |
| CN116797406A (en) * | 2023-06-29 | 2023-09-22 | 华腾建信科技有限公司 | Engineering data processing method and system capable of automatically generating visual progress |
| CN116962649A (en) * | 2023-09-19 | 2023-10-27 | 安徽送变电工程有限公司 | Image monitoring and adjusting system and line construction model |
| CN116962649B (en) * | 2023-09-19 | 2024-01-09 | 安徽送变电工程有限公司 | Image monitoring and adjusting system and line construction model |
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