CN107729808B - Intelligent image acquisition system and method for unmanned aerial vehicle inspection of power transmission line - Google Patents
Intelligent image acquisition system and method for unmanned aerial vehicle inspection of power transmission line Download PDFInfo
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Abstract
The invention discloses an intelligent image acquisition system and method for power transmission line unmanned aerial vehicle inspection, when an unmanned aerial vehicle inspection system reaches a suspension point along a planned flight path, a tower is roughly positioned, automatic adjustment of a nacelle or a cloud deck is realized, data acquisition equipment is aligned to a tower target, a frame of tower image information is obtained, the loss of a region extraction network and a classification network of a convolutional neural network is optimized and improved, and an improved convolutional neural network algorithm is utilized to quickly identify an attached typical component on the tower; determining the position of the target relative to the whole image according to the identified target position, calculating the pixel-level offset of the target from the center when the target position is not in the center of the image, and completing the position adjustment of the target in the image by converting the pixel value into the rotation quantity of the camera; and judging the area occupation ratio of the target in the image, and if the occupation ratio is smaller than a set value, zooming the camera or propelling the unmanned aerial vehicle to the position away from the target to shoot.
Description
Technical Field
The invention relates to the field of digital image processing, in particular to an intelligent image acquisition system and method for unmanned aerial vehicle inspection of a power transmission line.
Background
The transmission line is a framework support of a power grid, and with the increase of the transmission line history, especially the rapid development of ultrahigh voltage and extra-high voltage lines, more serious challenges are provided for the daily inspection work of the transmission line. Unmanned aerial vehicle patrols and examines the mode as a novel, efficient and is applied to daily patrolling and examining, but patrols and examines the in-process at unmanned aerial vehicle, needs two staff's cooperation to accomplish jointly and patrols and examines the task usually. One control hand is responsible for controlling the unmanned aerial vehicle platform, and one control hand is responsible for controlling the observation and the collection of airborne nacelle or cloud platform in order to accomplish the target information. In the inspection mode, on one hand, two control hands are required to be tightly matched, the requirement on the operation skill of an operator is high, and the labor intensity is high; on the other hand, the control is affected by communication delay, and the effect of target information acquisition is often not good. In order to reduce labor intensity and improve effectiveness of information acquisition, an intelligent acquisition system and method are urgently needed to achieve automatic acquisition of power transmission line information.
In the existing automatic acquisition system of the power transmission line, the unmanned aerial vehicle inspection tripod head control (ZL201210302421.0) of the power transmission line based on visual servo, which is proposed by the institute of electric power science of Shandong province electric power group, realizes the automatic control of the tripod head in a target matching mode, but can only be applied to the condition of a known target template, and has poor robustness.
The line inspection standardized acquisition method and system (CN201510504379.4) proposed by the aerospace scenic (Beijing) science and technology Limited company realizes fixed-point acquisition aiming at specific equipment by using set airway data and camera parameters, but the acquired transmission line image information is deviated due to the influence of field operation environment (illumination and wind speed) and GPS positioning precision.
Disclosure of Invention
The invention provides an intelligent image acquisition system and method for unmanned aerial vehicle inspection of a power transmission line, aiming at solving the problems.
The invention aims to provide an intelligent image acquisition system for unmanned aerial vehicle inspection of a power transmission line.
The second purpose of the invention is to provide an intelligent image acquisition method for unmanned aerial vehicle inspection of the power transmission line.
In order to achieve the purpose, the invention adopts the following technical scheme:
an intelligent image acquisition method for unmanned aerial vehicle inspection of a power transmission line comprises the following steps:
(1) when the unmanned aerial vehicle inspection system reaches a suspension point along a planned track route, performing coarse positioning on a tower, realizing automatic adjustment of a nacelle or a cloud deck, and aligning data acquisition equipment to a tower target;
(2) acquiring image information of a frame of tower, optimizing and improving the loss of an area extraction network and a classification network of a convolutional neural network, and quickly identifying an attached typical component on the tower by using an improved convolutional neural network algorithm;
(3) determining the position of the target relative to the whole image according to the identified target position, calculating the pixel-level offset of the target from the center when the target position is not in the center of the image, and completing the position adjustment of the target in the image by converting the pixel value into the rotation quantity of the camera;
(4) judging the area occupation ratio of the target in the image, if the occupation ratio is smaller than a set value, zooming the camera or pushing the unmanned aerial vehicle to the target to enable the occupation ratio of the target in the image to meet the set condition, and shooting;
(5) and (5) acquiring image information of a new position, executing the step (2) to the step (4), and continuously acquiring the inspection image.
In the step (1), the tower position coarse positioning specifically comprises the following steps:
(1-1) converting a geodetic coordinate system and a geocentric coordinate system according to GPS coordinates of the unmanned aerial vehicle platform and the tower to obtain position information of the tower relative to the unmanned aerial vehicle platform;
(1-2) when the unmanned aerial vehicle reaches the hovering position, converting a ground center coordinate system and an inertia coordinate system, and calculating to obtain the azimuth and the pitch angle of the airborne pod or the holder;
and (1-3) aligning the visual axis of the airborne nacelle or the holder to the tower through servo control, so that the tower appears in the large visual field range of the electric power line patrol nacelle.
And (1-2) calculating the azimuth angle and the pitch angle of the target relative to the airborne coordinate system according to the position coordinate information of the target in the airborne coordinate system after coordinate conversion.
In the step (2), based on the power transmission line inspection image database, the positions of the tower, the insulator, the vibration damper and the voltage-sharing ring component in the image are marked, category attribute labels are added to corresponding components, the marked power transmission line target component data are used as a training input data set, a VGG16 convolutional neural network front 13-layer network is adopted, the extraction of the power transmission line target image features is achieved, and finally the multi-dimensional high-level semantic features are obtained.
In the step (2), a reference window is constructed, a plurality of region extraction kernels are formed, region acquisition is performed on regions where targets may exist, and a region extraction network is formed to extract candidate positions of the target components.
In the step (2), the softmax algorithm is adopted to classify the extracted target area, the category attribute of the target is determined, and classification of multiple types of targets is realized.
In the step (2), the multidimensional high-level semantic feature data obtained by the feature extraction network is used as input data for training the area extraction network and the classification network.
In the step (2), the feature extraction network, the area extraction network and the classification network are combined and optimized, the feature extraction network is initialized by using a VGG16 convolutional neural network, and multi-dimensional feature data are extracted; respectively inputting the loss function into a regional extraction network and a classification network, optimizing the loss function by using a random gradient descent method, transmitting the loss to a feature extraction network through a feedback transmission network, and adjusting and optimizing internal parameters of the feature extraction network; and after multiple iterations, training the whole network is completed, and a power transmission line component detection model is obtained.
In the step (2), a power transmission line component detection process is completed by fusing a feature extraction network, a regional extraction network and a classification network, multi-dimensional image abstract features are obtained by performing feature extraction operation only once in the detection process, the regional extraction network quickly obtains possible position information of a target, and the classification network is used for classifying the target category.
In the step (2), a feature extraction convolution operation is carried out on the input image by using a feature extraction network to obtain a multi-dimensional feature map; generating a large number of transmission line target candidate region frames on the feature map by using a region extraction network; and taking out the features in the candidate region frames on the feature map and forming a high-dimensional feature vector, calculating the category score of the features of each target region by using a classification network, only keeping the target candidate frames with the scores higher than a set value, carrying out non-maximum value suppression on the target candidate region frames of the power transmission line, and predicting the more appropriate target peripheral frame position.
In the step (4), the servo control of the pan-tilt or the pod is performed based on the eye-in-hand vision servo control system.
In the step (4), the camera and the video camera are simultaneously installed on the cloud deck or the nacelle and are located at coaxial positions, the position conversion of the cloud deck is converted into the change of image parameters through a camera perspective projection matrix, so that the conversion relation between an image characteristic space and a cloud deck tail end position space is obtained, the position and angle information of the electric transmission line target relative to the image center is obtained, the pixel recorded offset is obtained, the rotating angular speed and linear speed of the camera are determined according to the linear mapping relation between the target pixel level offset and the cloud deck rotation quantity, the camera is adjusted through the servo control system to place the target at the center position, and therefore the automatic acquisition process is completed.
The utility model provides an image intelligent acquisition system for transmission line unmanned aerial vehicle patrols and examines, but off-line or on-line operation on processor equipment such as server specifically include:
the rough positioning module is configured to perform rough positioning on the tower when the unmanned aerial vehicle inspection system reaches a suspension point along a planned track route, so that automatic adjustment of a nacelle or a holder is realized, and the data acquisition equipment is aligned to a tower target;
the target identification module is configured to acquire image information of a frame of tower, optimize and improve the loss of an area extraction network and a classification network of the convolutional neural network, and quickly identify an attached typical component on the tower by using an improved convolutional neural network algorithm;
the computing module is configured to determine the position of the target relative to the whole image according to the identified target position, calculate the pixel-level offset of the target from the center when the target position is not in the center of the image, and complete the position adjustment of the target in the image by converting the pixel value into the rotation amount of the camera;
and the servo control module is configured to judge the area occupation ratio of the target in the image, and if the occupation ratio is smaller than a set value, the camera is zoomed in or is pushed close to the position of the unmanned aerial vehicle away from the target, so that the occupation ratio of the target in the image meets a set condition, and shooting is performed.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention realizes the rough positioning of the tower through the rapid positioning technology, then utilizes the target recognition algorithm to determine the position information of the target, and finally realizes the high-definition image information acquisition of the target by converting the target pixel information into the servo control system quantity;
2. according to the invention, the losses of the regional extraction network and the classification network are optimized, the convolution network is further improved, the training of the whole network is completed, and the power transmission line detection model is obtained and used for the actual detection process, so that the method has a good application prospect and can be helpful for completing the unmanned aerial vehicle inspection task of the power transmission line.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of a tower position coarse positioning coordinate transformation relationship of the present invention;
FIG. 2 is a block diagram of the area extraction network and classification network of the present invention;
fig. 3 is a flow chart of the intelligent acquisition system for the unmanned aerial vehicle inspection image of the power transmission line.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As introduced by the background art, in the prior art, the image intelligent acquisition system and method for unmanned aerial vehicle inspection of the power transmission line have the defects of poor robustness and easiness in being influenced by other factors when being applied to the known target template, and in order to solve the technical problems, the technology realizes the rough positioning of a tower through a quick positioning technology, then determines the position information of a target by using a target identification algorithm, and finally realizes the high-definition image information acquisition of the target by converting target pixel information into a servo control system quantity.
As shown in fig. 3, the method specifically includes:
the method comprises the following steps: and roughly positioning the position of the tower. When the unmanned aerial vehicle inspection system reaches the suspension point along the planned track route, the tower coarse positioning system is started to realize the automatic adjustment of the nacelle or the cradle head, and the data acquisition equipment is aligned to the tower target.
Step two: and automatically identifying the transmission line target. Acquiring image information of a frame of tower, and quickly identifying accessory typical components on the tower, such as typical components of insulators, vibration dampers, grading rings and the like, by using an improved convolutional neural network algorithm.
Step three: and calculating the rotation quantity. And automatically identifying and positioning the obtained target position by utilizing the second target, and determining the position of the target relative to the whole image. And if the target position is not at the center of the image, calculating the pixel-level offset of the target from the center, and converting the pixel value into the camera rotation amount to complete the position adjustment of the target in the image.
Step four: and rotating the holder and acquiring the image information at the new position again, and executing the second step and the third step to enable the image target to be in the center of the camera view field.
Step five: and collecting target component information. When the area ratio of the target in the image is less than 50%, zooming the camera or pushing the camera to the position of the unmanned aerial vehicle away from the target to enable the ratio of the target in the image to be not less than 50%; and starting a shutter to finish shooting.
The tower coarse position positioning technology based on the unmanned aerial vehicle inspection platform attitude and GPS information mainly uses inspection platform navigation information and tower GPS data, and according to conversion among several coordinate systems, the guiding azimuth and pitch angle values of the electric power inspection nacelle or the cradle head are obtained through calculation. The tower position coarse positioning in the first step comprises the following specific steps:
A. and converting a geodetic coordinate system and a geocentric coordinate system according to the GPS coordinates of the unmanned aerial vehicle platform and the tower to obtain the position information of the tower relative to the unmanned aerial vehicle platform.
B. When the unmanned aerial vehicle reaches the hovering position, the azimuth and the pitch angle of the airborne pod or the holder are calculated through the conversion of a ground center coordinate system and an inertia coordinate system, the distance D, the azimuth α and the pitch angle β of the target relative to the airborne coordinate system are calculated, and the formula is as follows:
and x, y and z are positions of the target in the airborne coordinate system after coordinate conversion.
C. Through servo control, the visual axis of the airborne nacelle or the cloud deck is aligned with the tower, so that the tower is in the large visual field range of the electric power line patrol nacelle.
The specific steps of target identification in the third step are as follows:
the identification and positioning of the component is accomplished based on a modified convolutional neural network (fast region conditional neural network).
A. Constructing power transmission line image feature extraction network
1. Based on the power transmission line inspection image database, the positions of components such as a tower, an insulator, a vibration damper, a grading ring and the like in the image are marked manually, and category attribute labels are added to the corresponding components.
2. The method comprises the steps of taking calibrated target component data (target area images and category labels) of the power transmission line as a training input data set, adopting a front 13-layer network in a VGG16 convolutional network, mainly comprising convolutional layers and pooling layers, realizing extraction of target image features of the power transmission line by optimizing parameters of each layer, and finally obtaining 512-dimensional high-level semantic features.
B. Constructing regional extraction networks and classification networks
1. The region extraction network is used for extracting candidate positions of the target component, defining three proportions and three scales of reference windows, forming nine region extraction kernels, and performing region acquisition on regions where targets may exist.
2. The classification network is used for classifying the extracted target area and determining the class attribute of the target. And the classification network adopts a softmax algorithm to realize the classification of various targets.
3. The area extraction network and the classification network are both connected behind the feature extraction network, and in order to obtain a better network, the losses of the area extraction network and the classification network need to be optimized, and the loss function is as follows:
wherein i represents the ith network region extraction core; λ represents a balance parameter; n is a radical ofclsRepresents the total category number; n is a radical ofregRepresenting the number of extracted target frames; p is a radical ofiRepresenting the prediction probability for the category of the ith target region,
representing the actual type probability of the object. t is tiInformation of four coordinates representing the extracted target frame,
representing actual coordinate information of the object. L isclsLog loss, L, for a classregRepresenting the loss of fit of the extraction box to the actual target box.
512-dimensional feature data obtained by the feature extraction network is used as input data for training the region extraction network and the classification network, and the combination relationship and the training process of the two networks are shown in fig. 2.
Performing combined tuning on the feature extraction network, the regional extraction network and the classification network, initializing the feature extraction network by using a VGG16 network, and extracting 512-dimensional feature data; respectively inputting the loss function into a regional extraction network and a classification network, optimizing the loss function by using a random gradient descent method, transmitting the loss to a feature extraction network through a feedback transmission network, and adjusting and optimizing internal parameters of the feature extraction network; and after multiple iterations, training the whole network is completed, and a power transmission line detection model is obtained and used for an actual detection process.
C. Object detection network
And completing the detection process of the power transmission line component by fusing the feature extraction network, the area extraction network and the classification network. In the detection process, feature extraction operation is performed only once to obtain 512-dimensional image abstract features, the regional extraction network quickly obtains possible position information of the target, and the classification network is used for classifying the target category, wherein the specific flow is as follows:
1. performing feature extraction convolution operation on the input image by using a feature extraction network to obtain a 512-dimensional feature map;
2. generating a large number of transmission line target candidate region frames on the feature map by using a region extraction network;
3. extracting features in the candidate region frames on the feature map and forming high-dimensional feature vectors, calculating the category score of the features of each target region by a classification network, and only keeping the first 300 target candidate frames with higher scores;
4. and carrying out non-maximum suppression on the target candidate area frame of the power transmission line, and predicting a more appropriate target peripheral frame position.
The vision servo system can be classified into an Eye-in-Hand system (Eye in Hand) and an Eye-to-Hand system (Eye to Hand) according to the installation position of the vision sensor. The eye-in-hand system is used for installing the sensor on the robot end effector, so that the vision range is large, the movement is flexible, and high control precision can be obtained. When the visual servo is carried out, the position and the orientation of the object are calculated in real time by using an image visual means, and the rotation angle and the rotation quantity of the nacelle or the holder are determined according to the information of the orientation and the position of the object. The servo control process of step four is image-based eye-on-hand vision servo control.
The camera and the video camera are simultaneously arranged on the cloud platform or the nacelle and are in coaxial positions. The visual servo is carried out by adopting an Eye-in-Hand mode, the holder has m degrees of freedom, and the angular speed of the rotation of the holder is q ═ q1,L,qp]The linear velocity of the tip is r ═ r1,L,rm]The two have the following relationship: r is JrQ, wherein
And converting the position transformation of the holder into the change of image parameters through a camera perspective projection matrix, thereby obtaining the transformation relation between the image characteristic space and the holder tail end position space. Assuming image coordinatesTo (x, y), the final transformation relation is:
wherein
f is the camera focal length. u ═ vx,νy,νz,ωx,ωy,ωz]TAnd ν and ω indicate linear and angular velocities of rotation of the camera. J. the design is a squareImageThe image feature change rate is the Jacobian matrix of the image, and the relation between the image feature change rate and the holder speed is represented by a composite Jacobian matrix J ═ JImage·JrAnd (4) showing.
According to the position and angle information of the transmission line target relative to the image center obtained in the third step, the pixel remembering offset (P) is obtainedx,Py) Because the pan-tilt control adopts a Look-then-move mode, the linear mapping relation between the target pixel level offset and the pan-tilt rotation amount is defined as follows:
(Px,Py)＝JImage*u
the Jacobian matrix of the image and the angular velocity and the linear velocity of the rotation of the camera can be solved by selecting a conversion equation. And adjusting the camera through the servo control system to place the target at the center position of the view field, thereby completing the automatic acquisition process.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
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 (12)
1. An intelligent image acquisition method for unmanned aerial vehicle inspection of a power transmission line is characterized by comprising the following steps: the method comprises the following steps:
(1) when the unmanned aerial vehicle inspection system reaches a suspension point along a planned track route, performing coarse positioning on a tower, realizing automatic adjustment of a nacelle or a cloud deck, and aligning data acquisition equipment to a tower target;
(2) acquiring image information of a frame of tower, optimizing and improving the loss of an area extraction network and a classification network of a convolutional neural network, and quickly identifying an attached typical component on the tower by using an improved convolutional neural network algorithm;
(3) determining the position of the target relative to the whole image according to the identified target position, calculating the pixel-level offset of the target from the center when the target position is not in the center of the image, and completing the position adjustment of the target in the image by converting the pixel value into the rotation quantity of the camera;
(4) judging the area occupation ratio of the target in the image, if the occupation ratio is smaller than a set value, zooming the camera or propelling the unmanned aerial vehicle to the position away from the target to enable the occupation ratio of the target in the image to meet the set condition, and shooting;
(5) acquiring image information of a new position, executing the step (2) to the step (4), and continuously acquiring the inspection image;
in the step (2), the feature extraction network, the area extraction network and the classification network are combined and optimized, the feature extraction network is initialized by using a convolution network, and multi-dimensional feature data are extracted; respectively inputting the loss function into a regional extraction network and a classification network, optimizing the loss function by using a random gradient descent method, transmitting the loss to a feature extraction network through a feedback transmission network, and adjusting and optimizing internal parameters of the feature extraction network; and after multiple iterations, training the whole network is completed, and a power transmission line detection model is obtained.
2. The intelligent image acquisition method for the unmanned aerial vehicle inspection of the power transmission line according to claim 1, which is characterized in that: in the step (1), the tower position coarse positioning specifically comprises the following steps:
(1-1) converting a geodetic coordinate system and a geocentric coordinate system according to GPS coordinates of the unmanned aerial vehicle platform and the tower to obtain position information of the tower relative to the unmanned aerial vehicle platform;
(1-2) when the unmanned aerial vehicle reaches the hovering position, converting a ground center coordinate system and an inertia coordinate system, and calculating to obtain the azimuth and the pitch angle of the airborne nacelle or the cloud deck and the distance of the target relative to the airborne coordinate system;
and (1-3) aligning the visual axis of the airborne nacelle or the holder to the tower through servo control, so that the tower appears in the large visual field range of the electric power line patrol nacelle.
3. The intelligent image acquisition method for the unmanned aerial vehicle inspection of the power transmission line according to claim 2, characterized in that: and (1-2) calculating the azimuth angle and the pitch angle of the target relative to the airborne coordinate system according to the position coordinate information of the target in the airborne coordinate system after coordinate conversion.
4. The intelligent image acquisition method for the unmanned aerial vehicle inspection of the power transmission line according to claim 1, which is characterized in that: in the step (2), based on the power transmission line inspection image database, the positions of the tower, the insulator, the vibration damper and the voltage-sharing ring component in the image are marked, category attribute labels are added to corresponding components, the marked power transmission line target component data are used as a training input data set, a front 13-layer network in a VGG16 convolutional network is adopted, the extraction of the power transmission line target image features is achieved, and finally the multi-dimensional high-level semantic features are obtained.
5. The intelligent image acquisition method for the unmanned aerial vehicle inspection of the power transmission line according to claim 1, which is characterized in that: in the step (2), a reference window is constructed, a plurality of region extraction kernels are formed, region acquisition is performed on regions where targets may exist, and a region extraction network is formed to extract candidate positions of the target components.
6. The intelligent image acquisition method for the unmanned aerial vehicle inspection of the power transmission line according to claim 1, which is characterized in that: in the step (2), the softmax algorithm is adopted to classify the extracted target area, the category attribute of the target is determined, and classification of multiple types of targets is realized.
7. The intelligent image acquisition method for the unmanned aerial vehicle inspection of the power transmission line according to claim 1, which is characterized in that: in the step (2), the multidimensional high-level semantic feature data obtained by the feature extraction network is used as input data for training the area extraction network and the classification network.
8. The intelligent image acquisition method for the unmanned aerial vehicle inspection of the power transmission line according to claim 1, which is characterized in that: in the step (2), a power transmission line component detection process is completed by fusing a feature extraction network, a regional extraction network and a classification network, multi-dimensional image abstract features are obtained by performing feature extraction operation only once in the detection process, the regional extraction network quickly obtains possible position information of a target, and the classification network is used for classifying the target category.
9. The intelligent image acquisition method for the unmanned aerial vehicle inspection of the power transmission line according to claim 1, which is characterized in that: in the step (2), a feature extraction convolution operation is carried out on the input image by using a feature extraction network to obtain a multi-dimensional feature map; generating a large number of transmission line target candidate region frames on the feature map by using a region extraction network; and taking out the features in the candidate region frames on the feature map and forming a high-dimensional feature vector, calculating the category score of the features of each target region by using a classification network, only keeping the target candidate frames with the scores higher than a set value, carrying out non-maximum value suppression on the target candidate region frames of the power transmission line, and predicting the more appropriate target peripheral frame position.
10. The intelligent image acquisition method for the unmanned aerial vehicle inspection of the power transmission line according to claim 1, which is characterized in that: in the step (4), the servo control of the pan-tilt or the pod is performed based on the eye-in-hand vision servo control system.
11. The intelligent image acquisition method for the unmanned aerial vehicle inspection of the power transmission line according to claim 1, which is characterized in that: in the step (4), the camera and the video camera are simultaneously installed on the cloud deck or the nacelle and are located at coaxial positions, the position conversion of the cloud deck is converted into the change of image parameters through a camera perspective projection matrix, so that the conversion relation between an image characteristic space and a cloud deck tail end position space is obtained, the position and angle information of the electric transmission line target relative to the image center is obtained, the pixel recorded offset is obtained, the rotating angular speed and linear speed of the camera are determined according to the linear mapping relation between the target pixel level offset and the cloud deck rotation quantity, the camera is adjusted through the servo control system to place the target at the center position, and therefore the automatic acquisition process is completed.
12. The utility model provides an image intelligent acquisition system for transmission line unmanned aerial vehicle patrols and examines, characterized by: the method specifically comprises the following steps:
the rough positioning module is configured to perform rough positioning on the tower when the unmanned aerial vehicle inspection system reaches a suspension point along a planned track route, so that automatic adjustment of a nacelle or a holder is realized, and the data acquisition equipment is aligned to a tower target;
the target identification module is configured to acquire image information of a frame of tower, optimize and improve the loss of an area extraction network and a classification network of the convolutional neural network, and quickly identify an attached typical component on the tower by using an improved convolutional neural network algorithm;
the computing module is configured to determine the position of the target relative to the whole image according to the identified target position, calculate the pixel-level offset of the target from the center when the target position is not in the center of the image, and complete the position adjustment of the target in the image by converting the pixel value into the rotation amount of the camera;
the servo control module is configured to judge the area occupation ratio of the target in the image, and if the occupation ratio is smaller than a set value, the camera is zoomed in or the position of the unmanned aerial vehicle away from the target is pushed forward, so that the occupation ratio of the target in the image meets a set condition, and shooting is carried out;
optimizing and improving the loss of the area extraction network and the classification network of the convolutional neural network, wherein the optimizing and improving the loss of the area extraction network and the classification network of the convolutional neural network comprises the steps of carrying out joint tuning on the feature extraction network, the area extraction network and the classification network, initializing the feature extraction network by using the convolutional network, and extracting multi-dimensional feature data; respectively inputting the loss function into a regional extraction network and a classification network, optimizing the loss function by using a random gradient descent method, transmitting the loss to a feature extraction network through a feedback transmission network, and adjusting and optimizing internal parameters of the feature extraction network; and after multiple iterations, training the whole network is completed, and a power transmission line detection model is obtained.
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