CN114863258B - Method for detecting small target based on visual angle conversion in sea-sky-line scene - Google Patents
Method for detecting small target based on visual angle conversion in sea-sky-line scene Download PDFInfo
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Abstract
The invention discloses a method for detecting a small target based on visual angle conversion in a sea-sky-line scene, which comprises the following steps: acquiring an image to be detected; identifying a sea-sky-line; framing an effective rectangular area of an image to be detected; dividing the effective rectangular area into N 2 Each image block, wherein an overlapping area exists between every two adjacent image blocks; will N 2 Arranging the image blocks according to N rows and N columns to obtain a recombined image; detecting sea surface targets in the recombined image by using a deep learning network model, obtaining first coordinate information of each sea surface target, and combining the first coordinate information of each sea surface target into a first coordinate information set; and respectively converting the first coordinate information of each sea surface target into second coordinate information of the sea surface target in the image to be detected, and forming a second coordinate information set. The invention improves the detection precision of small target ships and small target buoys near sea-sky-lines through the selection of effective areas and the recombination of image blocks.
Description
Technical Field
The invention belongs to the technical field of target identification, and particularly relates to a method for detecting a small target based on visual angle conversion in a sea-sky-line scene.
Background
At present, the target detection technology plays more and more important roles in various fields and is gradually matured. The existing mature target detection technology is mostly based on a deep learning method. In general, a deep learning method scales input image data to a fixed size in an image preprocessing stage, and detects a target in the scaled image data by loading a model, for example, applying a relatively wide YOLO algorithm model and SSD algorithm model.
In the field of sea surface target detection, small target vessels, small target buoys and the like near sea aerials result in extremely small targets due to the long distance. At present, when a small target ship and a small target buoy near a sea antenna are detected, the following problems exist during the operation of a traditional deep learning network model:
1. inputting a long-distance sea antenna image of a deep learning network model, wherein the long-distance sea antenna image is an image with higher resolution, such as 5472 × 3648, however, the proportion of pixels of a small target near a sea antenna in the whole image is small, most of the pixels are regions such as sea wave sky and the like which are not concerned by a user, and therefore most of operation time is applied to unrelated regions when a traditional algorithm is operated; 2. in an image preprocessing stage of a deep learning network model, high-resolution image data is compressed to a resolution of 640 × 640 pixels, even in order to compress the computational efficiency to a resolution of 416 × 416 pixels, small targets near a sea antenna become smaller, and meanwhile, more interference pixel points are introduced, so that detection failure is caused; 3. under a complex sea-sky-line scene, airplanes, flying birds and the like in the sky near the sea-sky-line cause large interference to the identification of small target ships, small target buoys and the like near the sea-sky-line, and the interference cannot be eliminated.
Disclosure of Invention
The invention aims to overcome one or more defects in the prior art and provides a method for detecting a small target based on view angle conversion in a sea-sky-line scene.
The purpose of the invention is realized by the following technical scheme:
the method for detecting the small target based on the view angle conversion in the sea-sky-line scene specifically comprises the following steps:
acquiring an image to be detected;
identifying a sea-sky line in an image to be detected;
selecting an effective rectangular area of the image to be detected according to the sea antenna frame;
transversely dividing the effective rectangular region into N 2 The image block comprises image blocks, wherein an overlapping area exists between two adjacent image blocks, the transverse widths of all the image blocks are the same, and the value of N is a positive integer greater than one;
will N 2 Arranging the image blocks according to N rows and N columns to obtain a recombined image of the image to be detected;
detecting sea surface targets in the recombined image by using a pre-constructed deep learning network model, obtaining first coordinate information of each sea surface target, and combining the first coordinate information of each sea surface target into a first coordinate information set, wherein the first coordinate information is the coordinate information of the sea surface target in the recombined image;
and respectively converting the first coordinate information of each sea surface target into second coordinate information of the sea surface target, and combining the second coordinate information of each sea surface target into a second coordinate information set, wherein the second coordinate information is the coordinate information of the sea surface target in the image to be detected.
In a further improvement, after the step of respectively converting the first coordinate information of each sea surface target into the second coordinate information of the sea surface target and combining the second coordinate information of each sea surface target into the second coordinate information set, the method further includes the following steps:
and removing the repeated sea surface target coordinate information in the second coordinate information set.
Further improved, the sea-sky line in the image to be detected is identified, specifically including:
calculating the vertical gradient of the image to be detected, and extracting to obtain edge features;
obtaining an edge straight line segment according to the edge characteristics;
screening the edge straight line segments according to a preset first threshold value to obtain target straight line segments;
aggregating the target straight line segments by adopting a preset clustering algorithm to obtain a sea-sky line segment set;
and fitting the sea-sky-line segment set by adopting a least square method to obtain the sea-sky-line.
Further improved, the selecting the effective rectangular area of the image to be detected according to the sea antenna frame specifically comprises:
identifying the intersection point of the sea-sky-line and the left boundary line of the image to be detected, and recording the coordinates of the intersection point as
；
Identifying the intersection point of the sea-sky-line and the right boundary line of the image to be detected, and recording the coordinates of the intersection point as
；
Calculating the coordinates of the center point of the sea-sky-line according to the coordinates of the intersection point of the sea-sky-line and the left boundary line of the image to be detected and the coordinates of the intersection point of the sea-sky-line and the right boundary line of the image to be detected
Wherein
， 
；
Judgment equation
Whether the result is true or not; if yes, determining a first distance parameter
And a second distance parameter
If not, determining a first distance parameter
And a second distance parameter
Wherein W is the width of the image to be detected;
the coordinates of the upper boundary line are calculated from the first distance parameter d1
And calculating the coordinates of the lower boundary line based on the second distance parameter d2
；
And forming an effective rectangular area of the image to be detected by the upper boundary line, the lower boundary line, the left boundary line of the image to be detected and the right boundary line of the image to be detected.
In a further improvement, the first coordinate information of the sea surface object includes coordinate information of a center point of the sea surface object, height information of an object frame for framing the sea surface object, and width information of the object frame.
In a further improvement, the transverse widths of the overlapping regions are all first preset intervals, and the first preset intervals are positive integers.
In a further improvement, the calculating the vertical gradient of the image to be detected specifically includes:
calculating the vertical gradient of the image to be detected by a kernel operator, wherein
Weight value
。
In a further improvement, the method further includes, after removing the repeated sea surface target coordinate information in the second coordinate information set, the following steps:
and removing the coordinate information of the interference target in the second coordinate information set to obtain a final coordinate information set of the sea surface target in the image to be detected, wherein the interference target comprises an airplane and a bird in the sky.
In a further improvement, the removing the coordinate information of the interference target in the second coordinate information set specifically includes:
calculating the vertical coordinate of the lower right corner of each sea surface target based on the second coordinate information set, and recording the vertical coordinate of the lower right corner as the vertical coordinate
；
Judgment of
If the sea surface target is not the sea surface target, keeping the coordinate information of the sea surface target in a second coordinate information set, and if the sea surface target is not the sea surface target, removing the coordinate information of the sea surface target from the second coordinate information set;
wherein,
and m is the number of the remaining sea surface targets after the repeated sea surface target coordinate information in the second coordinate information set is removed.
The invention has the following beneficial effects:
1) the sea antenna in the image to be detected is fitted, an effective rectangular area of the image to be detected is selected based on the sea antenna, the effective rectangular area is transversely divided into a plurality of image blocks, meanwhile, in order to avoid cutting of a sea surface target, when the effective rectangular area is transversely divided, two adjacent image blocks are partially overlapped, all the image blocks are recombined, the recombined image is input into a pre-constructed deep learning network model to be subjected to target detection, and the detected coordinates of the sea surface target are converted into the coordinate position in the image to be detected.
According to the method, the interference of most invalid information is eliminated through the selection of the effective area, and meanwhile, compared with the original image to be detected, the resolution of the sea surface target input into the deep learning network model is increased by the recombined image, so that the accuracy of the detection of the sea surface target is improved, and the detection of small target ships, small target buoys and the like near the sea antenna under the complex sea antenna scene is completed.
2) And calculating the vertical gradient of the image to be detected through a kernel operator, and combining a least square method to realize the accurate simulation of the sea-sky-line.
3) And the precision of sea surface target detection is further improved by removing the repeated target and the interference target coordinate information in the second coordinate information set.
Drawings
FIG. 1 is a flow chart of a method for detecting small targets based on perspective transformation in a sea-sky-line scenario;
FIG. 2 is a schematic diagram of a division of an effective rectangular area;
fig. 3 is a schematic diagram of a recombined image obtained by recombining image blocks.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, the present embodiment provides a method for detecting a small target based on view angle conversion in a sea-sky-line scene, which is used for detecting a small sea-surface target such as a small ship and a small buoy near a sea-sky-line, and specifically includes the following steps:
and S1, acquiring an image to be detected.
And S2, identifying the sea-sky-line in the image to be detected. In a common embodiment, before the sea-sky-line in the image to be detected is identified, filtering and denoising processing is further performed on the image to be detected.
In the present embodiment, S2 includes the following sub-steps:
and a substep S21 of calculating the vertical gradient of the image to be detected and extracting to obtain edge features.
And a substep S22 of obtaining an edge straight line segment according to the edge characteristics.
And a substep S23 of screening the edge straight line segment according to a preset first threshold value to obtain a target straight line segment.
And a substep S24, adopting a preset k-meas clustering algorithm to aggregate the target straight line segments to obtain a sea-sky line segment set.
And a substep S25 of fitting the sea-sky-line segment set by adopting a least square method to obtain the sea-sky-line.
And S3, selecting an effective rectangular area of the image to be detected according to the sea antenna frame.
In the present embodiment, S3 includes the following sub-steps:
substep S31, identifying the intersection point of the sea-sky-line and the left boundary line of the image to be detected, and recording the coordinates of the intersection point as
。
Substep S32, identifying the intersection point of the sea-sky-line and the right boundary line of the image to be detected, and recording the coordinates of the intersection point as
。
Substep S33, calculating coordinates of center point of sea-sky-line according to coordinates of intersection point of sea-sky-line and left boundary line of image to be detected and coordinates of intersection point of sea-sky-line and right boundary line of image to be detected
Wherein
， 
。
Substep S34, determining equation
Whether the result is true or not; if yes, determining a first distance parameter
And a second distance parameter
At this time, the first value mode of the first distance parameter d1 and the second distance parameter d2 is adopted; if not, determining a first distance parameter
And a second distance parameter
In this case, the first distance parameter d1 and the second distance parameter d2 have the second value. Wherein W is the width of the image to be detected. The step determines the value mode of the first distance parameter d1 and the second distance parameter d2 according to the calculation of the sea-sky-line inclination angle. When the sea-sky-line level or the inclination angle of the sea-sky-line is less than 10 degrees, the first distance parameter d1 and the second distance parameter d2 adopt the second value mode, and when the inclination angle of the sea-sky-line is more than or equal to 10 degrees, the first distance parameter d1 and the second distance parameter d2 adopt the first value mode. In the first value taking mode, the calculation coefficient 1.2 and the calculation coefficient 1.5 are preferable values based on experience, can be adjusted according to specific situations, and meanwhile, sea surface targets close to the lens are relatively large, so that the distance parameter on the ocean side is set to be larger, the sea surface targets far away from the lens are relatively smaller, and the distance parameter on the sky side is set to be smaller by oneAnd (3) a plurality of. The calculation factor in the second distance parameter d2 is 1.5 and the calculation factor in the first distance parameter d1 is 1.2.
Substep S35, calculating coordinates of the upper boundary line based on the first distance parameter d1
And calculating the coordinates of the lower boundary line based on the second distance parameter d2
。
Substep S36, forming an effective rectangular region of the image to be detected by the upper boundary line, the lower boundary line, the left boundary line of the image to be detected and the right boundary line of the image to be detected.
In addition, the coordinates of the left boundary line of the image to be detected
Coordinates of the right boundary line of the image to be detected
. In this embodiment, it is preferable
Thus, therefore, it is
。
S4, laterally dividing the effective rectangular area into N by N image blocks, where an overlapping area with a lateral width of a first preset interval d3 exists between any two adjacent image blocks, and the lateral widths of all the image blocks are the same, where a value of N is a positive integer greater than one, and a value of the first preset interval d3 is a positive integer.
The calculation of the transverse width w1 of an image block is as follows:
calculating a first intermediate parameter
N is positiveAn integer number;
calculating the lateral width of an image block
W1 is a positive integer, specifically: take the positive integer closest to the actual calculated value of w1 and greater than the actual value of w 1.
The calculation process of N in the transverse segmentation of the effective rectangular region is as follows:
if it is
If N is equal to 2; if it is
If N is 3; if it is
If so, then N takes the value of 4; if it is
Then N takes the value 5.
And S5, arranging the N-by-N image blocks according to N rows and N columns to obtain a recombined image of the image to be detected.
S6, inputting the recombined images into a pre-constructed deep learning network model for target detection, identifying sea surface targets in the recombined images, obtaining first coordinate information of the sea surface targets, and combining the obtained first coordinate information of the sea surface targets into a first coordinate information set. The first coordinate information is coordinate information of the sea surface target in the recombined image. The deep learning network model is used for detecting sea surface targets in a common embodiment. The first coordinate information of the sea surface target comprises the coordinate information of the center point of the sea surface target, the height information of a target frame for framing the sea surface target and the width information of the target frame.
And S7, converting the first coordinate information of each sea surface target into second coordinate information of the sea surface target respectively, and combining the second coordinate information of each sea surface target into a second coordinate information set. And the second coordinate information is the coordinate information of the sea surface target in the image to be detected. And the second coordinate information of the sea surface target comprises the coordinate information of the central point of the sea surface target in the image to be detected, the height information of a target frame for framing the sea surface target and the width information of the target frame.
Preferably, the following steps are also included after S7:
and S8, removing the repeated sea surface target coordinate information in the second coordinate information set. Because two adjacent image blocks have an overlapping area with the transverse width of the first preset interval d3, after the target detection is performed through the deep learning network model and then the coordinate conversion is performed, a plurality of sea surface targets with the same coordinate information may appear, and the precision of the sea surface target detection is improved by screening out the repeated sea surface target coordinate information.
Preferably, the vertical gradient of the image to be detected is calculated, specifically:
calculating the vertical gradient of the image to be detected by a kernel operator, wherein
Weighted value
. The larger the weight value k is, the larger the gradient value in the vertical direction is, and k takes a value of 1 in this embodiment.
Preferably, the following steps are also included after S8:
and S9, removing the coordinate information of the interference target in the second coordinate information set to obtain a final coordinate information set of the sea surface target in the image to be detected. The interference target includes an airplane, a bird and the like in the sky near the sea-sky-line.
Removing the coordinate information of the interference target in the second coordinate information set, which specifically comprises the following substeps:
s91, calculating the vertical coordinate of the lower right corner of each sea surface target based on the second coordinate information set, and recording the vertical coordinate of the lower right corner as
。
S92, judgment
And if not, removing the coordinate information of the sea surface target from the second coordinate information set.
Wherein,
and m is the remaining sea surface target coordinate number after the repeated sea surface target coordinate information in the second coordinate information set is removed, namely the remaining sea surface target number. And the accuracy of sea surface target detection is further improved by screening out the coordinate information of the interference target in the second coordinate information set.
The first preset interval d3 is preferably greater than half of the maximum width of the sea surface targets such as various small target vessels and small target buoys, based on the type of small target vessels and small target buoys to be detected.
With reference to fig. 2 and 3, the following describes a generation process of a reconstructed image (four image blocks are arranged in two rows and two columns in a rectangular manner) and a specific calculation process of converting each coordinate information in the first coordinate information set to a corresponding coordinate position in an image to be detected, by dividing the effective rectangular area into four parts by transverse division.
The generation process of the recombined image comprises the following steps:
four image blocks obtained by transversely dividing the effective rectangular area are sequentially defined as a first image block, a second image block, a third image block and a fourth image block from left to right;
placing the first image blocks in a first row and a first column of rectangular arrangement, placing the second image blocks in a second row and a second column of rectangular arrangement, placing the third image blocks in a first row and a first column of rectangular arrangement, and placing the fourth image blocks in a second row and a second column of rectangular arrangement to obtain a recombined image;
a specific calculation process for converting each piece of coordinate information in the first set of coordinate information to a corresponding coordinate position in the image to be detected:
converting the coordinate information of the sea surface target detected in the first image block in the recombined image into the coordinate position in the image to be detected, wherein the calculation formula is as follows:
；
converting the coordinate information of the sea surface target detected in the second image block in the recombined image into the coordinate position in the image to be detected, wherein the calculation formula is as follows:
converting the coordinate information of the sea surface target detected in the third image block in the recombined image into the coordinate position in the image to be detected, wherein the calculation formula is as follows:
converting the coordinate information of the sea surface target detected in the fourth image block in the recombined image into the coordinate position in the image to be detected, wherein the calculation formula is as follows:
；
wherein,
the coordinates of the sea surface object in the recombined image,
for coordinates of sea surface objects detected by the reconstructed image in the image to be detected, and
。
the method for detecting the small target ship in the sea-sky-line scene adopts three means of selecting an effective rectangular area, transversely dividing an image to be detected and recombining image blocks, belongs to a conversion realization of a machine visual angle in deep learning, saves the operation amount of a deep learning network model, increases the resolution ratio of a sea surface small target in image data input into the deep learning network model, greatly improves the detection precision of the small target ship and a small target buoy near the sea-sky-line, overcomes the defects of the existing target detection scheme mentioned in the background technology, and has a larger application prospect.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. The method for detecting the small target based on the visual angle conversion in the sea-sky-line scene is characterized by comprising the following steps:
acquiring an image to be detected;
identifying a sea-sky line in an image to be detected;
selecting an effective rectangular area of the image to be detected according to the sea antenna frame;
transversely dividing the effective rectangular region into N 2 The image block comprises image blocks, wherein an overlapping area exists between two adjacent image blocks, the transverse widths of all the image blocks are the same, and the value of N is a positive integer greater than one;
will N 2 An instituteArranging the image blocks according to N rows and N columns to obtain a recombined image of the image to be detected;
detecting sea surface targets in the recombined image by using a pre-constructed deep learning network model, obtaining first coordinate information of each sea surface target, and combining the first coordinate information of each sea surface target into a first coordinate information set, wherein the first coordinate information is the coordinate information of the sea surface target in the recombined image;
respectively converting the first coordinate information of each sea surface target into second coordinate information of the sea surface target, and combining the second coordinate information of each sea surface target into a second coordinate information set, wherein the second coordinate information is the coordinate information of the sea surface target in the image to be detected;
the effective rectangular area of waiting to detect the image is elected according to sea antenna frame specifically includes:
identifying the intersection point of the sea-sky-line and the left boundary line of the image to be detected, and recording the coordinates of the intersection point as
；
Identifying the intersection point of the sea-sky-line and the right boundary line of the image to be detected, and recording the coordinates of the intersection point as
；
Calculating the coordinates of the center point of the sea-sky-line according to the coordinates of the intersection point of the sea-sky-line and the left boundary line of the image to be detected and the coordinates of the intersection point of the sea-sky-line and the right boundary line of the image to be detected
Wherein
，
；
Judgment equation
Whether the result is true; if yes, determining a first distance parameter
And a second distance parameter
If not, determining a first distance parameter
And a second distance parameter
Wherein W is the width of the image to be detected;
the coordinates of the upper boundary line are calculated from the first distance parameter d1
And calculating the coordinates of the lower boundary line based on the second distance parameter d2
；
And forming an effective rectangular area of the image to be detected by the upper boundary line, the lower boundary line, the left boundary line of the image to be detected and the right boundary line of the image to be detected.
2. The method for detecting small targets based on view angle transformation in sea-sky-line scene as claimed in claim 1, wherein the step of converting the first coordinate information of each sea-surface target into the second coordinate information of the sea-surface target and combining the second coordinate information of each sea-surface target into the second coordinate information set further comprises the following steps:
and removing the repeated sea surface target coordinate information in the second coordinate information set.
3. The method for detecting the small target based on the view angle conversion in the sea-sky-line scene as claimed in claim 1, wherein the identifying the sea-sky-line in the image to be detected specifically comprises:
calculating the vertical gradient of the image to be detected, and extracting to obtain edge features;
obtaining an edge straight line segment according to the edge characteristics;
screening the edge straight line segments according to a preset first threshold value to obtain target straight line segments;
aggregating the target straight line segments by adopting a preset clustering algorithm to obtain a sea-sky line segment set;
and fitting the sea-sky-line segment set by adopting a least square method to obtain the sea-sky-line.
4. The method for detecting the small target based on the view angle conversion in the sea-sky-line scene according to claim 1, wherein the first coordinate information of the sea-surface target includes coordinate information of a center point of the sea-surface target, height information of a target frame for framing the sea-surface target, and width information of the target frame.
5. The method for detecting the small target based on the view angle conversion in the sea-sky-line scene as claimed in claim 1, wherein the horizontal widths of the overlapping regions are all a first preset interval, and the first preset interval is a positive integer.
6. The method for detecting the small target based on the view angle conversion in the sea-sky-line scene as claimed in claim 3, wherein the calculating the vertical gradient of the image to be detected specifically comprises:
calculating the vertical gradient of the image to be detected by a kernel operator, wherein
Weighted value
。
7. The method for detecting the small target based on the view angle transformation in the sea-sky-line scene according to claim 2, wherein the step of removing the repeated sea surface target coordinate information in the second coordinate information set further comprises the following steps:
and removing the coordinate information of the interference target in the second coordinate information set to obtain a final coordinate information set of the sea surface target in the image to be detected, wherein the interference target comprises an airplane and a bird in the sky.
8. The method for detecting the small target based on the perspective transformation in the sea-sky-line scene according to claim 7, wherein the removing the coordinate information of the interfering target in the second coordinate information set specifically includes:
calculating the vertical coordinate of the lower right corner of each sea surface target based on the second coordinate information set, and recording the vertical coordinate of the lower right corner as the vertical coordinate
；
Judgment of
If the sea surface target is not the sea surface target, keeping the coordinate information of the sea surface target in a second coordinate information set, and if the sea surface target is not the sea surface target, removing the coordinate information of the sea surface target from the second coordinate information set;
wherein,
and m is the number of the remaining sea surface targets after the repeated sea surface target coordinate information in the second coordinate information set is removed.
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