CN111696071A - Spherical radio telescope reflector node identification system and method - Google Patents
Spherical radio telescope reflector node identification system and method Download PDFInfo
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Abstract
The invention discloses a system and a method for identifying a reflecting surface node of a spherical radio telescope, wherein the system comprises: the image receiving module is used for receiving a reflecting surface image, and the reflecting surface is formed by a plurality of unit panels; the image processing module is used for carrying out image processing on the reflecting surface image so as to identify a slit straight line segment between the unit panels; and the node identification module is used for identifying nodes according to straight line sections of gaps between the unit panels to obtain the measurement coordinates of the nodes of the reflecting surface. By the technical scheme provided by the invention, the measuring efficiency of the reflecting surface can be improved, and the problems of electromagnetic interference caused by the reflecting surface active target, difficulty in maintaining the reflecting surface laser target and the like can be well solved.
Description
Technical Field
The invention relates to a spherical radio telescope node identification technology, in particular to a spherical radio telescope reflecting surface node identification system and method.
Background
The cable net structure of an active reflecting surface of a FAST (Five-rounded-meter Aperture Spherical radio Telescope, 500-meter Aperture Spherical radio Telescope) is huge, the stress condition is complex, when a driving motor pulls a cable net node, the cable net node does not strictly move along the radial direction, and a tangential transverse movement exists, so that the node position is not accurately judged only by the adjustment amount of the driving motor, and the elastic deformation of a down cable bearing huge pulling force is difficult to accurately estimate.
The FAST telescope main body consists of four process systems, namely an active reflecting surface system, a feed source supporting system, a receiver and terminal system and a measuring and controlling system. The active reflecting surface, such as a pan with the same diameter of 500 meters, can collect the electric wave signals radiated by the celestial body, and the signals are received and recorded by the receiver equipment. Fig. 1 is a schematic diagram of a FAST active reflecting surface cable net structure provided by the prior art of the present invention, and as shown in fig. 1, an active reflecting surface system is composed of a hoop beam 101 supported all around, a main cable 102 of a main body structure, a down-cable 103 connected with a ground adjusting surface type, and a panel (not shown in fig. 1) laid on the cable net. The cross-connected steel cable sections form an integral cable net, the cable net grids are triangles with the scale of 11 meters, 2225 cross-connected positions are cable net nodes, each node is connected with 6 cable net main cables, lower pull cables and a driving mechanism are installed at the lower ends of the nodes, and the driving mechanism consists of a driving motor and a telescopic actuator. Under the pulling of the driving mechanism, the lower inhaul cable enables the whole cable net to form an initial spherical surface under the action of prestress; during observation, the length and the tension of the lower inhaul cable are controlled, so that the reflecting surface forms an instantaneous paraboloid in the effective lighting caliber, and the flexible cable is ensured to be not loosened continuously in the whole tensioning and observation process through structural design.
The FAST reflecting surface consists of thousands of unit panels, is supported by a steel cable net, and realizes cable net deformation by controlling nodes of the steel cable net. When the telescope is observed, the node adjustment amount is calculated according to astronomical planning, node measurement feedback information and the like and is issued to the actuator for local control, the position of the node is adjusted through the actuator, active deformation of the reflecting surface is achieved, and an instantaneous paraboloid with the caliber of about 300 meters is formed.
At present, a method of measuring FAST cable network nodes by using a laser total station is adopted, and compared with other technologies, the total station is a mature commercial product and is more suitable for FAST large-scale, multi-target and dynamic measurement tasks. The reflecting surface measurement system formed by the total station is adopted to complete the measurement of the reflecting surface node, and the defects of low measurement efficiency, difficult target maintenance and the like exist.
Disclosure of Invention
The invention aims to provide a system and a method for identifying a reflecting surface node of a spherical radio telescope, which are used for solving the problems of low measuring efficiency of the reflecting surface, difficult target maintenance and the like.
In order to achieve the above object, the present invention provides a spherical radio telescope reflecting surface node identification system, which comprises: the image receiving module is used for receiving a reflecting surface image, and the reflecting surface is formed by a plurality of unit panels; the image processing module is used for carrying out image processing on the reflecting surface image so as to identify a slit straight line segment between the unit panels; and the node identification module is used for identifying nodes according to straight line sections of gaps between the unit panels to obtain the measurement coordinates of the nodes of the reflecting surface.
Preferably, the image processing module includes: the denoising unit is used for carrying out gray level binarization processing on the reflecting surface image to obtain a binarization processing image; the edge detection unit is used for carrying out edge detection on the binaryzation processing image through an edge detection algorithm to obtain image edge pixels; the characteristic straight line extraction unit is used for extracting characteristic straight lines according to the image edge pixels to obtain image edge characteristic straight line segments; and the characteristic straight line de-duplication fitting unit is used for de-duplicating and fitting the image edge characteristic straight line segment to obtain a slit straight line segment between the unit panels.
Preferably, the node identification module includes: the characteristic node extraction unit is used for traversing the slit straight-line segment, judging the position relation of the slit straight-line segment according to the slope of the slit straight-line segment, and calculating the pixel coordinate of the intersection point of the non-parallel slit straight-line segments to obtain an initial characteristic node; and the characteristic node duplicate removal unit is used for traversing the initial characteristic nodes and performing duplicate removal operation according to the distance between the initial characteristic nodes to obtain the measurement coordinates of the reflecting surface nodes.
Preferably, the system for identifying the reflection surface node of the spherical radio telescope provided by the present invention further comprises: the interesting region identification module is used for dividing an interesting region according to a preset rule by taking the measurement coordinate of each reflecting surface node as a center to obtain the interesting region; and the characteristic node fine detection module is used for carrying out image processing on each interested area in the interested areas through the image processing module again and carrying out node identification through the node identification module so as to obtain the accurate coordinates of the reflecting surface nodes.
Preferably, the system for identifying the reflection surface node of the spherical radio telescope provided by the present invention further comprises: the down-sampling module is used for performing down-sampling processing on the reflecting surface image to obtain a down-sampled image; the image processing module is used for carrying out image processing on the down-sampled image so as to identify a straight line segment of a gap between unit panels.
Correspondingly, the invention also provides a spherical radio telescope reflecting surface node identification method, which comprises the following steps: receiving a reflection surface image, the reflection surface being composed of a plurality of unit panels; performing image processing on the reflection surface image to identify a slit straight-line segment between unit panels; and identifying nodes according to straight line segments of gaps among the unit panels to obtain measurement coordinates of the nodes of the reflecting surface.
Preferably, the image processing the reflection surface image to identify a straight line segment of a gap between unit panels includes: carrying out gray level binarization processing on the reflection surface image to obtain a binarization processed image; carrying out edge detection on the binarization processing image through an edge detection algorithm to obtain image edge pixels; extracting a characteristic straight line according to the image edge pixel to obtain an image edge characteristic straight line segment; and carrying out duplication removal and fitting on the image edge feature straight line segment to obtain a slit straight line segment between the unit panels.
Preferably, the obtaining of the measurement coordinates of the reflection surface node by node identification according to the straight line segment of the gap between the unit panels includes: traversing the slit straight-line segments, judging the mutual position relation according to the slope of the slit straight-line segments, and calculating the pixel coordinates of the intersection points of the non-parallel slit straight-line segments to obtain initial characteristic nodes; and traversing the initial feature nodes, and performing duplication elimination operation according to the distance between the initial feature nodes to obtain the measurement coordinates of the reflecting surface nodes.
Preferably, the method for identifying the reflection surface node of the spherical radio telescope provided by the invention further comprises the following steps: respectively using the measurement coordinate of each reflecting surface node as a center, and defining an interested area according to a preset rule to obtain the interested area; and carrying out image processing again and carrying out node identification on each interested area in the interested areas so as to obtain accurate coordinates of the nodes of the reflecting surface.
Preferably, the method for identifying the reflection surface node of the spherical radio telescope provided by the invention further comprises the following steps: performing down-sampling processing on the reflecting surface image to obtain a down-sampled image; wherein the down-sampled image is image processed to identify slit straight line segments between unit panels.
According to the technical scheme provided by the invention, the reflection surface measurement efficiency can be improved, and the problems of electromagnetic interference caused by the reflection surface active target, difficult maintenance of the reflection surface laser target and the like can be well solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a schematic diagram of the FAST cable net structure provided by the prior art;
FIG. 2 is a block diagram of a spherical radio telescope reflecting surface node identification system provided by the present invention;
FIG. 3 is an illustration of a reflective surface panel provided by the present invention;
FIG. 4 is a block diagram of another spherical radio telescope reflecting surface node identification system provided by the present invention;
fig. 5 is a flowchart of a node identification process of a reflecting surface of a spherical radio telescope according to an embodiment of the present invention;
FIG. 6 is a live view of a reflector image provided in accordance with an embodiment of the present invention;
fig. 7 is a down-sampled image obtained by down-sampling a reflection surface image according to an embodiment of the present invention;
fig. 8 is a binarized image obtained by performing grayscale binarization on a down-sampled image according to an embodiment of the present invention;
fig. 9 is an image obtained by performing edge detection on a binarized image according to an embodiment of the present invention;
FIG. 10 is a diagram illustrating an image obtained by feature line extraction of edge pixels of an image according to an embodiment of the present invention;
FIG. 11 is an image of straight line segments of an edge feature of an image after de-emphasis and fitting, according to an embodiment of the present invention;
FIG. 12 is an image obtained after feature node extraction is performed according to straight line segments of a slit according to an embodiment of the present invention;
FIG. 13 is a block diagram illustrating an image obtained after de-duplication of initial feature nodes according to an embodiment of the present invention; and
fig. 14 is a flowchart of a method for identifying a node of a reflecting surface of a spherical radio telescope according to the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are intended for purposes of illustration and explanation only and are not intended to limit the scope of the invention.
Before explaining the present invention in detail, the background of the invention will be first described to facilitate easier understanding of the invention, and is not intended to limit the invention.
The invention is based on the principle of photogrammetry, and carries out node identification on the reflecting surface by the photogrammetry technology. The photogrammetry technology takes a single or a plurality of high-precision measuring cameras as a sensor, carries out rapid non-contact measurement by using an intersection measuring principle, has large measuring range and high speed, and is suitable for the occasion of multi-point dynamic measurement.
The reflection surface nodes are intersections of the gaps between the unit panels, but it should be understood by those skilled in the art that the reflection surface nodes mentioned in the present invention generally refer to the reflection surface nodes identified by the technical solution of the present invention, and not to the actual reflection surface nodes, and that there may be some error therebetween, and the technical solution of the present invention is to reduce such error.
Fig. 2 is a block diagram of a spherical radio telescope reflecting surface node identification system provided by the present invention, and as shown in fig. 2, the system includes an image receiving module 201, an image processing module 202 and a node identification module 203.
The image receiving module 201 is used for receiving a reflection surface image, and the reflection surface is composed of a plurality of unit panels. It will be understood by those skilled in the art that the reflective surface is formed by a plurality of unit panels, which in the present invention are generally triangular in shape. Fig. 3 is a diagram of a reflection surface provided by the present invention, and the reflection surface shown in fig. 3 is composed of a plurality of triangular unit panels, and a linear gap exists between the triangular unit panels. The image of the reflecting surface can be obtained by real-time imaging by a DPU (digital positioning unit), for example, and specifically, a camera having an imaging function such as a camera may be used.
The image processing module 202 is used for performing image processing on the reflection surface image to identify straight line segments of the gaps between the unit panels. In the reflection surface image, the gray value changes sharply from the unit panel to the gap between the unit panels, and appears as a first derivative of the gray value having an amplitude at this point, so that the straight line gap between the unit panels, which is referred to as the straight line segment of the gap, can be identified by using such a feature.
The node identification module 203 is configured to perform node identification according to straight line segments of gaps between the unit panels to obtain measurement coordinates of nodes of the reflecting surface. The node identifying module 203 extracts the positions of the reflecting surface nodes based on the straight line segments of the slit obtained by the image processing module 202, and it should be understood that the positions of the reflecting surface nodes can be used as the basis for the subsequent three-dimensional reconstruction. The position of the reflecting surface node is the measured coordinate of the reflecting surface node, and can also be the precise coordinate of the reflecting surface node described in other places of the invention.
Fig. 4 is a block diagram of another spherical radio telescope reflecting surface node identification system provided by the present invention, and as shown in fig. 4, the image processing module 202 includes a de-noising unit 2021, an edge detecting unit 2022, a characteristic straight line extracting unit 2023, and a characteristic straight line de-duplication fitting unit 2024.
The denoising unit 2021 performs grayscale binarization on the reflection surface image to obtain a binarized image. The key of image binarization is to select a proper segmentation threshold value so as to realize noise removal in an image.
The dynamic threshold algorithm is an algorithm well known to those skilled in the art, and the principle thereof is briefly described here: firstly, drawing a gray level histogram of a reflecting surface image; then, sequentially comparing the number of pixel points corresponding to gray values from 0 to 255 in the gray histogram, finding out the maximum number of pixel points, namely a peak, and marking the gray level corresponding to the peak; secondly, the peak value p of the peak of the gray histogram is multiplied by a set division coefficient f, and the division coefficient can be set to be 0.05 in the invention, namely f is 0.05, the obtained constant (namely 0.05p) is taken as a straight line L parallel to the x axis of the gray histogram, and the straight line L and the gray histogram have a plurality of intersection points; then taking the wave crest as a boundary, wherein the gray value corresponding to the first intersection point close to the peak value on the left side of the wave crest is the searched segmentation threshold value T; if there are 2 or more than 2 intersections on the left side of the peak, the division threshold T may be set to the average of the gray values corresponding to the first intersection and the second intersection close to the peak. The setting mode of the division threshold T is a general setting mode, and it should be understood by those skilled in the art that, in the case where there are more than 2 intersections on the left side of the peak, the division threshold T may be set to an average value of the gray values corresponding to a plurality of intersections close to the peak, and those skilled in the art may set the number of "the plurality of intersections" according to actual conditions.
After the division threshold value is obtained by the dynamic threshold value algorithm, the reflection surface image is processed according to the obtained division threshold value, for example, pixels having a gray value higher than the division threshold value are set to white, and pixels having a gray value lower than the division threshold value are set to black, thereby obtaining a binarized processed image.
The edge detection unit 2022 performs edge detection on the binarized image by an edge detection algorithm to obtain image edge pixels. In the technical scheme provided by the invention, an edge detection algorithm is adopted to identify pixels of a binarized image, wherein the gray value of the pixels is higher than a segmentation threshold value, namely the image edge pixels described herein.
The edge detection algorithm is a mature algorithm, and a person skilled in the art can adopt a proper edge detection algorithm according to actual conditions, and the technical scheme provided by the invention adopts a Canny edge detection operator in practical application, however, the invention is not limited to the Canny edge detection operator, and a person skilled in the art can also adopt other edge detection algorithms besides the Canny edge detection operator as long as the obtained result meets the expected requirements.
The Canny edge detection operator adopted in the practical application of the invention is briefly introduced here, and the detection of the edge detection operator is generally divided into the following three steps: filtering, enhancing and detecting. Firstly, denoising by adopting Gaussian smooth convolution filtering; then correspondingly calculating the gradient amplitude G and the direction theta; then, carrying out 'thin edge' operation on the image by using a non-maximum value inhibition method; and finally, setting a hysteresis threshold by using a high threshold and a low threshold. If the gradient amplitude G of a certain pixel is larger than a high threshold value, the pixel is regarded as an edge pixel and is reserved; if the gradient amplitude G of a certain pixel is smaller than the low threshold value, deleting the pixel; if the amplitude G of a pixel is between the two, the pixel is considered as an edge pixel only if it is satisfied that an edge pixel exists in the range of 8 neighborhoods around the pixel, where 8 neighborhoods refer to 8 pixels above, below, left, right, above-left, above-right, below-left, and below-right of the pixel, that is, for a pixel with the amplitude G between the two, the pixel is considered as an edge pixel to be retained if the pixel is adjacent to the edge pixel, otherwise, the pixel is deleted.
Specifically, in the technical scheme of the present invention, a Canny edge detection operator is used to perform edge search on the binarized image obtained by the denoising unit 2021, so as to obtain image edge pixels. It will be understood by those skilled in the art that the Canny edge detector is described only briefly herein, and is not described in excessive detail herein since it is well known in the art.
The feature straight line extraction unit 2023 performs feature straight line extraction according to the image edge pixels to obtain image edge feature straight line segments. After the edge detection unit 2022 performs edge detection on the binarized image to obtain image edge pixels, the feature straight line extraction unit 2023 extracts feature straight lines from the image edge pixels to provide a basis for extracting feature nodes of the subsequent reflecting surface.
In the prior art, a plurality of algorithms can be used for feature line extraction, and those skilled in the art can adopt corresponding algorithms according to actual situations, but need to meet the requirement of feature line extraction. The method adopts accumulative probability Hough transform, namely, image edge pixels are converted into straight line segments through the accumulative probability Hough transform, namely, image edge feature straight line segments. Furthermore, as will be understood by those skilled in the art, after the feature straight line extraction unit 2023 obtains the image edge feature straight line segments, the end points of each image edge feature straight line segment are recorded and stored, and the obtained image edge feature straight line segments are numbered, and the numbers and the end points of the image edge feature straight line segments are correspondingly stored in the memory. The storage process is well known to those skilled in the art and will not be described in detail herein.
The feature straight line de-duplication fitting unit 2024 performs de-duplication and fitting on the image edge feature straight line segment to obtain a slit straight line segment between unit panels. After the feature straight line extraction unit 2023 extracts the feature straight lines, the image edge feature straight line segments are extracted, and due to the gradual change principle of the pixels, a plurality of interference intersection points exist in the subsequent process of solving the intersection point of the image edge feature straight line segments, so that the accuracy of the experimental result is affected, and therefore, the deduplication of the obtained image edge feature straight line segments is a very necessary and important step for ensuring the accuracy rate of the intersection points.
The following describes the process of the feature straight line de-emphasis fitting unit 2024 to perform de-emphasis and fitting operations on the image edge feature straight line segments.
Traversing all the image edge feature straight line segments obtained by the feature straight line extraction unit 2023 (which can be traversed according to the numbers of the image edge feature straight line segments), in the process of traversing the image edge feature straight line segments, assuming that there are i and j two image edge feature straight line segments, and the coordinates of two end points of i are (x 1)i,y1i) And (x 2)i,y2i) And j has coordinates of (x 3) at its two endsj,y3j) And (x 4)j,y4j)。
The angle corresponding to the slope of i is k1iThe angle corresponding to the slope of j is represented by k2jDenotes, k1iAnd k2jCalculated by formula (1):
k1 calculated for equation (1)iAnd k2jCalculate | k1i-k2jIf | k1i-k2jI is less than or equal to 7.5 degrees, i and j are considered parallel to each other, if | k1i-k2jIf | is greater than 7.5 degrees, i and j are considered to be non-parallel to each other. For two image edge feature straight-line segments judged to be parallel to each other, calculating the distance between the two image edge feature straight-line segments parallel to each other, assuming that i and j are two image edge feature straight-line segments parallel to each other, calculating the distance d between the two parallel image edge feature straight-line segments i and j by using formula (2):
in formula (2), c1 ═ x2i×y1i-x1i×y2i，c2＝x4j×y3j-x3j×y4j，e＝y2i-y1i，b1＝x1i-x2i，b2＝x3j-x4jIf d is less than or equal to 70 pixels, i and j are considered to be overlapped, and if d is more than 70 pixels, i and j are considered to be not overlapped.
For two image edge feature straight-line segments which are regarded as being not parallel or coincident, no operation is performed, the two image edge feature straight-line segments are returned to be continuously traversed, and for the two image edge feature straight-line segments which are regarded as being coincident, a deduplication operation is performed, that is, for the two image edge feature straight-line segments which are regarded as being coincident, one image edge feature straight-line segment is deleted, the deleted image edge feature straight-line segment can be any one of the two image edge feature straight-line segments, or one image edge feature straight-line segment can be selected to be deleted according to a preset rule for deleting the coincident image edge feature straight-line segment, for example, the traversed image edge feature straight-line segment after deletion can be performed.
Here, the process of traversing the image edge feature straight-line segments is illustrated, for example, there are 5 image edge feature straight-line segments, which are a first image edge feature straight-line segment, a second image edge feature straight-line segment, a third image edge feature straight-line segment, a fourth image edge feature straight-line segment, and a fifth image edge feature straight-line segment. And when traversing the first image edge feature straight line segment, respectively judging whether the first image edge feature straight line segment is parallel to other 4 image edge feature straight line segments and whether the first image edge feature straight line segment and the other 4 image edge feature straight line segments are overlapped. When traversing to a second image edge feature straight-line segment, respectively judging whether the second image edge feature straight-line segment is mutually parallel and is overlapped with other 4 image edge feature straight-line segments, or only respectively judging whether the second image edge feature straight-line segment is mutually parallel and is overlapped with third to fifth image edge feature straight-line segments, if the second image edge feature straight-line segment and the fourth image edge feature straight-line segment are judged to be two overlapped image edge feature straight-line segments, deleting the fourth image edge feature straight-line segment, and only remaining 4 image edge feature straight-line segments of the first image edge feature straight-line segment, the second image edge feature straight-line segment, the third image edge feature straight-line segment and the fifth image edge feature straight-line segment. And then, when traversing to a third image edge feature straight-line segment, judging whether the third image edge feature straight-line segment is parallel to and overlapped with other 3 image edge feature straight-line segments, or judging whether the third image edge feature straight-line segment is parallel to and overlapped with a fifth image edge feature straight-line segment. The process of traversing described herein is for illustrative purposes only and is not intended to be limiting, and any reasonable manner of traversing straight line segments of an image edge feature is within the scope of the invention.
It should be understood by those skilled in the art that the process of deleting the image edge feature straight-line segments needs to complete the deletion of the numbers and end points of the corresponding image edge feature straight-line segments stored in the memory, the number and end points of the image edge feature straight-line segments after the deduplication, that is, the image edge feature straight-line segments corresponding to the numbers still existing in the memory, are also known, and the number and end points of the image edge feature straight-line segments after the deduplication can be subjected to least squares fitting by a least squares fitting algorithm known in the prior art to obtain the slit straight-line segments between the unit panels.
The above describes the process of performing the deduplication and fitting operation on the image edge feature straight-line segment, which is adopted in the embodiment of the present invention, but the present invention is not limited thereto, and any manner that can implement the deduplication and fitting operation on the image edge feature straight-line segment belongs to the protection scope of the present invention.
As shown in fig. 4, the node identifying module 203 includes a feature node extracting unit 2031 and a feature node deduplication unit 2032.
The feature node extraction unit 2031 traverses the slit straight-line segments, determines the mutual position relationship according to the slopes of the slit straight-line segments, and calculates the pixel coordinates of the intersection points of the non-parallel slit straight-line segments to obtain initial feature nodes.
As can be seen from the above description, the number and the end point of the straight line segment of the slit obtained by the image processing module 202 are known and stored in the memory, and the feature node extracting unit 2031 may traverse the straight line segment of the slit according to the number of the straight line segment of the slit, and during the process of traversing the straight line segment of the slit, assume that there are two straight line segments of m and n, where the coordinates of the two end points of m are (x 1) respectivelym,y1m) And (x 2)m,y2m) And n has coordinates of (x 1) at both endsn,y1n) And (x 2)n,y2n) The slope of m is represented by kmThe slope of n is represented by knDenotes kmAnd knCalculated by equation (3):
the parameter a is obtained by calculation according to the formula (4)m、an、bm、bn、cm、cn：
K calculated for formula (3)mAnd knCalculate | km-knIf km-knIf | is less than or equal to 2.5, m and n are parallel to each other, if | km-knIf | is greater than 2.5, then m and n are considered to be non-parallel to each other. And for the straight line segments of the two gaps which are regarded as being parallel to each other, performing no operation, and returning to continue traversing. For two slit straight-line segments which are regarded as being not parallel to each other, calculating the pixel coordinates of the intersection point of the two slit straight-line segments so as to obtain an initial characteristic node, wherein the pixel coordinates of the intersection point of the slit straight-line segments are expressed by (u, v), and the (u, v) is calculated by a formula (5) to obtain:
and after the pixel coordinates (u, v) are obtained through calculation, judging whether the pixel coordinates (u, v) are within the pixel range of the reflection surface image, and if the pixel coordinates (u, v) are within the pixel range of the reflection surface image, marking the pixel coordinates (u, v) as initial feature nodes. If the pixel coordinate (u, v) is not within the range of the reflection surface image pixel, the pixel coordinate (u, v) is ignored, namely not used as the initial feature node. Wherein, those skilled in the art should understand that the intersection point of two slit straight line segments may be above the slit straight line segment or outside the slit straight line segment.
Here, the process of traversing the slit straight-line segments is illustrated, for example, there are 5 slit straight-line segments, which are respectively a first slit straight-line segment, a second slit straight-line segment, a third slit straight-line segment, a fourth slit straight-line segment, and a fifth slit straight-line segment. And when traversing the first slit straight-line segment, respectively judging whether the first slit straight-line segment is parallel to other 4 slit straight-line segments. When traversing the second slit straight-line segment, respectively judging whether the second slit straight-line segment is parallel to other 4 slit straight-line segments, or respectively judging whether the second slit straight-line segment is parallel to the third to fifth slit straight-line segments, and so on. The process of traversing described herein is for illustrative purposes only and is not intended to be limiting, and any reasonable manner of traversing a straight line segment of a slot is within the scope of the present invention.
The feature node duplication removal unit 2032 traverses the initial feature nodes, and performs duplication removal operation according to the distance between the initial feature nodes to obtain the measurement coordinates of the reflecting surface nodes. For the initial feature nodes obtained by the feature node extraction unit 2031, there may be a certain number of noise points, and the operation of the feature node duplication removal unit 2032 is to remove these noise points, which may also be regarded as duplication removal operation, so as to obtain optimized feature nodes, that is, the measurement coordinates of the reflection surface nodes.
The feature node duplication removal unit 2032 traverses the initial feature nodes obtained by the feature node extraction unit 2031, and in the process of traversing the initial feature nodes, it is assumed that there are two initial feature nodes s and t, and the coordinate of s is (x 1)s,y1s) And t has the coordinate of (x 2)t,y2t) And the distance between s and t is calculated in dis by equation (6):
and judging whether dis is less than 30 pixels, if so, regarding the two initial feature nodes of s and t as adjacent points, otherwise, not performing any operation, and returning to continue traversing. For two initial feature nodes regarded as close points, one of the two initial feature nodes is deleted, the deleted initial feature node may be any one of the two initial feature nodes, or one of the two initial feature nodes may be selected to be deleted according to a preset rule for deleting the close initial feature node, for example, the initial feature node traversed after deletion may be used, or the initial feature node to be deleted may be determined according to coordinates of the two close initial feature nodes.
Here, the process of traversing the initial feature nodes is illustrated, for example, there are 5 initial feature nodes, which are respectively a first initial feature node segment, a second initial feature node, a third initial feature node, a fourth initial feature node, and a fifth initial feature node. And when traversing to the first initial feature node, respectively judging whether the first initial feature node and the other 4 initial feature nodes are considered as adjacent points. When traversing to the second initial feature node, respectively determining whether the second initial feature node and the other 4 initial feature nodes are considered as proximate points, or only determining whether the second initial feature node and the third to fifth initial feature nodes are considered as proximate points, if the second initial feature node and the fourth image edge feature straight-line segment are determined as proximate points, for example, the fourth initial feature node may be deleted, and at this time, only the first initial feature node, the second initial feature node, the third initial feature node, and the fifth initial feature node are left. Next, when traversing to the third initial feature node, judging whether the third initial feature node and other 3 initial feature nodes are considered as adjacent points, or only judging whether the third initial feature node and the fifth initial feature node are considered as adjacent points. The process of traversing described herein is for explanation purposes only and is not intended to limit the present invention, and any reasonable manner of traversing the initial feature nodes is within the scope of the present invention.
The way for determining the initial feature node to be deleted according to the coordinates of two similar initial feature nodes is as follows:
assuming s and t are two initial feature nodes which are close, the abscissa of s and t is compared, the abscissa of s is represented by s.x, the ordinate of s is represented by s.y, the abscissa of t is represented by t.x, the ordinate of t is represented by t.y, if | s.x-t.x | ≦ 10-6Sorting according to the ordinate of s and t, performing duplicate removal operation according to the result after sorting by the ordinate, and if s.y is less than t.y, deleting the initial feature node t and also deleting the initial feature node s; if | s.x-t.x | > 10-6Sorting is carried out according to the abscissa of the s and the t, the duplicate removal operation is carried out according to the sorted result of the abscissa, and if s.x is less than t.x, the initial feature node t can be deleted, and the initial feature node s can also be deleted.
After the node identification module 203 obtains the measurement coordinates of the reflecting surface nodes, it is considered that the rough extraction of the feature nodes is completed, however, when the accuracy requirement of the FAST site is relatively high, the rough extraction of the reflecting surface nodes is also required to be performed.
As shown in fig. 4, the system for identifying the reflection surface node of the spherical radio telescope provided by the present invention further includes an interested region identifying module 204 and a characteristic node fine detecting module 205.
The region-of-interest identification module 204 defines a region of interest according to a preset rule with the measurement coordinates of each reflecting surface node as a center, so as to obtain the region of interest.
The Region of Interest (ROI) is a specified small-range Region to reduce noise interference of other regions, and the Region of Interest is defined according to a predetermined rule, where the predetermined rule may be set to define a square Region with a side length of a specified length, that is, a Region of Interest, with the measurement coordinate of each reflection surface node as a center. It is easy to understand that, since one region of interest is defined for the measured coordinates of each reflecting surface node, the number of the regions of interest is the same as the number of the reflecting surface nodes (i.e., the reflecting surface nodes represented by the measured coordinates of the reflecting surface nodes) obtained by the node identifying module 203 in the course of the extraction process.
The order of defining the region of interest for each reflecting surface node may be, for example, the order of the reflecting surface nodes (i.e., the reflecting surface nodes represented by the measured coordinates of the reflecting surface nodes) obtained by traversing the node identification module 203, or may be the order of the abscissa (or ordinate) of the reflecting surface node (i.e., the reflecting surface node represented by the measured coordinates of the reflecting surface node) obtained by the node identification module 203 from small to large (or from large to small).
For some nodes which are positioned at the edge of the reflecting surface image and are specifically represented by the fact that the distance between the abscissa or the ordinate and the nearest edge of the reflecting surface image is less than half of the side length size of the ROI (namely, the reflecting surface node represented by the measuring coordinate of the reflecting surface node), if the abscissa of the node (namely, the reflecting surface node represented by the measuring coordinate of the reflecting surface node) is less than half of the side length of the ROI, the side length of the ROI is changed to be 2 times of the side length of the abscissa; if the distance between the abscissa of the node (namely the reflecting surface node represented by the measuring coordinate of the reflecting surface node) and the total column number of the reflecting surface image pixels is less than half of the side length of the ROI, changing the side length of the ROI into 2 times of the difference between the total column number of the reflecting surface image pixels and the abscissa of the node (namely the reflecting surface node represented by the measuring coordinate of the reflecting surface node); if the ordinate of the node (namely the reflecting surface node represented by the measuring coordinate of the reflecting surface node) is less than half of the side length of the ROI, changing the side length of the ROI to be 2 times of the ordinate; if the distance between the ordinate of the node (namely the reflecting surface node represented by the measuring coordinate of the reflecting surface node) and the total row number of the reflecting surface image pixels is less than half of the side length of the ROI, changing the side length of the ROI into 2 times of the difference between the total row number of the reflecting surface image pixels and the ordinate of the node (namely the reflecting surface node represented by the measuring coordinate of the reflecting surface node); for nodes (i.e., reflector nodes represented by the measured coordinates of the reflector nodes) at the four corners of the reflector image, the edge lengths of the ROIs for these nodes (i.e., reflector nodes represented by the measured coordinates of the reflector nodes) are changed to be twice the distance from the nearest edge.
The above describes the defining manner of the region of interest, but the invention is not limited thereto, and the region of interest is not limited to the square region, and the skilled person can define the region of interest with other shapes, such as rectangle, circle, etc., according to the actual situation.
The predetermined rule may be set to, for example, define rectangular regions, i.e., regions of interest, different in length and width, with the measurement coordinates of each of the reflecting surface nodes as the center.
In the case that the region of interest is a rectangular region, for some nodes (i.e., reflective surface nodes represented by the measured coordinates of the reflective surface nodes) which are located at the edge of the reflective surface image and specifically represent that the distance between the abscissa and the nearest edge of the reflective surface image is less than half of the length of the ROI, it is assumed that the abscissa direction is the length direction of the rectangular region of interest and the ordinate direction is the width direction of the rectangular region of interest, and if the abscissa of the node (i.e., the reflective surface node represented by the measured coordinates of the reflective surface nodes) is less than half of the length of the ROI, the length of the ROI is changed to 2 times the abscissa; if the distance between the abscissa of the node (namely the reflecting surface node represented by the measuring coordinate of the reflecting surface node) and the total column number of the reflecting surface image pixels is less than half of the length value of the ROI, changing the length of the ROI into 2 times of the difference between the total column number of the reflecting surface image pixels and the abscissa of the node (namely the reflecting surface node represented by the measuring coordinate of the reflecting surface node); if the vertical coordinate of the node (namely the reflecting surface node represented by the measuring coordinate of the reflecting surface node) is less than half of the width value of the ROI, changing the width of the ROI into 2 times of the vertical coordinate; if the distance between the ordinate of the node (i.e. the reflecting surface node represented by the measured coordinate of the reflecting surface node) and the total row number of the reflecting surface image pixels is less than half of the width value of the ROI, changing the width of the ROI to be 2 times of the difference between the total column number of the reflecting surface image pixels and the ordinate of the node (i.e. the reflecting surface node represented by the measured coordinate of the reflecting surface node); for nodes (i.e., reflector nodes represented by the measured coordinates of the reflector nodes) at the four corners of the reflector image, the length and width of the ROI of these nodes (i.e., reflector nodes represented by the measured coordinates of the reflector nodes) are changed to be twice the distance from the nearest edge.
After the region of interest is determined, the fine feature node detection module 205 performs a fine extraction operation on the region of interest.
The fine feature node detection module 205 is configured to perform image processing again through the image processing module 202 and perform node identification through the node identification module 203 for each of the regions of interest, so as to obtain accurate coordinates of the nodes of the reflecting surface. Specifically, for the region of interest obtained by the region of interest identification module 204, the operations such as the grayscale binarization processing, the edge detection, the feature line extraction, the deduplication and the fitting performed by the image processing module 202, and the initial feature node extraction and the deduplication performed by the node identification module 203 are performed again to obtain the precise coordinates of the reflection surface nodes, which are referred to herein as the measured coordinates of the reflection surface nodes obtained by the node identification module 203.
The spherical radio telescope reflection surface node identification system provided by the invention also comprises a down-sampling module (not shown in the figure), wherein the down-sampling module is used for performing down-sampling processing on the reflection surface image to obtain a down-sampled image; the image processing module 202 is configured to perform image processing on the down-sampled image to identify straight line segments of the gaps between the unit panels. That is, in the case of performing down-sampling processing on the reflection surface image by using the down-sampling module, the reflection surface image for which the processing of the image processing module 202 performing image processing to identify a straight line segment of the slit is performed may be replaced with the down-sampled image.
The image receiving module 201 receives a large reflection surface image, occupies too much memory space in the processing process, and is slow in processing speed, in order to overcome the problem, a down-sampling module can be adopted to perform down-sampling processing on the reflection surface image, so that the pixels of the reflection surface image are reduced, but the overall appearance of the reflection surface image is not changed, and on the premise of not influencing the experimental result, the calculation speed of the image is greatly improved. In the embodiment of the invention, the gaussian pyramid algorithm is adopted to perform down-sampling processing on the reflection surface image, but the invention is not limited to the gaussian pyramid algorithm, and any algorithm which can perform down-sampling processing and can achieve the purpose of the invention is within the protection scope of the invention.
In the Gaussian pyramid algorithm adopted by the invention, the down-sampling coefficient is 0.1, and the algorithm is briefly introduced as follows: first, for the upper layer image Gi-1Carrying out Gaussian kernel convolution on the image of the hierarchy; then, all even number rows and columns of the reflection surface image are removed to obtain GiA hierarchy of images in which the cut-off frequency of the low-pass filter employed by each hierarchy of images is incremented by a factor of 2. The gaussian pyramid algorithm adopted by the invention is an algorithm in the prior art, so the principle of the gaussian pyramid algorithm is only briefly introduced here, and the gaussian pyramid algorithm is a technique well known by those skilled in the art and is not described herein again.
Fig. 5 is a flowchart of a node identification process of a reflection surface of a spherical radio telescope according to an embodiment of the present invention, and the present invention is described below with reference to practical applications of the present invention, as shown in fig. 5, the process includes:
and step 510, defining and acquiring the ROI, and performing the operations of the step 503 to the step 508 on the acquired ROI again to obtain accurate coordinates of the reflecting surface node.
Fig. 14 is a flowchart of a method for identifying a node of a reflecting surface of a spherical radio telescope, according to the present invention, as shown in fig. 14, the method includes:
and 1403, node identification is carried out according to straight line segments of the gaps between the unit panels, and measurement coordinates of the nodes of the reflecting surface are obtained.
In the method for identifying the reflection surface node of the spherical radio telescope provided by the invention, the step of carrying out image processing on the reflection surface image to identify the straight line segment of the gap between the unit panels comprises the following steps: carrying out gray level binarization processing on the reflection surface image to obtain a binarization processed image; carrying out edge detection on the binaryzation processing image through an edge detection algorithm to obtain image edge pixels; extracting characteristic straight lines according to image edge pixels to obtain image edge characteristic straight line segments; and carrying out duplication removal and fitting on the image edge feature straight line segment to obtain a slit straight line segment between the unit panels.
In the method for identifying the reflection surface node of the spherical radio telescope provided by the invention, the step of identifying the node according to the straight line segment of the gap between the unit panels to obtain the measurement coordinate of the reflection surface node comprises the following steps: traversing the slit straight-line segments, judging the mutual position relation according to the slope of the slit straight-line segments, and calculating the pixel coordinates of the non-parallel intersection points of the slit straight-line segments to obtain initial characteristic nodes; traversing the initial feature nodes, and performing duplication elimination operation according to the distance between the initial feature nodes to obtain the measurement coordinates of the reflecting surface nodes.
The method for identifying the reflection surface node of the spherical radio telescope further comprises the following steps: respectively using the measurement coordinate of each reflecting surface node as a center, and defining an interested area according to a preset rule to obtain the interested area; and carrying out image processing again and carrying out node identification on each interested area in the interested areas so as to obtain accurate coordinates of the nodes of the reflecting surface.
The method for identifying the reflection surface node of the spherical radio telescope further comprises the following steps: performing down-sampling processing on the reflection surface image to obtain a down-sampled image; wherein the down-sampled image is image processed to identify straight line segments of the gap between the unit panels.
The above describes the process of performing the deduplication and fitting operation on the image edge feature straight-line segment, which is adopted in the embodiment of the present invention, but the present invention is not limited thereto, and any manner that can perform the deduplication and fitting operation on the image edge feature straight-line segment belongs to the protection scope of the present invention.
It should be noted that the specific details and benefits of the method for identifying the node of the reflecting surface of the spherical radio telescope provided by the present invention are similar to those of the system for identifying the node of the reflecting surface of the spherical radio telescope provided by the present invention, and are not described herein again.
Although the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solutions of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications all belong to the protection scope of the embodiments of the present invention.
The photogrammetry has the advantages of large measuring range, high precision and high measuring speed, can realize large-scale, high-precision and high-space-time sampling rate dynamic surface shape detection, can achieve the measuring range of 250 meters and the precision of RMS (root mean square) 2mm, can complete 1000-point measuring tasks in a measuring area within 1 minute, and breaks through the bottleneck of real-time dynamic monitoring of the reflecting surface of the giant radio telescope.
By adopting the technical scheme provided by the invention, the nodes of the reflecting surface can be extracted by adopting an image processing mode, the target-free measurement is well realized, the problems of electromagnetic interference and the like caused by an active target can be effectively avoided, and the problem that the target is extremely difficult to maintain is solved.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, the embodiments of the present invention do not describe every possible combination.
In addition, any combination of various different implementation manners of the embodiments of the present invention is also possible, and the embodiments of the present invention should be considered as disclosed in the embodiments of the present invention as long as the combination does not depart from the spirit of the embodiments of the present invention.
Claims (10)
1. A spherical radio telescope reflecting surface nodal identification system, the system comprising:
the image receiving module is used for receiving a reflecting surface image, and the reflecting surface is formed by a plurality of unit panels;
the image processing module is used for carrying out image processing on the reflecting surface image so as to identify a slit straight line segment between the unit panels; and
and the node identification module is used for identifying nodes according to straight line sections of gaps between the unit panels to obtain the measurement coordinates of the nodes of the reflecting surface.
2. The spherical radio telescope reflecting surface nodal identification system as claimed in claim 1, wherein said image processing module includes:
the denoising unit is used for carrying out gray level binarization processing on the reflecting surface image to obtain a binarization processing image;
the edge detection unit is used for carrying out edge detection on the binaryzation processing image through an edge detection algorithm to obtain image edge pixels;
the characteristic straight line extraction unit is used for extracting characteristic straight lines according to the image edge pixels to obtain image edge characteristic straight line segments; and
and the characteristic straight line de-duplication fitting unit is used for de-duplicating and fitting the image edge characteristic straight line segment to obtain a slit straight line segment between the unit panels.
3. The system of claim 1, wherein the nodal identification module comprises:
the characteristic node extraction unit is used for traversing the slit straight-line segment, judging the position relation of the slit straight-line segment according to the slope of the slit straight-line segment, and calculating the pixel coordinate of the intersection point of the non-parallel slit straight-line segments to obtain an initial characteristic node; and
and the characteristic node duplicate removal unit is used for traversing the initial characteristic nodes and performing duplicate removal operation according to the distance between the initial characteristic nodes to obtain the measurement coordinates of the reflecting surface nodes.
4. A spherical radio telescope reflecting surface nodal identification system as claimed in any one of claims 1 to 3, further including:
the interesting region identification module is used for dividing an interesting region according to a preset rule by taking the measurement coordinate of each reflecting surface node as a center to obtain the interesting region; and
and the characteristic node fine detection module is used for carrying out image processing on each interested area in the interested areas through the image processing module again and carrying out node identification through the node identification module so as to obtain the accurate coordinates of the reflecting surface nodes.
5. A spherical radio telescope reflecting surface nodal identification system as claimed in any one of claims 1 to 3, further including:
the down-sampling module is used for performing down-sampling processing on the reflecting surface image to obtain a down-sampled image;
the image processing module is used for carrying out image processing on the down-sampled image so as to identify a straight line segment of a gap between unit panels.
6. A spherical radio telescope reflecting surface node identification method is characterized by comprising the following steps:
receiving a reflection surface image, the reflection surface being composed of a plurality of unit panels;
performing image processing on the reflection surface image to identify a slit straight-line segment between unit panels; and
and identifying nodes according to straight line segments of gaps among the unit panels to obtain measurement coordinates of the nodes of the reflecting surface.
7. The method of claim 6, wherein the image processing of the reflection surface image to identify straight line segments of a gap between unit panels comprises:
carrying out gray level binarization processing on the reflection surface image to obtain a binarization processed image;
carrying out edge detection on the binarization processing image through an edge detection algorithm to obtain image edge pixels;
extracting a characteristic straight line according to the image edge pixel to obtain an image edge characteristic straight line segment; and
and carrying out duplication removal and fitting on the image edge characteristic straight line segment to obtain a slit straight line segment between the unit panels.
8. The method for identifying the nodal point of the reflecting surface of the spherical radio telescope according to claim 6, wherein the step of identifying the nodal point according to the straight line segment of the gap between the unit panels to obtain the measurement coordinates of the nodal point of the reflecting surface comprises the steps of:
traversing the slit straight-line segments, judging the mutual position relation according to the slope of the slit straight-line segments, and calculating the pixel coordinates of the intersection points of the non-parallel slit straight-line segments to obtain initial characteristic nodes; and
traversing the initial feature nodes, and performing duplication elimination operation according to the distance between the initial feature nodes to obtain the measurement coordinates of the reflecting surface nodes.
9. A spherical radio telescope reflecting surface nodal identification method according to any of claims 6 to 8, further comprising:
respectively using the measurement coordinate of each reflecting surface node as a center, and defining an interested area according to a preset rule to obtain the interested area; and
and carrying out image processing again on each interested area in the interested areas and carrying out node identification to obtain accurate coordinates of the nodes of the reflecting surface.
10. A spherical radio telescope reflecting surface nodal identification method according to any of claims 6 to 8, further comprising:
performing down-sampling processing on the reflecting surface image to obtain a down-sampled image;
wherein the down-sampled image is image processed to identify slit straight line segments between unit panels.
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