CN114022761A - Detection and positioning method and device for power transmission line tower based on satellite remote sensing image - Google Patents
Detection and positioning method and device for power transmission line tower based on satellite remote sensing image Download PDFInfo
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Abstract
The invention relates to a method and a device for detecting and positioning a power transmission line tower by using a satellite remote sensing image, wherein the method comprises the following steps: cutting the satellite remote sensing images of the research area to be predicted according to a preset overlapping degree, numbering and recording the position information of each cut image in the satellite remote sensing images of the research area to be predicted, and acquiring image data cut according to the preset overlapping degree; inputting the image data cut according to the preset overlapping degree into a pre-trained target detection model, and acquiring the prediction information of each picture in the image data cut according to the preset overlapping degree; the prediction information of each picture comprises normalized coordinates and confidence degrees of the center point of the power transmission line tower; the trained target detection model is a model obtained by training and testing a yolov5 target detection model in advance by adopting a training set and a testing set of satellite remote sensing images.
Description
Technical Field
The invention relates to the technical field of operation and maintenance of power transmission lines, in particular to a method and a device for detecting and positioning a power transmission line tower through satellite remote sensing images.
Background
The electric energy is a main component of an energy structure in China and is an important support for economic development. The transmission line towers are facilities for bearing and guiding high-voltage overhead lines in a power grid, and along with the popularization of electric power and the increasing complexity of the power grid, the number of the transmission line towers is increased year by year, and the transmission line towers have the characteristics of wide distribution, large span and complex surrounding terrain. If the position of the power transmission line tower is obtained by means of traditional manual field measurement, although the precision is high, a large amount of time and labor are required, the cost is high, and the efficiency is low. The remote sensing image contains abundant spatial information and has important significance in resource investigation, environment monitoring, geological disaster investigation, regional analysis, construction planning and the like. If the remote sensing image visual interpretation method is used for obtaining the position of the power transmission line tower, higher labor and time cost is still needed, the accuracy depends on the professional skill level and the meticulous degree of an interpreter, and due to the influence of visual fatigue, the efficiency and the accuracy of manual detection can be obviously reduced by continuous manual visual interpretation work.
Disclosure of Invention
Technical problem to be solved
In view of the above disadvantages and shortcomings of the prior art, the invention provides a method and a device for detecting and positioning a power transmission line tower by using a satellite remote sensing image, which solve the technical problems of high cost and low efficiency caused by the fact that a large amount of time and labor are required to be invested although the position of the power transmission line tower is obtained by manual field measurement in the traditional method.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
in a first aspect, an embodiment of the present invention provides a method for detecting and positioning a power transmission line tower based on a satellite remote sensing image, including:
cutting the satellite remote sensing images of the research area to be predicted according to a preset overlapping degree, numbering and recording the position information of each cut image in the satellite remote sensing images of the research area to be predicted, and acquiring image data cut according to the preset overlapping degree;
inputting the image data cut according to the preset overlapping degree into a pre-trained target detection model, and acquiring the prediction information of each picture in the image data cut according to the preset overlapping degree;
the prediction information of each picture comprises normalized coordinates and confidence degrees of the center point of the power transmission line tower;
the trained target detection model is a model obtained by training and testing a yolov5 target detection model in advance by adopting a training set and a testing set of satellite remote sensing images.
Preferably, the method further comprises:
analyzing and processing the prediction information of each picture in the image data cut according to the preset overlapping degree, and determining independent complete transmission line towers, the number of the transmission line towers and the coordinates of the transmission line towers in the image data cut according to the certain overlapping degree.
Preferably, the analyzing and processing are performed on the prediction information of each picture in the image data cut according to the preset overlapping degree, and the determining of the independent and complete transmission line towers, the number of the transmission line towers and the coordinates of the transmission line towers in the image data cut according to the certain overlapping degree specifically includes:
the prediction information of each picture comprises normalized coordinates and confidence degrees of the center point of the power transmission line tower;
performing first coordinate conversion processing on the normalized coordinates of the central point of the power transmission line tower in each picture to obtain the coordinates in the corresponding picture;
performing second coordinate conversion processing on the position information of each image in the satellite remote sensing image of the research area to be predicted and the coordinates in each picture to obtain the coordinates in the satellite remote sensing image of the research area to be predicted;
respectively judging whether the confidence of the center point of the power transmission line tower in the prediction information of each picture is lower than a preset threshold value, acquiring a first judgment result, deleting the power transmission line tower which is lower than the preset threshold value and is used as a detection target of the yolov5 target detection model, and keeping the power transmission line tower which is larger than or equal to the preset threshold value and is used as a detection target of the yolov5 target detection model;
judging whether the prediction frames in the yolov5 target detection model are overlapped or not, obtaining a second judgment result, and determining the power transmission line tower serving as the final detection target of the yolov5 target detection model according to the second judgment result;
and converting coordinates in the satellite remote sensing image of the research area to be predicted, which corresponds to the power transmission line pole tower of the final detection target of the yolov5 target detection model, into corresponding geographic coordinates by adopting a gdal six-parameter conversion model, and outputting the number of the power transmission line poles and the geographic coordinates of the power transmission line poles and towers in an image area.
Preferably, determining the power transmission line tower as the final detection target of the yolov5 target detection model according to the second judgment result, specifically including:
if the second judgment result is that no overlap exists in the prediction frames in the yolov5 target detection model, taking the power transmission line tower in the prediction frames in the yolov5 target detection model as the power transmission line tower of the final detection target of the yolov5 target detection model;
if the second judgment result is that the prediction frames in the yolov5 target detection model are overlapped, judging whether the prediction frames with the overlapped areas are positioned in the same picture;
if the prediction frames containing the overlapping areas are in the same picture, all the transmission line towers in the prediction frames are used as the transmission line towers of the final detection target of the yolov5 target detection model;
and if the prediction frames containing the overlapping areas are not in the same picture, all the power transmission line towers with the maximum confidence in the prediction frames are used as the power transmission line towers of the final detection target of the yolov5 target detection model.
Preferably, the first and second liquid crystal materials are,
wherein the first coordinate conversion processing is performed by using a formula (1);
the formula (1) is:
wherein, XminThe horizontal coordinate of the upper left corner point of the picture in the image data cut according to the preset overlapping degree;
Yminto cut according to a predetermined degree of overlapThe vertical coordinate of the upper left corner point of the picture in the subsequent image data;
Xmaxthe horizontal coordinate of the lower right corner point of the picture in the image data cut according to the preset overlapping degree;
Ymaxthe vertical coordinate of the lower right corner point of the picture in the image data cut according to the preset overlapping degree;
w is the width of each picture in the image data cut according to the preset overlapping degree;
h is the height of each picture in the image data cut according to the preset overlapping degree;
x is the normalized central abscissa of the prediction box;
y is the normalized central ordinate of the prediction frame;
w is the normalized width of the prediction box;
h is the normalized height of the prediction box;
adopting a formula (2) to perform second coordinate conversion processing to obtain coordinates in the satellite remote sensing image of the research area to be predicted;
wherein, the formula (2) is:
wherein x is1The horizontal coordinate of the upper left corner point in a satellite remote sensing image of a research area to be predicted;
y1the vertical coordinate of the upper left corner point in the satellite remote sensing image of the research area to be predicted;
x2the horizontal coordinate of the lower right corner point in the satellite remote sensing image of the research area to be predicted;
y2the vertical coordinate of the lower right corner in the satellite remote sensing image of the research area to be predicted;
i is a row number of the position of the picture in the satellite remote sensing image of the research area to be predicted;
j is the column number of the position of the picture in the satellite remote sensing image of the research area to be predicted;
q is a preset overlap.
Preferably, before acquiring the image data clipped according to the preset overlap degree, the method further includes:
preprocessing the satellite remote sensing image data of the research area to obtain preprocessed satellite remote sensing image data of the research area;
the preprocessing comprises the steps of carrying out primary cutting on an area containing the transmission line tower in the whole research area in the satellite remote sensing image data of the research area, and abandoning the area containing no transmission line tower;
cutting the preprocessed satellite remote sensing image data of the research area according to a preset format to obtain a first picture set;
the first picture set comprises a plurality of research area satellite remote sensing image pictures meeting a preset format;
performing image processing on each research area satellite remote sensing image picture meeting a preset format in the first picture set to obtain a second picture set;
the image processing is to process each research area satellite remote sensing image picture meeting a preset format in a first picture set by adopting a gray world mode or an automatic white balance mode or a histogram equalization mode;
taking 60% of pictures in the second picture set as a training set, 20% of pictures as a verification set and 20% of pictures as a test set;
training, testing and verifying the yolov5 target detection model by adopting the training set, the testing set and the verifying set to obtain a trained yolov5 target detection model;
the trained yolov5 target inspection model is the model of the yolov5 target inspection model when the loss function of the yolov5 target inspection model after training with the training set converges.
Preferably, the first and second liquid crystal materials are,
the gray world mode implementation method specifically comprises the following steps:
adjusting remote sensing image channel B by formula (A)nBand component C (B)n′)；
The formula (A) is:
wherein,
wherein,
C(Bn) Is the gray value of each pixel of the band n.
Preferably, the first and second liquid crystal materials are,
the implementation method of the automatic white balance algorithm specifically comprises the following steps:
and transferring the remote sensing image to a Lab color space, performing color cast detection on the image, calculating a color cast value, and transferring to an RGB color space after performing color correction.
Preferably, the first and second liquid crystal materials are,
a frame loss function in the loss functions of the yolov5 target detection model is CIoU;
the CIoU is as follows:
wherein IoU is the intersection ratio of the real box and the predicted box;
c is the diagonal length of the prediction frame;
ρ2(b，bgt) The Euclidean distance between the central points of the prediction frame and the real frame is measured;
alpha is a weight coefficient;
v is a measure of the similarity of aspect ratios.
On the other hand, this embodiment also provides a method and a device for detecting and positioning a power transmission line tower based on satellite remote sensing images, where the device includes:
at least one processor; and
the device comprises at least one memory which is in communication connection with the processor, wherein the memory stores program instructions which can be executed by the processor, and the processor calls the program instructions to execute the satellite remote sensing image power transmission line tower detection and positioning method.
(III) advantageous effects
The invention has the beneficial effects that: according to the method and the device for detecting and positioning the power transmission line tower based on the satellite remote sensing image, due to the fact that the pre-trained target detection model is adopted, the prediction information of each picture in the image data cut according to the preset overlapping degree is obtained, further, the detection and positioning of the power transmission line tower are achieved based on the fact that the prediction information of each picture in the image data cut according to the preset overlapping degree is analyzed and processed, the number of the independent and complete power transmission line towers, the number of the power transmission line towers and the coordinates of the power transmission line towers in the image data cut according to the certain overlapping degree are determined, compared with the prior art, the method and the device enable large-range simultaneous detection to be possible, efficiency is improved, and cost is reduced.
On the other hand, the method and the device for detecting and positioning the power transmission line tower of the satellite remote sensing image use a gray world algorithm or an automatic white balance algorithm or a histogram equalization algorithm for processing the satellite remote sensing image in the research area. Compared with the traditional deep learning target detection algorithm, the method has the advantages that the accuracy rate and the recall rate are obviously improved, the generalization capability of the model is enhanced, and the robustness is higher.
Drawings
FIG. 1 is a flow chart of a method for detecting and positioning a power transmission line tower based on satellite remote sensing images, according to the invention;
FIG. 2 is a schematic diagram of a detection and positioning method for a power transmission line tower based on satellite remote sensing images in an embodiment of the invention;
FIG. 3 is a schematic flow chart of an analysis process in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a detection result of detection by using the method for detecting and positioning the power transmission line tower based on the satellite remote sensing image;
fig. 5 is a schematic diagram of detection details in a detection result detected by the method for detecting and positioning the power transmission line tower based on the satellite remote sensing image.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
In order to better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The invention discloses a method for detecting and positioning a power transmission line tower based on a satellite remote sensing image of deep learning.
The invention innovatively uses a gray world algorithm, an automatic white balance algorithm and a histogram equalization algorithm for the remote sensing image to eliminate the influence of ambient light in the remote sensing image, obtain an image of an original scene, weaken the influence of a light source on a satellite camera sensor, simulate the constancy of a human visual system, design a CPAS detection and positioning method for keeping the space detail information and the space coordinate information of the remote sensing image, complete the detection of the power transmission line tower in the remote sensing image and output the position information of the power transmission line tower. Compared with the traditional method of manual field measurement and visual interpretation, the method has the advantages that large-range simultaneous detection is possible, the efficiency is improved, and the cost is reduced. Compared with the traditional deep learning target detection algorithm, the method has the advantages that the accuracy rate and the recall rate are improved, the generalization capability of the model is enhanced, and the robustness is higher.
Referring to fig. 1, the present embodiment provides a method for detecting and positioning a power transmission line tower through a satellite remote sensing image, including:
and cutting the satellite remote sensing image of the research area to be predicted according to the preset overlapping degree, numbering and recording the position information of each cut image in the satellite remote sensing image of the research area to be predicted, and acquiring the image data cut according to the preset overlapping degree.
And inputting the image data cut according to the preset overlapping degree into a pre-trained target detection model, and acquiring the prediction information of each picture in the image data cut according to the preset overlapping degree.
The prediction information of each picture comprises normalized coordinates and confidence degrees of the center point of the power transmission line tower.
The trained target detection model is a model obtained by training and testing a yolov5 target detection model in advance by adopting a training set and a testing set of satellite remote sensing images.
In practical application of this embodiment, the method further includes:
analyzing and processing the prediction information of each picture in the image data cut according to the preset overlapping degree, and determining independent complete transmission line towers, the number of the transmission line towers and the coordinates of the transmission line towers in the image data cut according to the certain overlapping degree.
Referring to fig. 3, in practical application of this embodiment, analyzing and processing prediction information of each picture in the image data cut according to the preset overlapping degree, and determining independent and complete transmission line towers, the number of transmission line towers, and coordinates of the transmission line towers in the image data cut according to the certain overlapping degree specifically include:
the prediction information of each picture comprises normalized coordinates and confidence degrees of the center point of the power transmission line tower.
And performing first coordinate conversion processing on the normalized coordinates of the central point of the power transmission line tower in each picture to obtain the coordinates in the corresponding picture.
And performing second coordinate conversion processing on the position information of each image in the satellite remote sensing image of the research area to be predicted and the coordinates in each picture to obtain the coordinates in the satellite remote sensing image of the research area to be predicted.
And respectively judging whether the confidence of the center point of the power transmission line tower in the prediction information of each picture is lower than a preset threshold value, acquiring a first judgment result, deleting the power transmission line tower which is used as the detection target of the yolov5 target detection model and has the first judgment result lower than the preset threshold value, and keeping the power transmission line tower which is used as the detection target of the yolov5 target detection model and has the first judgment result larger than or equal to the preset threshold value.
And judging whether the prediction frames in the yolov5 target detection model are overlapped or not, acquiring a second judgment result, and determining the power transmission line tower serving as the final detection target of the yolov5 target detection model according to the second judgment result.
And converting coordinates in the satellite remote sensing image of the research area to be predicted, which corresponds to the power transmission line pole tower of the final detection target of the yolov5 target detection model, into corresponding geographic coordinates by adopting a gdal six-parameter conversion model, and outputting the number of the power transmission line poles and the geographic coordinates of the power transmission line poles and towers in an image area.
In the specific application of this embodiment, the satellite remote sensing images of the transmission line towers of the final detection target of the yolov5 target detection model in the input study area to be predicted are displayed, as shown in fig. 4 and 5, and the number of the transmission line towers in the output image area and the coordinates of the transmission line towers are shown in table one.
Table-image area of power transmission line pole tower number and coordinate information of power transmission line pole tower
In practical application of this embodiment, determining, according to the second determination result, the power transmission line tower serving as the final detection target of the yolov5 target detection model specifically includes:
and if the second judgment result is that no overlap exists in the prediction frames in the yolov5 target detection model, taking the power transmission line tower in the prediction frames in the yolov5 target detection model as the power transmission line tower of the final detection target of the yolov5 target detection model.
And if the second judgment result is that the prediction frames in the yolov5 target detection model are overlapped, judging whether the prediction frames with the overlapped areas are positioned in the same picture.
And if the prediction frames containing the overlapping areas are in the same picture, all the power transmission line towers in the prediction frames are used as the power transmission line towers of the final detection target of the yolov5 target detection model.
And if the prediction frames containing the overlapping areas are not in the same picture, all the power transmission line towers with the maximum confidence in the prediction frames are used as the power transmission line towers of the final detection target of the yolov5 target detection model.
In practical application of the present embodiment, the first coordinate conversion process is performed by using formula (1).
The formula (1) is:
wherein, XminFor the upper left of the picture in the image data cut according to the preset overlapping degreeThe abscissa of the corner point.
YminThe vertical coordinate of the upper left corner point of the picture in the image data cut according to the preset overlapping degree.
YmaxThe horizontal coordinate of the lower right corner point of the picture in the image data cut according to the preset overlapping degree.
XmaxThe vertical coordinate of the lower right corner point of the picture in the image data cut according to the preset overlapping degree.
W is the width of each picture in the image data cut out according to the preset overlap.
H is the height of each picture in the image data cut according to the preset overlapping degree.
x is the normalized center abscissa of the prediction box.
y is the normalized center ordinate of the prediction box.
w is the normalized width of the prediction box.
h is the normalized height of the prediction box.
And (3) performing second coordinate conversion processing by adopting a formula (2) to obtain coordinates in the satellite remote sensing image of the research area to be predicted.
Wherein, the formula (2) is:
wherein x is1Is the horizontal coordinate of the upper left corner point in the satellite remote sensing image of the research area to be predicted.
y1The vertical coordinate of the upper left corner point in the satellite remote sensing image of the research area to be predicted.
x2Is the horizontal coordinate of the lower right corner point in the satellite remote sensing image of the research area to be predicted.
y2For in satellite remote sensing images of the area of investigation to be predictedLower right corner point ordinate.
And i is the line number of the position of the picture in the satellite remote sensing image of the research area to be predicted.
j is the column number of the position of the picture in the satellite remote sensing image of the research area to be predicted.
q is a preset overlap.
Referring to fig. 2, in practical application of the present embodiment, before acquiring the image data clipped according to the preset overlap, the method further includes:
and preprocessing the satellite remote sensing image data of the research area to obtain the preprocessed satellite remote sensing image data of the research area.
And the preprocessing comprises the steps of primarily cutting the whole region containing the transmission line tower in the research region in the satellite remote sensing image data of the research region, and abandoning the region containing no transmission line tower.
In this embodiment, remote sensing images in the research area are downloaded, typical areas containing power transmission line towers in the whole research area are cut to reduce unnecessary work in data set making, and parts of areas such as oceans without power transmission line towers are abandoned.
And cutting the preprocessed satellite remote sensing image data of the research area according to a preset format to obtain a first picture set.
The first picture set comprises a plurality of research area satellite remote sensing image pictures meeting a preset format.
And carrying out image processing on each research area satellite remote sensing image picture meeting a preset format in the first picture set to obtain a second picture set.
And the image processing is to process each research area satellite remote sensing image picture meeting a preset format in the first picture set by adopting a gray world mode or an automatic white balance mode or a histogram equalization mode.
In the specific application of this embodiment, a gray world algorithm, an automatic white balance algorithm, and a histogram equalization process are randomly performed on each of the research area satellite remote sensing image pictures in the first picture set that satisfy the preset format.
And taking 60% of the pictures in the second picture set as a training set, 20% of the pictures as a verification set and 20% of the pictures as a test set.
And training, testing and verifying the yolov5 target detection model by adopting the training set, the testing set and the verifying set to obtain the trained yolov5 target detection model.
The trained yolov5 target inspection model is the model of the yolov5 target inspection model when the loss function of the yolov5 target inspection model after training with the training set converges.
In practical application of this embodiment, the method for implementing the gray world mode specifically includes:
adjusting remote sensing image channel B by formula (A)nBand component C (B)n′)。
The formula (A) is:
wherein,
wherein,
C(Bn) Is the gray value of each pixel of the band n.
In specific application, the gray world algorithm is implemented by the following steps: calculating the average value of pixels of each channel of the remote sensing image as B1，B2，B3Three channels are taken as an example of the case,
recalculating B1，B2，B3Gain factor of three channels
According to the Von Kries diagonal model, adjusting B according to the gray value C of the pixel in the remote sensing image1，B2，B3Band components:
in practical application of this embodiment, the implementation method of the automatic white balance algorithm specifically includes:
and transferring the remote sensing image to a Lab color space, performing color cast detection on the image, calculating a color cast value, and transferring to an RGB color space after performing color correction.
In practical application of this embodiment, a frame loss function in the loss functions of the yolov5 target detection model is CIoU.
The CIoU is as follows:
where IoU is the intersection ratio of the real box and the predicted box.
And C is the diagonal length of the prediction box.
p2(b,bgt) Is used to measure the Euclidean distance between the central point of the predicted frame and the central point of the real frame.
And alpha is a weight coefficient.
v is a measure of the similarity of aspect ratios.
In the specific application of the embodiment, the CIoU integrates the aspect ratio of the prediction frame, the distance between the target and the anchor point (anchor), the overlapping degree, the scale and other factors, so that the regression of the target frame is more stable, and the convergence rate is accelerated.
Deep learning essentially comprises the steps of constructing a machine learning architecture model with multiple hidden layers, and training through large-scale data to obtain a large amount of more representative characteristic information. Therefore, the samples are classified and predicted, and the classification and prediction precision is improved. The process achieves the purpose of feature learning by means of a deep learning model. With the continuous perfection of the deep learning theory and the iterative update of the detection algorithm, the neural network has strong feature extraction capability, and compared with the traditional target detection method, the target detection method based on the deep learning has obvious superiority.
In the embodiment, the detection and positioning of the transmission line tower are realized by using the deep learning method, and compared with the traditional method of manual field measurement and visual interpretation, the large-range simultaneous detection is possible, the efficiency is improved, and the cost is reduced.
In the embodiment, a gray world algorithm, an automatic white balance algorithm and a histogram equalization algorithm are used for processing the satellite remote sensing image in the research area. Compared with the traditional deep learning target detection algorithm, the method has the advantages that the accuracy rate and the recall rate are obviously improved, the generalization capability of the model is enhanced, and the robustness is higher.
The remote sensing image has a larger size and contains coordinate information. Direct-injection model prediction loses a great deal of spatial detail information as well as coordinate information. The method provided by the invention can effectively retain the space detail information and the coordinate information of the remote sensing image through tests.
Since the system described in the above embodiment of the present invention is a system used for implementing the method of the above embodiment of the present invention, based on the method described in the above embodiment of the present invention, a person skilled in the art can understand the specific structure and the modification of the system/apparatus, and thus the detailed description is omitted here. All systems adopted by the method of the above embodiments of the present invention are within the intended scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the terms first, second, third and the like are for convenience only and do not denote any order. These words are to be understood as part of the name of the component.
Furthermore, it should be noted that in the description of the present specification, the description of the term "one embodiment", "some embodiments", "examples", "specific examples" or "some examples", etc., means that a specific feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, the claims should be construed to include preferred embodiments and all changes and modifications that fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include such modifications and variations.
Claims (10)
1. A detection and positioning method for a power transmission line tower based on a satellite remote sensing image is characterized by comprising the following steps:
cutting the satellite remote sensing images of the research area to be predicted according to a preset overlapping degree, numbering and recording the position information of each cut image in the satellite remote sensing images of the research area to be predicted, and acquiring image data cut according to the preset overlapping degree;
inputting the image data cut according to the preset overlapping degree into a pre-trained target detection model, and acquiring the prediction information of each picture in the image data cut according to the preset overlapping degree;
the prediction information of each picture comprises normalized coordinates and confidence degrees of the center point of the power transmission line tower;
the trained target detection model is a model obtained by training and testing a yolov5 target detection model in advance by adopting a training set and a testing set of satellite remote sensing images.
2. The method of claim 1, further comprising:
analyzing and processing the prediction information of each picture in the image data cut according to the preset overlapping degree, and determining independent complete transmission line towers, the number of the transmission line towers and the coordinates of the transmission line towers in the image data cut according to the certain overlapping degree.
3. The method according to claim 2, wherein the analyzing the prediction information of each picture in the image data cut according to the preset overlapping degree to determine the number of independent complete transmission line towers, the number of transmission line towers and the coordinates of the transmission line towers in the image data cut according to the certain overlapping degree specifically comprises:
the prediction information of each picture comprises normalized coordinates and confidence degrees of the center point of the power transmission line tower;
performing first coordinate conversion processing on the normalized coordinates of the central point of the power transmission line tower in each picture to obtain the coordinates in the corresponding picture;
performing second coordinate conversion processing on the position information of each image in the satellite remote sensing image of the research area to be predicted and the coordinates in each picture to obtain the coordinates in the satellite remote sensing image of the research area to be predicted;
respectively judging whether the confidence of the center point of the power transmission line tower in the prediction information of each picture is lower than a preset threshold value, acquiring a first judgment result, deleting the power transmission line tower which is lower than the preset threshold value and is used as a detection target of the yolov5 target detection model, and keeping the power transmission line tower which is larger than or equal to the preset threshold value and is used as a detection target of the yolov5 target detection model;
judging whether the prediction frames in the yolov5 target detection model are overlapped or not, obtaining a second judgment result, and determining the power transmission line tower serving as the final detection target of the yolov5 target detection model according to the second judgment result;
and converting coordinates in the satellite remote sensing image of the research area to be predicted, which corresponds to the power transmission line pole tower of the final detection target of the yolov5 target detection model, into corresponding geographic coordinates by adopting a gdal six-parameter conversion model, and outputting the number of the power transmission line poles and the geographic coordinates of the power transmission line poles and towers in an image area.
4. The method according to claim 3, wherein determining the transmission line tower as the final detection target of the yolov5 target detection model according to the second determination result specifically comprises:
if the second judgment result is that no overlap exists in the prediction frames in the yolov5 target detection model, taking the power transmission line tower in the prediction frames in the yolov5 target detection model as the power transmission line tower of the final detection target of the yolov5 target detection model;
if the second judgment result is that the prediction frames in the yolov5 target detection model are overlapped, judging whether the prediction frames with the overlapped areas are positioned in the same picture;
if the prediction frames containing the overlapping areas are in the same picture, all the transmission line towers in the prediction frames are used as the transmission line towers of the final detection target of the yolov5 target detection model;
and if the prediction frames containing the overlapping areas are not in the same picture, all the power transmission line towers with the maximum confidence in the prediction frames are used as the power transmission line towers of the final detection target of the yolov5 target detection model.
5. The method of claim 4,
wherein the first coordinate conversion processing is performed by using a formula (1);
the formula (1) is:
wherein, XminThe horizontal coordinate of the upper left corner point of the picture in the image data cut according to the preset overlapping degree;
Yminthe vertical coordinate of the upper left corner point of the picture in the image data cut according to the preset overlapping degree;
Xmaxthe horizontal coordinate of the lower right corner point of the picture in the image data cut according to the preset overlapping degree;
Ymaxthe vertical coordinate of the lower right corner point of the picture in the image data cut according to the preset overlapping degree;
w is the width of each picture in the image data cut according to the preset overlapping degree;
h is the height of each picture in the image data cut according to the preset overlapping degree;
x is the normalized central abscissa of the prediction box;
y is the normalized central ordinate of the prediction frame;
w is the normalized width of the prediction box;
h is the normalized height of the prediction box;
adopting a formula (2) to perform second coordinate conversion processing to obtain coordinates in the satellite remote sensing image of the research area to be predicted;
wherein, the formula (2) is:
wherein x is1The horizontal coordinate of the upper left corner point in a satellite remote sensing image of a research area to be predicted;
y1for satellite remote in the research area to be predictedSensing the vertical coordinate of the upper left corner point in the image;
x2the horizontal coordinate of the lower right corner point in the satellite remote sensing image of the research area to be predicted;
y2the vertical coordinate of the lower right corner in the satellite remote sensing image of the research area to be predicted;
i is a row number of the position of the picture in the satellite remote sensing image of the research area to be predicted;
j is the column number of the position of the picture in the satellite remote sensing image of the research area to be predicted;
q is a preset overlap.
6. The method of claim 5, wherein prior to acquiring the image data cropped to the predetermined degree of overlap, the method further comprises:
preprocessing the satellite remote sensing image data of the research area to obtain preprocessed satellite remote sensing image data of the research area;
the preprocessing comprises the steps of carrying out primary cutting on an area containing the transmission line tower in the whole research area in the satellite remote sensing image data of the research area, and abandoning the area containing no transmission line tower;
cutting the preprocessed satellite remote sensing image data of the research area according to a preset format to obtain a first picture set;
the first picture set comprises a plurality of research area satellite remote sensing image pictures meeting a preset format;
performing image processing on each research area satellite remote sensing image picture meeting a preset format in the first picture set to obtain a second picture set;
the image processing is to process each research area satellite remote sensing image picture meeting a preset format in a first picture set by adopting a gray world mode or an automatic white balance mode or a histogram equalization mode;
taking 60% of pictures in the second picture set as a training set, 20% of pictures as a verification set and 20% of pictures as a test set;
training, testing and verifying the yolov5 target detection model by adopting the training set, the testing set and the verifying set to obtain a trained yolov5 target detection model;
the trained yolov5 target inspection model is the model of the yolov5 target inspection model when the loss function of the yolov5 target inspection model after training with the training set converges.
7. The method of claim 6,
the gray world mode implementation method specifically comprises the following steps:
adjusting remote sensing image channel B by formula (A)nBand component C (B)n′)；
The formula (A) is:
wherein,
wherein,
C(Bn) Is the gray value of each pixel of the band n.
8. The method of claim 7,
the implementation method of the automatic white balance algorithm specifically comprises the following steps:
and transferring the remote sensing image to a Lab color space, performing color cast detection on the image, calculating a color cast value, and transferring to an RGB color space after performing color correction.
9. The method of claim 8,
a frame loss function in the loss functions of the yolov5 target detection model is CIoU;
the CIoU is as follows:
wherein IoU is the intersection ratio of the real box and the predicted box;
c is the diagonal length of the prediction frame;
ρ2(b，bgt) The Euclidean distance between the central points of the prediction frame and the real frame is measured;
alpha is a weight coefficient;
v is a measure of the similarity of aspect ratios.
10. A detection and positioning method device for a power transmission line tower based on a satellite remote sensing image is characterized by comprising the following steps:
at least one processor; and
at least one memory communicatively connected to the processor, wherein the memory stores program instructions executable by the processor, and the processor calls the program instructions to perform the method according to any one of claims 1 to 9.
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