CN111652894A - Aircraft-oriented annular target self-adaptive detection method - Google Patents
Aircraft-oriented annular target self-adaptive detection method Download PDFInfo
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Abstract
The invention relates to an aircraft-oriented annular target self-adaptive detection method, in particular to the field of data processing. The method comprises the following steps: s1: acquiring a target image, and obtaining a candidate region according to the target image; s2: carrying out edge detection on the target image to obtain a target edge; s3: performing edge enhancement on the target edge by using morphological filtering; s4: carrying out Hough transform on the candidate region to obtain a candidate annular target; s5: and eliminating interference on the annular target to obtain an optimal fitting annular target. The scheme solves the technical problem of how to enhance the detection and tracking effects of the annular target, and is suitable for the annular target detection of the aircraft.
Description
Technical Field
The invention relates to the field of data processing, in particular to an aircraft-oriented annular target self-adaptive detection method.
Background
How to detect a ring-shaped target quickly and accurately in a complex image has been a significant problem for researchers to try to find. The method has important application in the fields of biomedical microscopic images, industrial automatic detection, robot vision, space technology, military defense and the like. In the flight process of the miniature flapping rotor wing, effective detection of the annular target is also an extremely important research content, subsequent tracking and control can be directly influenced, and the key of success or failure of visual navigation is realized. The circular rings are mostly elliptical under different viewing angles, and all ring (ellipse) detection methods depend on the unique algebraic geometric properties of the ellipses. Fig. 1 is a diagram of mathematical attributes often used in ellipse detection. The main ellipse parameters include the ellipse center coordinate, the ellipse major and minor axis length, the ellipse focal length and the ellipse deflection angle. The characteristics of the ellipse used for detecting the ellipse generally include symmetry of the ellipse, parallel chord theorem, ellipse chord tangent theorem and the like.
The existing ellipse detection method can be divided into three types based on the structure: the minimum quadratic dependence on algebraic geometry and its optimization method, the clustering-based or voting-based hough transform and its variation method, and related combination methods, such as methods combining techniques of statistical analysis, heuristics, or combinations. The third method is to first fully utilize the basic geometric characteristics of the ellipse to reduce the complex problem into a sub-problem. This method is also referred to as an arc-based ellipse detection method. Generally, the method extracts an elliptical arc and then fits the ellipse by using the elliptical arc.
In the circular ring detection under the condition of the aircraft, due to the change of the visual angle, the dynamic motion of the aircraft and the interference of complex backgrounds, the performance of the traditional detection algorithm is poor, and the false detection is easy to occur.
Disclosure of Invention
The invention aims to solve the technical problem of how to enhance the detection and tracking effects of the annular target.
The technical scheme for solving the technical problems is as follows: an adaptive detection method for an annular target facing an aircraft comprises the following steps:
s1: acquiring a target image, and obtaining a candidate region according to the target image;
s2: carrying out edge detection on the target image to obtain a target edge;
s3: performing edge enhancement on the target edge by using morphological filtering;
s4: carrying out Hough transform on the candidate region to obtain a candidate annular target;
s5: and eliminating interference on the annular target to obtain an optimal fitting annular target.
The invention has the beneficial effects that: the invention designs a rapid ellipse detection method based on edge detection and Hough transform aiming at the defects of poor detection performance and poor robustness of the traditional Hough detection method in a complex dynamic environment. Firstly, an improved Canny edge detection method is provided, and the noise is removed while the edge information is effectively retained; then, the edges of the target are enhanced by using morphological filtering, so that the robustness of the method is improved; carrying out Hough transformation on the candidate region and extracting a candidate annular target; and finally, effectively eliminating the interference target based on a voting mechanism of fitting degree, and detecting the optimal annular target. Experimental results show that the method has good robustness for complex backgrounds and motion scenes, and can reliably detect and stably track the annular target, so that the technical problem of how to enhance the detection and tracking effects of the annular target is solved.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, step S2 specifically includes:
s21: carrying out image denoising on the target image by using Gaussian filtering;
s22: obtaining the gradient amplitude and the direction of the filtered target image according to the finite difference of the first-order partial derivatives;
s23: performing non-maximum suppression on the gradient amplitude;
s24: obtaining a high threshold and a low threshold, finding out the outline of the image edge in the target image according to the high threshold, and connecting the edge break points of the outline according to the low threshold until the whole outline is closed to obtain the target edge.
The further scheme has the advantages that in order to better detect edges and reduce the influence of noise on results in the later period, the original image is subjected to Gaussian smooth denoising; the traditional Canny operator only extracts the gradients in the x direction and the y direction and then performs calculation, but still loses some important edge information, especially some information in the oblique direction. Therefore, the gradient information in two oblique directions of the image is calculated, and finally the original gradient information and the information in the oblique direction are integrated to obtain a final edge image; the larger the gradient value of a point in the image, the larger the gradient magnitude corresponding to it. But not necessarily all points with larger gradient magnitudes will be true edge points of the image. Therefore, to determine the edges, local gradient maxima points must be preserved, i.e., non-maxima suppression of gradient magnitudes. By using gradient angle information, a method of comparing local gradient values and reserving a local gradient maximum value is adopted to inhibit a non-maximum value; .
Further, step S3 specifically includes:
s31: acquiring structural elements, and carrying out corrosion operation on the target image according to the structural elements;
s32: and performing expansion operation on the target image by using the structural elements with smaller sizes to obtain a connected domain.
The method has the advantages that isolated noise points can be eliminated after structural elements are corroded, and the target image can be expanded to fill up the cavity to form a connected domain, so that the edge enhancement effect is achieved.
Further, step S4 specifically includes:
s41: selecting the end point of the major axis of the ellipse, and calculating the central coordinate, the semi-major axis and the included angle theta between the major axis of the ellipse and the x axis;
s42: calculating the length of the short shaft to obtain an equation of an ellipse;
s43: and obtaining a candidate annular target according to the equation of the ellipse.
Further, step S42 specifically includes:
s421: taking an edge point in any connected domain, and recording the edge point as an m point;
s422: repeating the step A421, and obtaining a corresponding beta value according to each m point;
s423: counting the occurrence frequency of each calculated beta value, and setting a threshold value T according to the frequency;
s424, obtaining the ellipse { O ] when the β value corresponding to the m point meets the threshold Tx,Oy,α,β,θ}。
The beneficial effect of adopting the above further scheme is that if the point pair selected at the beginning is two end points of the ellipse major axis, and the m points are also points on the ellipse, the calculated beta value is the same each time, and there are many m points on the ellipse, the number of times of occurrence of the beta reaches a peak value, a threshold value T is set, if the accumulation (the number of times of occurrence) of a certain beta exceeds T, the assumed major axis end point is determined to be correct, and the ellipse exists at the position.
Further, step S5 specifically includes:
s51: according to the ellipse { Ox,Oyα, θ } yields the degree of fit dis of the pixeli＝exp(-abs(Er1+Er2-1)), wherein:
Er1＝((Pix-Oix)·cos(θi)+(Piy-Oiy)·sin(θi))2/αi 2，
Er2＝(-(Pix-Oix)·sin(θi)+(Piy-Oiy)·cos(θi))2/βi 2；
s52: for all dis in connected domainiVoting for pixels less than 0.5, and taking the fitting degree as a weight;
s53: the connected domain with the maximum ticket value is used as the optimal fitting annular target
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a method flow diagram of an embodiment of an adaptive detection method for an annular target of an aircraft according to the present invention;
FIG. 2 is a schematic diagram of elliptical parameters of another embodiment of the adaptive detection method for an annular target of an aircraft according to the present invention;
fig. 3 is a schematic diagram of any point on an ellipse of an other embodiment of the adaptive detection method for the annular target of the aircraft.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
The embodiment is basically as shown in the attached figure 1:
the self-adaptive detection method for the annular target facing the aircraft in the embodiment comprises the following steps:
s1: acquiring a target image, and obtaining a candidate region according to the target image;
s2: carrying out edge detection on the target image to obtain a target edge;
s3: performing edge enhancement on the target edge by using morphological filtering;
s4: carrying out Hough transform on the candidate region to obtain a candidate annular target;
s5: and carrying out interference elimination on the annular target to obtain the optimal fitting annular target.
The invention has the beneficial effects that: the invention designs a rapid ellipse detection method based on edge detection and Hough transform aiming at the defects of poor detection performance and poor robustness of the traditional Hough detection method in a complex dynamic environment. Firstly, an improved Canny edge detection method is provided, and the noise is removed while the edge information is effectively retained; then, the edges of the target are enhanced by using morphological filtering, so that the robustness of the method is improved; carrying out Hough transformation on the candidate region and extracting a candidate annular target; and finally, effectively eliminating the interference target based on a voting mechanism of fitting degree, and detecting the optimal annular target. Experimental results show that the method has good robustness for complex backgrounds and motion scenes, and can reliably detect and stably track the annular target, so that the technical problem of how to enhance the detection and tracking effects of the annular target is solved.
On the basis of the technical scheme, the invention can be further improved as follows.
Optionally, in some other embodiments, step S2 specifically includes:
s21: carrying out image denoising on a target image by utilizing Gaussian filtering;
s22: obtaining the gradient amplitude and the direction of the filtered target image according to the finite difference of the first-order partial derivatives;
s23: carrying out non-maximum suppression on the gradient amplitude;
s24: and obtaining a high threshold and a low threshold, finding out the outline of the image edge in the target image according to the high threshold, and connecting the edge breakpoints of the outline according to the low threshold until the whole outline is closed to obtain the target edge.
In order to better detect the edge in the later period and reduce the influence of noise on the result, firstly, Gaussian smooth denoising is carried out on an original image; the traditional Canny operator only extracts gradients in the x direction and the y direction and then carries out calculation, but still loses some important edge information, particularly some information in the oblique direction. Therefore, the gradient information in two oblique directions of the image is calculated, and finally the original gradient information and the information in the oblique directions are integrated to obtain a final edge image; the larger the gradient value of a point in the image, the larger the gradient magnitude corresponding to it. But not necessarily all points with larger gradient magnitudes will be true edge points of the image. Therefore, to determine the edges, local gradient maxima points must be preserved, i.e., non-maxima suppression of gradient magnitudes. By utilizing gradient angle information, a method of comparing local gradient values and reserving a local gradient maximum value is adopted to inhibit a non-maximum value; .
Optionally, in some other embodiments, step S3 specifically includes:
s31: obtaining structural elements, and performing corrosion operation on the target image according to the structural elements, wherein the size of the structural elements in the embodiment may be generally 5 × 5 pixels;
s32: the target image is expanded by using the structural element with the smaller size to obtain the connected domain, and the structural element with the smaller size in this embodiment may be about 3 × 3 pixels with an error of 1 pixel.
Isolated noise points can be eliminated after structural elements are corroded, and cavities can be filled to form connected domains after expansion operation is carried out on the target image, so that the edge enhancement effect is achieved.
As shown in fig. 2 and fig. 3, optionally, in some other embodiments, the step S4 specifically includes:
s41: selecting the end point of the major axis of the ellipse, and calculating the central coordinate, the semi-major axis and the included angle theta between the major axis of the ellipse and the x axis;
s42: calculating the length of the short shaft to obtain an equation of an ellipse;
s43: and obtaining a candidate annular target according to an equation of the ellipse.
Optionally, in some other embodiments, step S42 specifically includes:
s421: taking an edge point in any connected domain, and recording the edge point as an m point;
s422: repeating the step A421, and obtaining a corresponding beta value according to each m point;
s423: counting the occurrence frequency of each calculated beta value, and setting a threshold value T according to the frequency, wherein the threshold value in the embodiment is 60;
s424: m point pairsAn ellipse { O } is obtained when the corresponding β value meets the threshold Tx,Oy,α,β,θ}。
In this embodiment, the β value is the short radius of the ellipse, if the point pair selected at the beginning is two end points of the major axis of the ellipse, and the m points are also points on the ellipse, the β value calculated each time is the same, and there are many m points on the ellipse, the number of times of β occurrence reaches a peak value, a threshold value T is set, and if the accumulation (the number of times of occurrence) of a certain β exceeds T, the assumed major axis end point is determined to be correct, and the ellipse exists at the position.
Optionally, in some other embodiments, step S5 specifically includes:
s51: according to an ellipse { Ox,Oyα, θ } yields the degree of fit dis of the pixeli＝exp(-abs(Er1+Er2-1)), wherein:
Er1＝((Pix-Oix)·cos(θi)+(Piy-Oiy)·sin(θi))2/αi 2，
Er2＝(-(Pix-Oix)·sin(θi)+(Piy-Oiy)·cos(θi))2/βi 2；
s52: for all dis in connected domainiVoting for pixels less than 0.5, and taking the fitting degree as a weight;
s53: the connected domain with the maximum ticket value is used as the optimal fitting annular target
On the basis of the technical scheme, the invention can be further improved as follows.
It should be noted that the above embodiments are product embodiments corresponding to the above method embodiments, and for the description of each structural device and the optional implementation in this embodiment, reference may be made to the corresponding description in the above method embodiments, and details are not repeated herein.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. An adaptive detection method for an annular target facing an aircraft is characterized by comprising the following steps:
s1: acquiring a target image, and obtaining a candidate region according to the target image;
s2: carrying out edge detection on the target image to obtain a target edge;
s3: performing edge enhancement on the target edge by using morphological filtering;
s4: carrying out Hough transform on the candidate region to obtain a candidate annular target;
s5: and eliminating interference on the annular target to obtain an optimal fitting annular target.
2. The adaptive detection method for the annular target facing the aircraft according to claim 1, wherein step S2 specifically includes:
s21: carrying out image denoising on the target image by using Gaussian filtering;
s22: obtaining the gradient amplitude and the direction of the filtered target image according to the finite difference of the first-order partial derivatives;
s23: performing non-maximum suppression on the gradient amplitude;
s24: obtaining a high threshold and a low threshold, finding out the outline of the image edge in the target image according to the high threshold, and then connecting the edge break points of the outline according to the low threshold until the whole outline is closed to obtain the target edge.
3. The adaptive detection method for annular targets facing aircraft according to claim 2, characterized in that: step S3 specifically includes:
s31: obtaining structural elements, and carrying out corrosion operation on the target image according to the structural elements;
s32: and performing expansion operation on the target image by using the structural elements with smaller sizes to obtain a connected domain.
4. The adaptive detection method for annular targets facing aircraft according to claim 3, characterized in that: step S4 specifically includes:
s41: selecting the end point of the major axis of the ellipse, and calculating the central coordinate, the semi-major axis and the included angle theta between the major axis of the ellipse and the x axis;
s42: calculating the length of the short shaft to obtain an equation of an ellipse;
s43: and obtaining a candidate annular target according to the equation of the ellipse.
5. The adaptive detection method for annular targets facing aircraft according to claim 4, characterized in that: step S42 specifically includes:
s421: taking an edge point in any connected domain, and recording the edge point as an m point;
s422: repeating the step A421, and obtaining a corresponding beta value according to each m point;
s423: counting the occurrence frequency of each calculated beta value, and setting a threshold value T according to the frequency;
s424, obtaining the ellipse { O ] when the β value corresponding to the m point meets the threshold Tx,Oy,α,β,θ}。
6. The adaptive detection method for annular targets facing aircraft according to claim 5, characterized in that: step S5 specifically includes:
s51: according to the ellipse { Ox,Oyα, θ } yields the degree of fit dis of the pixeli＝exp(-abs(Er1+Er2-1)), wherein:
Er1＝((Pix-Oix)·cos(θi)+(Piy-Oiy)·sin(θi))2/αi 2，
Er2＝(-(Pix-Oix)·sin(θi)+(Piy-Oiy)·cos(θi))2/βi 2；
s52: for all dis in connected domainiVoting for pixels less than 0.5, and taking the fitting degree as a weight;
s53: the connected domain with the maximum ticket value is used as the optimal fitting annular target
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