CN114428518A - Fan orientation method for automatic inspection of fan blade of unmanned aerial vehicle - Google Patents
Fan orientation method for automatic inspection of fan blade of unmanned aerial vehicle Download PDFInfo
- Publication number
- CN114428518A CN114428518A CN202111497724.8A CN202111497724A CN114428518A CN 114428518 A CN114428518 A CN 114428518A CN 202111497724 A CN202111497724 A CN 202111497724A CN 114428518 A CN114428518 A CN 114428518A
- Authority
- CN
- China
- Prior art keywords
- fan
- image
- orientation
- positive
- unmanned aerial
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000007689 inspection Methods 0.000 title claims abstract description 30
- 238000012545 processing Methods 0.000 claims abstract description 30
- 230000000877 morphologic effect Effects 0.000 claims description 13
- 230000009466 transformation Effects 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 8
- 238000001914 filtration Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 4
- 230000009471 action Effects 0.000 claims description 3
- 238000000926 separation method Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 230000002567 autonomic effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/12—Target-seeking control
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Image Analysis (AREA)
- Structures Of Non-Positive Displacement Pumps (AREA)
Abstract
The invention discloses a fan orientation method for automatic inspection of fan blades of an unmanned aerial vehicle, which can quickly and accurately estimate the true orientation angle of a fan, is applied to the unmanned aerial vehicle inspection operation of a plurality of wind power plants such as a source-off and edge-fixing fields, has the estimation precision within 1 degree and has strong practical application value. The whole fan orientation method has strong scene adaptability and can be suitable for any fan orientation and blade position. Compared with the existing fan orientation method based on the laser radar, the method has the advantages of simplicity and convenience in operation, high processing speed and the like.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle application, and particularly relates to a fan orientation method for automatic inspection of fan blades of an unmanned aerial vehicle.
Background
With the rapid development of new energy industry in China, clean energy represented by wind power generation has the characteristics of small pollution, renewability and the like, the original energy structure is gradually improved, and the method plays a positive role in promoting the sustainable development of regional economy and society. However, with the continuous expansion of the scale of the wind power plant, the requirement of the later safety maintenance of the equipment also increases at a high speed, and the limited inspection manpower and the complex inspection environment greatly influence the operation efficiency and the safety of the equipment maintenance. Therefore, it is very important to find an efficient and safe fan inspection mode. Many rotor unmanned aerial vehicle has characteristics such as mobility height, strong adaptability, carries on the operation through carrying on loads such as visible light camera, infrared camera or laser radar to with the help of automatic route planning algorithm, can ensure that unmanned aerial vehicle is autonomic to carry out data acquisition, guarantee front end data acquisition's rapidity and reliability effectively, help patrolling and examining the high-efficient accurate discovery of personnel and handle the sand hole that exists on the fan blade, exception such as gel coat damage.
For the front-end data acquisition end of the whole unmanned aerial vehicle fan blade inspection, fan orientation and fan blade positioning technologies are the prerequisites for ensuring air route planning accuracy, and fan orientation is the most important, so that whether the unmanned aerial vehicle can accurately fly right ahead of a fan plane to shoot a front view of the fan is directly determined. At present, few unmanned aerial vehicle fan blade patrols and examines solution merchants (like compound sub-intelligence, expand and beat the intelligence) on the market all use lidar to carry out blade location navigation, though the accuracy of this mode is higher, nevertheless because the fan size is very big, adopt lidar to carry out the consuming time of data acquisition in earlier stage and processing longer, have the restriction to the high timeliness demand of this business. Therefore, it is very important to find a more concise and rapid fan orientation method.
Disclosure of Invention
The invention aims to overcome the technical problems in the background art and provide a fan orientation method for automatic inspection of a fan blade of an unmanned aerial vehicle, which can well utilize the obvious color characteristics of the fan blade in the environment, realize accurate and reliable fan orientation function through a simple image processing mode and effectively ensure the efficient implementation of the automatic inspection process of the whole fan blade of the unmanned aerial vehicle.
In order to solve the technical problem, the technical scheme of the invention is as follows:
a fan orientation method for automatic inspection of unmanned aerial vehicle fan blades, the method comprising:
carrying out image processing on an input image to obtain a binary image with noise points removed;
fitting the straight line in the binarized image after the noise points are removed by using a Hough transform straight line detection method, and taking the straight line corresponding to the maximum voting number obtained after fitting as a connecting line of the fan blade main body;
for the connection line of the fan blade main body, obtaining the rotation angle of the fan blade in the image by using the slope of the straight line;
performing Euclidean transformation on the binarized image without the noise points based on the rotation angle in the image and cutting a target area to obtain an image of the whole part of the fan blade;
equally dividing the whole partial image of the fan blade to obtain a main body part image and a cabin partial image of the fan blade, and counting the foreground number of the main body part and the cabin part of the fan blade;
judging the positive and negative directions of the fan according to the foreground numbers of the main body part and the cabin part of the fan blade;
and outputting a final orientation angle value by using the positive and negative judgment result of the orientation of the fan, and finishing the whole fan orientation process.
The color characteristics of the fan blade in the environment can be well utilized, the accurate and reliable fan orientation function is realized through a simple image processing mode, the efficient proceeding of the automatic inspection flow of the fan blade of the whole unmanned aerial vehicle is effectively guaranteed, the real orientation angle of the fan can be quickly and accurately estimated, the method is already applied to the unmanned aerial vehicle inspection operation of a plurality of wind power plants such as source placement, edge positioning and the like, the estimation precision can reach within 1 degree, and the method has a strong practical application value. The whole fan orientation method has strong scene adaptability and can be suitable for any fan orientation and blade position. Compared with the existing fan orientation method based on the laser radar, the method has the advantages of simplicity and convenience in operation, high processing speed and the like.
Further, the image processing of the input image specifically includes:
s1: extracting a brightness channel of an input image, and counting the mean value and the standard deviation of a gray level image under the brightness channel;
s2: based on the mean value and the standard deviation of the gray level image, filtering the input image by using a sliding window to obtain a gray level image with separated foreground and background;
s3: carrying out binarization processing on the gray-scale image with the foreground and background separated by using an Otsu method to obtain a gray-scale image after binarization processing;
s4: and performing morphological opening and closing operation processing on the gray-scale image subjected to binarization processing to obtain a binarization image from which noise points are removed.
Further, the morphological opening and closing operation processing includes:
and removing small discrete regions in the binary-processed gray-scale image by using a morphological open operation, and further completing gaps of large regions in the binary-processed gray-scale image by using a morphological close operation.
Further, the input image is a top view of the unmanned aerial vehicle collected right above the fan.
Further, before the image processing of the input image, the method further comprises:
firstly, judging the pitch angle of an input top view collected right above the fan, if the deviation of the input top view and the vertical direction is within +/-10 degrees, judging the top view to be an available top view, and entering the subsequent processing.
Further, the specific process of performing the euclidean transformation and clipping the target region includes:
taking the image center of the binarized image without the noise points as a rotation center, and taking a rotation angle obtained by the slope of the straight line in the step as an amount to be rotated, so as to calculate a corresponding rotation matrix;
then, under the action of the rotation matrix, performing Euclidean transformation on the binarized image without the noise points to rotate the fan body in the image from inclination to horizontal;
and (4) obtaining the outline of the rotated binary image, cutting and storing all horizontal areas of the rectangle corresponding to the maximum outline.
Further, it specifically includes to carry out positive and negative judgement of fan orientation:
if the foreground number of the main body part of the fan blade is larger than that of the cabin part, the fan blade is arranged under the upper cabin, and if the foreground number of the main body part of the fan blade is smaller than that of the cabin part, the fan blade is arranged under the lower cabin, so that the positive and negative direction judgment of the fan is completed.
Further, outputting a final orientation angle value by utilizing a positive and negative judgment result of the orientation of the fan; the method specifically comprises the following steps:
after positive and negative judgment of the fan orientation, determining whether to add 180-degree compensation according to different orientations of the fan orientation so as to obtain an included angle beta between the fan orientation and the positive direction of the image, wherein the included angle alpha between the positive direction of the image and the positive geographical north direction can be directly read from a pan-tilt yaw angle in the image attribute, and therefore the included angle gamma between the positive direction of the hub and the positive geographical north direction is the sum of the included angle alpha and the included angle beta, and the final orientation angle belongs to a range of (-180 degrees to +180 degrees); and correcting the final orientation angle, wherein the final orientation angle gamma meets the following formula:
compared with the prior art, the invention has the advantages that:
a fan orientation method for automatic inspection of fan blades of unmanned aerial vehicles can well utilize the color characteristics of the fan blades, which are obvious in the environment, and realize an accurate and reliable fan orientation function through a simple image processing mode, so that the efficient implementation of the automatic inspection process of the fan blades of the whole unmanned aerial vehicles is effectively ensured, the real orientation angle of the fan can be quickly and accurately estimated, the method is already applied to the unmanned aerial vehicle inspection operation of a plurality of wind power fields such as source-to-source and edge-fixing, the estimation precision can reach within 1 degree, and the method has a strong practical application value. The whole fan orientation method has strong scene adaptability and can be suitable for any fan orientation and blade position. Compared with the existing fan orientation method based on the laser radar, the method has the advantages of simplicity and convenience in operation, high processing speed and the like.
Drawings
FIG. 1 is a flow chart of the front end operation of automatic inspection of unmanned aerial vehicle fan blades;
FIG. 2 is a flow chart of a fan orientation method;
FIG. 3 is a top view of a fan acquired directly above the fan using a M300 drone in Da Jiang;
FIG. 4 is a schematic view of three angles involved in the fan orientation method;
FIG. 5 is a gray scale diagram of the fan body to be selected obtained through foreground and background separation;
FIG. 6 is a comparison graph of noise rejection results before and after morphological processing;
FIG. 7 is a fan blade body wiring diagram obtained through Hough transform line fitting;
FIG. 8 is a cut out view of the fan body area;
FIG. 9 is a view of the blade body (upper) and the rear of the nacelle (lower) divided up and down;
FIG. 10 is a diagram of fan orientation results in an actual fan inspection operation.
Detailed Description
The following describes embodiments of the present invention with reference to examples:
it should be noted that the structures, proportions, sizes, and other elements shown in the specification are included for the purpose of understanding and reading only, and are not intended to limit the scope of the invention, which is defined by the claims, and any modifications of the structures, changes in the proportions and adjustments of the sizes, without affecting the efficacy and attainment of the same.
In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are used for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms may be changed or adjusted without substantial change in the technical content.
The first embodiment is as follows:
the invention provides a fan orientation method for automatic inspection of fan blades of an unmanned aerial vehicle, which is mainly applied to a front-end data acquisition end for inspection of the fan blades of the unmanned aerial vehicle, wherein the front-end arrangement operation flow is shown in figure 1. The method comprises the steps of collecting a top view of an unmanned aerial vehicle right above a fan, obtaining a fan orientation angle through algorithm estimation, adopting a simple image processing means in the algorithm, performing foreground and background separation by using obvious color information of a fan main body, detecting and fitting by using Hough transformation straight lines to obtain straight lines with the highest voting number, judging the positive and negative orientation, and finally obtaining the fan orientation angle.
The unmanned aerial vehicle platform adopted by the invention is Dajiang M300, and the Zen Si P1 full-frame camera is carried, so that reliable guarantee is provided for safe flight and data acquisition. Specifically, firstly, judging the pitch angle of an input top view collected right above the fan, if the deviation of the input top view from the vertical direction is within +/-10 degrees, judging the top view to be an available top view, entering subsequent processing, and otherwise, exiting; then, color space conversion is carried out on the image, the image is converted from an RGB space to an HSV space, a brightness channel is extracted, and the mean value and the standard deviation of the gray-scale image under the channel are counted; filtering the gray level image by using sliding windows with equal step length, reserving a local window with more than two standard deviations, and realizing the front background separation of the fan main body and the surrounding environment in the image; then carrying out binarization on the filtered gray-scale image by using an Otsu method, and filtering out the existing noise by using morphological open-close operation; then fitting straight lines in the image by using a Hough transform straight line detection method, and taking the straight line corresponding to the maximum voting number as a connecting line of the fan blade main body, so that the rotation angle of the fan plane in the image can be obtained at the moment; then, carrying out European-style transformation on the image by using the rotation angle, and cutting to obtain a main body part of the fan blade; and finally, judging the positive and negative orientation of the fan by using the foreground proportion size of the main body of the blade, outputting a final orientation angle value, and ending the whole fan orientation process.
The second embodiment:
the invention provides a fan orientation method for automatic fan blade inspection of an unmanned aerial vehicle, which is in an extremely important position in the whole fan inspection business, as shown in figure 1, an automatic route planning algorithm needs to obtain an accurate route according to fan orientation and a fan blade positioning technology, and the fan positioning technology directly determines whether the unmanned aerial vehicle can accurately fly right in front of a fan plane to shoot a front view of the fan. Therefore, the main problem to be solved by the invention is to find a simple and reliable fan orientation method.
As shown in fig. 2, the whole fan orientation process includes seven main parts: extracting a brightness channel of an input image, counting the mean value and the standard deviation of the brightness channel, realizing foreground and background separation by utilizing sliding window filtering, carrying out morphological opening and closing operation on a binary image, fitting a straight line corresponding to the maximum voting number through Hough transformation straight line detection, carrying out Euclidean transformation on the image, cutting a target area, judging the positive and negative directions of a fan, and outputting a final direction angle value. During operation, a full-frame camera of Zen P1 mounted on an M300 unmanned aerial vehicle of Dajiang is used for shooting a top view of the wind turbine directly above the wind turbine, as shown in FIG. 3, the XMP attribute of the image comprises three attitude angles of pitching, rolling, yawing and the like of a holder, if the deviation between the pitching angle and the vertical direction is within +/-10 degrees, the usable top view is judged, and the subsequent processing is carried out. Then, recording the yaw angle of the pan-tilt, namely, reading the included angle α between the positive direction of the image and the positive geographical north direction, but because the included angle β still exists between the orientation of the fan and the positive direction of the image, the included angle γ between the orientation of the fan and the positive geographical north direction needs to be obtained according to the included angle α and the included angle β, namely, the fan orientation result. Since the included angle α is known and the included angle β is unknown, the main body of the fan orientation works to estimate the angle β, and the schematic diagram of each angle is shown in fig. 4.
After ensuring the basic information of the input image, the method is implemented by the following steps:
specifically, as step S1: and extracting the brightness channel of the input image and counting the mean value and the standard deviation of the brightness channel. The method comprises the steps of converting an input image from an RGB color space to an HSV color space (Hue, Saturation and brightness), extracting a brightness channel in the RGB color space, wherein the brightness channel describes the brightness change of the image, and can better reflect the difference between a fan body and the surrounding environment. And then, counting the mean value and the standard deviation of the gray level image under the channel to reflect the gray level change condition of the whole image.
Specifically, as step S2: and realizing foreground and background separation by utilizing sliding window filtering. Because the standard deviation reflects the discrete degree of the data, for the main body of the fan with more obvious brightness characteristics, the screening of the fan region in the image can be realized by screening the image blocks with larger local region brightness in a sliding window mode. Specifically, a window of size N x N is selected as the sliding window, again with a sliding step size N in the horizontal and vertical directions, where N is 1/200 of the original image height. For each window region, the pixel mean of the region is counted, and if it is smaller than the sum of the grayscale image mean and twice the standard deviation in step S1, the window region is set to zero. And finally, after the whole image is circularly traversed, the remaining image is the fan main body to be selected, so that the front background separation of the fan main body and the surrounding environment is preliminarily realized, as shown in fig. 5.
Specifically, as step S3: and performing morphological opening and closing operation on the binary image. Since noise may exist in the filtered gray-scale image, the noise needs to be removed to ensure the accuracy of the subsequent calculation result. Specifically, the gray map is binarized by using the Otsu method, then the small discrete regions are removed by using morphological open operation in sequence, and gaps of the large regions are further supplemented by using closed operation. Through this step, noise points can be better removed, as shown in fig. 6.
Specifically, as step S4: and detecting and fitting a straight line corresponding to the maximum voting number by utilizing a Hough transformation straight line. The fan blade main body is most prominent in the binary image after noise point removal, and for a fan top view, the main body orientation is vertical to a blade connecting line, so that the fan orientation angle can be reversely deduced by determining the deflection angle of the blade connecting line. Because the blades are generally longer and are basically distributed in a straight line, the number of long straight lines which can be fitted is the largest theoretically, according to the characteristic, the straight lines which may exist in the binary image can be fitted by using Hough transform, and after the polar coordinate resolution is set, the whole binary image is traversed, so that all straight lines meeting the length requirement can be obtained. Then, a straight line with the same polar coordinates having the most occurrence times (i.e., the maximum number of votes) is selected as a main connecting line of the fan blade, so as to obtain the rotation angle of the fan plane in the image according to the slope of the straight line, as shown in fig. 7.
Specifically, as step S5: and performing Euclidean transformation on the image and cutting out the target area. Although the rotation angle of the blade body in the image can be obtained through step S4, the direction of the rotation angle cannot be determined, and therefore this step is intended to prepare for subsequent determination of the fan direction. Specifically, the image center is used as the rotation center, and the rotation angle obtained by the slope of the straight line in the above steps is used as the rotation amount to be rotated, so that the corresponding rotation matrix can be easily calculated; and then, under the action of the rotation matrix, performing Euclidean transformation on the binary image to enable the fan body in the image to rotate from inclination to horizontal. In order to further avoid the influence outside the fan body, the contour of the rotated binary image is obtained, and all horizontal areas of the rectangle corresponding to the maximum contour are cut and stored, so that the cutting of the fan body part can be completed, as shown in fig. 8.
Specifically, as step S6: and judging whether the fan faces forward or backward. Since the positive and negative directions of the fan orientation bring 180-degree deviation to the final result, the fan main body binary diagram stored in step S5 can be equally divided into an upper half part and a lower half part, and at this time, the rear part of the nacelle and the blade main body can be distinguished, as shown in fig. 9; the foreground number sum in the upper region and the lower region is counted respectively, if the upper half region is larger, the blade is considered to be under the upper engine room, otherwise, the blade is under the lower engine room, and therefore positive and negative judgment of the fan orientation is completed.
Specifically, as step S7: the final orientation angle value is output. After positive and negative judgment of the fan orientation, whether 180-degree compensation is added or not can be determined according to different directions, so that an included angle beta between the fan orientation and the positive direction of the image is obtained. Because the included angle alpha between the positive direction of the image and the positive geographical north direction can be directly read from the yaw angle of the holder in the image attribute, the included angle gamma between the positive direction of the hub and the positive geographical north direction is the sum of the included angle alpha and the included angle beta. Considering that the final orientation angle belongs to the range of (-180 degrees to +180 degrees), the result thereof needs to be corrected, and therefore the final orientation angle γ satisfies the following formula:
in conclusion, through the seven steps, the real orientation angle of the fan can be quickly and accurately estimated, and the whole fan orientation method has strong scene adaptability and can be suitable for any fan orientation and any blade position. Compared with the existing fan orientation method based on the laser radar, the method has the advantages of simplicity and convenience in operation, high processing speed and the like.
Particularly, as shown in fig. 10, in the actual scene verification, the fan orientation method provided by the invention has been applied to the unmanned aerial vehicle inspection operation of a plurality of wind power plants such as the source-off and edge-fixing, the estimation accuracy can reach within 1 degree, and the method has a strong practical application value.
While the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Many other changes and modifications can be made without departing from the spirit and scope of the invention. It is to be understood that the invention is not to be limited to the specific embodiments, but only by the scope of the appended claims.
Claims (8)
1. A fan orientation method for automatic inspection of unmanned aerial vehicle fan blades, the method comprising:
carrying out image processing on an input image to obtain a binary image with noise points removed;
fitting the straight line in the binarized image after the noise points are removed by using a Hough transform straight line detection method, and taking the straight line corresponding to the maximum voting number obtained after fitting as a connecting line of the fan blade main body;
for the connection line of the fan blade main body, obtaining the rotation angle of the fan blade in the image by using the slope of the straight line;
performing Euclidean transformation on the binarized image without the noise points based on the rotation angle in the image and cutting a target area to obtain an image of the whole part of the fan blade;
equally dividing the whole partial image of the fan blade to obtain a main part image of the fan blade and a cabin partial image, and counting the number of the foregrounds of the main part of the fan blade and the number of the foregrounds of the cabin part;
judging the positive and negative directions of the fan according to the foreground numbers of the main body part and the cabin part of the fan blade;
and outputting a final orientation angle value by using the positive and negative judgment result of the orientation of the fan, and finishing the whole fan orientation process.
2. The fan orientation method for automatic inspection of unmanned aerial vehicle fan blades according to claim 1, wherein the image processing of the input image specifically comprises:
s1: extracting a brightness channel of an input image, and counting the mean value and the standard deviation of a gray level image under the brightness channel;
s2: based on the mean value and the standard deviation of the gray level image, filtering the input image by using a sliding window to obtain a gray level image with separated foreground and background;
s3: carrying out binarization processing on the gray-scale image with the foreground and background separated by using an Otsu method to obtain a gray-scale image after binarization processing;
s4: and performing morphological opening and closing operation processing on the gray-scale image subjected to the binarization processing to obtain a binarization image from which noise points are removed.
3. The fan orientation method for automatic inspection of unmanned aerial vehicle fan blades according to claim 2, wherein the morphological opening and closing operation processing comprises:
and removing small discrete regions in the binary-processed gray-scale image by using a morphological open operation, and further completing gaps of large regions in the binary-processed gray-scale image by using a morphological close operation.
4. The method of claim 1, wherein the input image is a top view of the UAV taken directly above the fan.
5. The method of claim 4, wherein prior to image processing the input image, the method further comprises:
firstly, judging the pitch angle of an input top view collected right above the fan, if the deviation of the input top view and the vertical direction is within +/-10 degrees, judging the top view to be an available top view, and entering the subsequent processing.
6. The fan orientation method for the automatic inspection of the fan blades of the unmanned aerial vehicle according to claim 1, wherein the specific process of performing Euclidean transformation and cutting the target area comprises the following steps:
taking the image center of the binarized image without the noise points as a rotation center, and taking a rotation angle obtained by the slope of the straight line in the step as an amount to be rotated, so as to calculate a corresponding rotation matrix;
under the action of the rotation matrix, performing Euclidean transformation on the binarized image without the noise points to rotate the fan main body in the image from inclination to horizontal;
and (4) obtaining the outline of the rotated binary image, cutting and storing all horizontal areas of the rectangle corresponding to the maximum outline.
7. The fan orientation method for automatic inspection of unmanned aerial vehicle fan blades according to claim 1, wherein the determining whether the fan orientation is positive or negative specifically comprises:
if the foreground number of the main body part of the fan blade is larger than that of the cabin part, the fan blade is arranged under the upper cabin, and if the foreground number of the main body part of the fan blade is smaller than that of the cabin part, the fan blade is arranged under the lower cabin, so that the positive and negative direction judgment of the fan is completed.
8. The fan orientation method for automatic inspection of fan blades of unmanned aerial vehicles according to claim 1, characterized in that a final orientation angle value is output by using a judgment result of positive and negative orientation of the fan; the method specifically comprises the following steps:
after positive and negative judgment of the fan orientation, determining whether to add 180-degree compensation according to different orientations of the fan orientation so as to obtain an included angle beta between the fan orientation and the positive direction of the image, wherein the included angle alpha between the positive direction of the image and the positive geographical north direction can be directly read from a pan-tilt yaw angle in the image attribute, and therefore the included angle gamma between the positive direction of the hub and the positive geographical north direction is the sum of the included angle alpha and the included angle beta, and the final orientation angle belongs to a range of (-180 degrees to +180 degrees); and correcting the final orientation angle, wherein the final orientation angle gamma meets the following formula:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111497724.8A CN114428518B (en) | 2021-12-09 | 2021-12-09 | Fan orientation method for automatic inspection of unmanned aerial vehicle fan blade |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111497724.8A CN114428518B (en) | 2021-12-09 | 2021-12-09 | Fan orientation method for automatic inspection of unmanned aerial vehicle fan blade |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114428518A true CN114428518A (en) | 2022-05-03 |
| CN114428518B CN114428518B (en) | 2024-03-22 |
Family
ID=81311240
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111497724.8A Active CN114428518B (en) | 2021-12-09 | 2021-12-09 | Fan orientation method for automatic inspection of unmanned aerial vehicle fan blade |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114428518B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115908049A (en) * | 2022-11-18 | 2023-04-04 | 众芯汉创（北京）科技有限公司 | Fan blade hovering position dynamic identification system based on intelligent identification |
| CN116912548A (en) * | 2023-07-19 | 2023-10-20 | 北京图知天下科技有限责任公司 | Blade image processing method based on fan dynamic inspection |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE202017103924U1 (en) * | 2017-06-30 | 2017-07-21 | Ebm-Papst Mulfingen Gmbh & Co. Kg | blower |
| CN107491742A (en) * | 2017-07-28 | 2017-12-19 | 西安因诺航空科技有限公司 | Stable unmanned plane target tracking when a kind of long |
| JP2018021491A (en) * | 2016-08-02 | 2018-02-08 | 株式会社日立製作所 | System and flight route generating method |
| CN108733079A (en) * | 2018-06-19 | 2018-11-02 | 上海扩博智能技术有限公司 | Automatic detecting flight path is carried out to wind turbine by unmanned plane and determines method and system |
| CN111289533A (en) * | 2020-02-25 | 2020-06-16 | 国电电力内蒙古新能源开发有限公司 | Fan blade inspection method and device, unmanned aerial vehicle and storage medium |
| JP2020118141A (en) * | 2019-01-28 | 2020-08-06 | 株式会社日立製作所 | Blade inspection system of wind power generator, wind power generation system, remote integrated monitor system of wind farm |
| CN112882487A (en) * | 2019-11-29 | 2021-06-01 | 北京国电思达科技有限公司 | Unmanned aerial vehicle inspection track generation method, inspection method and system for fan blade |
| CN112904877A (en) * | 2021-01-14 | 2021-06-04 | 星闪世图(台州)科技有限公司 | Automatic fan blade inspection system and method based on unmanned aerial vehicle |
| CN113342041A (en) * | 2021-06-16 | 2021-09-03 | 河北华电沽源风电有限公司 | Method and system for realizing automatic inspection of fan blade power generation equipment by unmanned aerial vehicle |
| CN113723192A (en) * | 2021-07-30 | 2021-11-30 | 鹏城实验室 | Blade image acquisition method in running state of fan |
-
2021
- 2021-12-09 CN CN202111497724.8A patent/CN114428518B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2018021491A (en) * | 2016-08-02 | 2018-02-08 | 株式会社日立製作所 | System and flight route generating method |
| DE202017103924U1 (en) * | 2017-06-30 | 2017-07-21 | Ebm-Papst Mulfingen Gmbh & Co. Kg | blower |
| CN107491742A (en) * | 2017-07-28 | 2017-12-19 | 西安因诺航空科技有限公司 | Stable unmanned plane target tracking when a kind of long |
| CN108733079A (en) * | 2018-06-19 | 2018-11-02 | 上海扩博智能技术有限公司 | Automatic detecting flight path is carried out to wind turbine by unmanned plane and determines method and system |
| JP2020118141A (en) * | 2019-01-28 | 2020-08-06 | 株式会社日立製作所 | Blade inspection system of wind power generator, wind power generation system, remote integrated monitor system of wind farm |
| CN112882487A (en) * | 2019-11-29 | 2021-06-01 | 北京国电思达科技有限公司 | Unmanned aerial vehicle inspection track generation method, inspection method and system for fan blade |
| CN111289533A (en) * | 2020-02-25 | 2020-06-16 | 国电电力内蒙古新能源开发有限公司 | Fan blade inspection method and device, unmanned aerial vehicle and storage medium |
| CN112904877A (en) * | 2021-01-14 | 2021-06-04 | 星闪世图(台州)科技有限公司 | Automatic fan blade inspection system and method based on unmanned aerial vehicle |
| CN113342041A (en) * | 2021-06-16 | 2021-09-03 | 河北华电沽源风电有限公司 | Method and system for realizing automatic inspection of fan blade power generation equipment by unmanned aerial vehicle |
| CN113723192A (en) * | 2021-07-30 | 2021-11-30 | 鹏城实验室 | Blade image acquisition method in running state of fan |
Non-Patent Citations (3)
| Title |
|---|
| O MOOLAN-FEROZE 等: "Simultaneous drone localisation and wind turbine model fitting during autonomous surface inspection", 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 4 November 2019 (2019-11-04), pages 2014 - 2021, XP033695819, DOI: 10.1109/IROS40897.2019.8968247 * |
| 毛希玮;徐莹莹;: "基于无人机的风机叶片缺陷自动检测技术", 复合材料科学与工程, no. 09, 15 September 2020 (2020-09-15), pages 87 - 91 * |
| 汪强;张元明;: "基于翼型剖面重心调节的风机叶片优化设计", 航空工程进展, no. 02, 25 May 2010 (2010-05-25), pages 52 - 56 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115908049A (en) * | 2022-11-18 | 2023-04-04 | 众芯汉创（北京）科技有限公司 | Fan blade hovering position dynamic identification system based on intelligent identification |
| CN115908049B (en) * | 2022-11-18 | 2023-11-21 | 众芯汉创（北京）科技有限公司 | Fan blade hovering position dynamic identification system based on intelligent identification |
| CN116912548A (en) * | 2023-07-19 | 2023-10-20 | 北京图知天下科技有限责任公司 | Blade image processing method based on fan dynamic inspection |
| CN116912548B (en) * | 2023-07-19 | 2024-07-09 | 北京图知天下科技有限责任公司 | Blade image processing method based on fan dynamic inspection |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114428518B (en) | 2024-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112633535B (en) | Intelligent inspection method and system for photovoltaic power station based on unmanned aerial vehicle image | |
| CN110244766B (en) | Planning method and system for unmanned aerial vehicle routing inspection route of photovoltaic power station | |
| CN104766058B (en) | A kind of method and apparatus for obtaining lane line | |
| CN110163177B (en) | Unmanned aerial vehicle automatic sensing and identifying method for wind turbine generator blades | |
| CN106326892B (en) | Visual landing pose estimation method of rotary wing type unmanned aerial vehicle | |
| CN109002048B (en) | Multi-rotor unmanned aerial vehicle large-scale centralized photovoltaic power station image data acquisition method | |
| CN110619329B (en) | Carriage number and loading state identification method of railway freight open wagon based on airborne vision | |
| CN114428518B (en) | Fan orientation method for automatic inspection of unmanned aerial vehicle fan blade | |
| CN109270534A (en) | A kind of intelligent vehicle laser sensor and camera online calibration method | |
| CN109936080A (en) | A kind of method of unmanned plane inspection transmission line of electricity | |
| WO2020083103A1 (en) | Vehicle positioning method based on deep neural network image recognition | |
| CN105373135A (en) | Method and system for guiding airplane docking and identifying airplane type based on machine vision | |
| CN110516532B (en) | Unmanned aerial vehicle railway track line identification method based on computer vision | |
| CN108961276B (en) | Distribution line inspection data automatic acquisition method and system based on visual servo | |
| CN113723192A (en) | Blade image acquisition method in running state of fan | |
| Xi et al. | A vision-based inspection strategy for large-scale photovoltaic farms using an autonomous UAV | |
| CN113610749A (en) | Fan blade defect detection method based on neural network | |
| CN115240089A (en) | Vehicle detection method of aerial remote sensing image | |
| CN114972767A (en) | Vehicle track and course angle extraction method based on high-altitude unmanned aerial vehicle video | |
| CN112947526A (en) | Unmanned aerial vehicle autonomous landing method and system | |
| CN116184431A (en) | Unmanned aerial vehicle autonomous navigation positioning map construction method, device and system | |
| CN105335688B (en) | A kind of aircraft model recognition methods of view-based access control model image | |
| CN113256668A (en) | Image segmentation method and device | |
| CN116893685A (en) | Unmanned aerial vehicle route planning method and system | |
| CN115082455A (en) | Photovoltaic assembly positioning and defect detecting method in infrared image based on deep learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |