CN111461013A - Real-time fire scene situation sensing method based on unmanned aerial vehicle - Google Patents
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Abstract
The invention discloses a real-time fire scene situation perception method based on an unmanned aerial vehicle, which comprises the following steps: s1, automatically planning and generating a plurality of air routes by the unmanned aerial vehicle and taking off; s2, in each route, performing frame extraction on the images, and performing real-time matching, splicing and fusion to generate a strip chart; s3, matching, splicing and fusing the two strip graphs in adjacent routes to generate a big graph; s4, after the task is finished, overlapping the spliced normal posture image to a satellite map, and carrying out fire source detection on the normal posture image; and S5, finally, marking the orientation information of the fire source. According to the invention, the camera azimuth information recorded when the unmanned aerial vehicle takes a picture is utilized, the images are matched, spliced and fused through an image processing algorithm, and the situation map and the fire source related information are obtained immediately after the flight is finished, so that effective information is provided for ground monitoring personnel, the rapid on-site situation perception of the unmanned aerial vehicle is realized, and the on-site rescue efficiency is improved.
Description
Technical Field
The invention belongs to the field of real-time fire scene situation perception, and particularly relates to a real-time fire scene situation perception method based on an unmanned aerial vehicle.
Background
In recent years, along with the rapid development of unmanned aerial vehicle trade, unmanned aerial vehicle also more and more is applied to fields such as forestry, fire control, along with the development of unmanned aerial vehicle technique, the demand in segment market field increases, and unmanned aerial vehicle's application is showing more and more abundant possibility, and aerial photograph, plant protection replace the electric power workman to patrol line and so on, and unmanned aerial vehicle's application is more and more extensive, is promoting the development in each field.
As a novel middle and low altitude real-time television imaging and infrared imaging rapid acquisition system, the unmanned aerial vehicle has particular unique advantages in aspects of forestry resource investigation, ecological environment, forest fire prevention, forest pest control and the like of areas where vehicles and people cannot reach, and in the aspect of forest fire control, as powerful supplement of the existing forest monitoring means, the unmanned aerial vehicle can rapidly position fire points when forest fires occur to determine fires, can monitor and prevent forest fires, and can monitor and control the forest fires
Therefore, the real-time fire scene situation sensing method based on the unmanned aerial vehicle is provided, in the field rescue stage, a fireman needs to quickly know fire scene situation information, carries the pan-tilt camera by the unmanned aerial vehicle, carries out image splicing in real time, quickly generates an orthostatic situation image, automatically analyzes fire source information, and has important significance for improving rescue efficiency.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a real-time fire scene situation perception method based on an unmanned aerial vehicle.
In order to achieve the purpose, the invention provides the following technical scheme:
a real-time fire scene situation perception method based on an unmanned aerial vehicle comprises the following steps:
s1, automatically planning and generating a plurality of air routes by the unmanned aerial vehicle and taking off;
s2, in each route, performing frame extraction on the images, and performing real-time matching, splicing and fusion to generate a strip chart;
s3, matching, splicing and fusing the two strip graphs in adjacent routes to generate a big graph;
s4, after the task is finished, overlapping the spliced normal posture image to a satellite map, and carrying out fire source detection on the normal posture image;
and S5, finally, marking the orientation information of the fire source.
Preferably, the fire source is marked in step S5, the image mosaic technology is used to generate the fire scene situation diagram, and the image processing technology is used to mark and monitor the fire source.
Preferably, in each route, the method for matching, splicing and fusing adjacent images to generate the strip chart includes:
1) SURF feature point detection is carried out on two adjacent frames of images, matching denoising is carried out based on a RANSAC method, and a homography matrix is generated;
2) the matching quality analysis is carried out and,
if the matching quality is not good, the matching based on a plurality of characteristic blocks is carried out again on the two frames of images to obtain a homography matrix;
if the two images cannot be matched, calculating a homography matrix by combining pos information of the two images;
trimming the homography matrix to enable the homography matrix to be suitable for meeting the splicing of the long sequence images;
3) and splicing according to the homography matrix, and fusing images near the seam.
Preferably, in the adjacent routes, the method for matching, splicing and fusing the two strip charts to generate the large chart comprises the following steps:
1) in the overlapping region of the strip chart 1 and the strip chart 2, a fixed arrangement and a fixed number (n) of ROI interesting regions are extracted as a template set
2) Respectively matching the templates by using a normalized correlation coefficient matching method
Matching with the strip chart 2, and obtaining a coordinate set C of a matching central point in the strip chart 21～Cn；
3) To coordinate set C1～CnPerforming cluster analysis to remove outliers and retain m interior points C1～Cm；
4) To coordinate set C1～CmPerforming a least squares based line fit to obtain equation L1；
5) To coordinate set C1～CmAnd line L1Separately calculate C1～CmTo L1And screening out the coordinates of which the distance exceeds a threshold value to obtain p coordinate sets C subjected to denoising1～Cp；
6) To coordinate set C1～CpAnd performing least square method-based line fitting again to obtain line equation L2；
7) According to the coordinate set C1～CpAnd equation L of a straight line2From L2Calculating the rotation and translation parameters of the strip chart 2 relative to the strip chart 1 by the slope;
8) and splicing the strip chart 2 and the strip chart 1 according to the rotation and translation parameters, and fusing images near the seam.
Preferably, the method of matching mass analysis comprises:
1) judging the number of the matched feature point pairs after denoising, and considering that the matching quality is poor when the number of the matched feature point pairs is lower than a certain threshold;
2) if the number of the feature point pairs is enough, the mapped 4 vertexes form a quadrangle according to the homography matrix, the shape characteristic of the quadrangle is judged, and whether the matching quality is too poor is judged according to the characteristics of the side length, the length-width ratio, the ratio of the upper side length to the lower side length and the like of the quadrangle.
Preferably, the fusion method adopts a feather algorithm, and adds the pixel values of the two images according to a certain weight value in an overlapping area near the seam to synthesize a new image.
The invention has the technical effects and advantages that: according to the real-time fire scene situation sensing method based on the unmanned aerial vehicle, before taking off, only simple mouse operation is needed, the polygon vertex of the area to be detected is dragged on the map of the ground station, and then the air route can be automatically generated; in the flight process, the holder is kept vertically downward, and the image and POS information are transmitted to the ground station in real time through a data link; meanwhile, the ground station end processes the images in real time and splices the images in real time to generate an orthomorphic image. In the image splicing process, the SURF + RANSAC and a plurality of feature block matching algorithms are adopted, so that a good matching effect is still achieved for a feature scarce area, forced splicing processing is carried out on the condition of matching failure, and the algorithm can be ensured to be normally operated all the time in a flight phase. In order to ensure real-time performance, the operation speed is optimized for each stage of image processing, and the memory occupation condition during operation is also independently optimized. In addition, for the fire source extraction, the color characteristics of the fire source are obvious, so the algorithm combines the characteristics of two color spaces of HSV + RGB, and the reliable extraction of the fire source is realized. The invention realizes the real-time and reliable situation perception of the unmanned aerial vehicle to disaster areas such as fire fields and the like, and improves the rescue efficiency.
Drawings
FIG. 1 is a schematic diagram of fire source detection and marking effects of a real-time fire scene situation perception method based on an unmanned aerial vehicle;
FIG. 2 is a flow chart of the real-time fire scene situation awareness method based on the unmanned aerial vehicle.
In the figure: 1. a histogram of the stripes; 2. a satellite map; 3. extracting a frame image; 4. a fire source.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
A real-time fire scene situation perception method based on an unmanned aerial vehicle comprises the following steps:
s1, automatically planning and generating a plurality of air routes by the unmanned aerial vehicle and taking off;
s2, in each route, performing frame extraction on the images, and performing real-time matching, splicing and fusion to generate a strip chart;
s3, matching, splicing and fusing the two strip graphs in adjacent routes to generate a big graph;
s4, after the task is finished, overlapping the spliced normal posture image to a satellite map, and carrying out fire source detection on the normal posture image;
and S5, finally, marking the orientation information of the fire source.
And marking the fire source in the step S5, generating a fire scene situation diagram by adopting an image splicing technology, and marking and monitoring the fire source by adopting an image processing technology.
In each route, the method for matching, splicing and fusing adjacent images to generate the strip chart comprises the following steps:
1) SURF feature point detection is carried out on two adjacent frames of images, matching denoising is carried out based on a RANSAC method, and a homography matrix is generated;
2) the matching quality analysis is carried out and,
if the matching quality is not good, the matching based on a plurality of characteristic blocks is carried out again on the two frames of images to obtain a homography matrix;
if the two images cannot be matched, calculating a homography matrix by combining pos information of the two images;
trimming the homography matrix to enable the homography matrix to be suitable for meeting the splicing of the long sequence images;
3) and splicing according to the homography matrix, and fusing images near the seam.
In adjacent routes, the method for matching, splicing and fusing the two strip graphs to generate the large graph comprises the following steps:
1) in the overlapping region of the strip chart 1 and the strip chart 2, a fixed arrangement and a fixed number (n) of ROI interesting regions are extracted as a template set
2) Respectively matching the templates by using a normalized correlation coefficient matching method
Matching with the strip chart 2, and obtaining a coordinate set C of a matching central point in the strip chart 21～Cn；
3) To coordinate set C1～CnPerforming cluster analysis to remove outliers and retain m interior points C1～Cm；
4) To coordinate set C1～CmPerforming a least squares based line fit to obtain equation L1；
5) To coordinate set C1～CmAnd line L1Separately calculate C1～CmTo L1And screening out the coordinates of which the distance exceeds a threshold value to obtain p coordinate sets C subjected to denoising1～Cp；
6) To coordinate set C1～CpAnd performing least square method-based line fitting again to obtain line equation L2；
7) According to the coordinate set C1～CpAnd equation L of a straight line2From L2Calculating the rotation and translation parameters of the strip chart 2 relative to the strip chart 1 by the slope;
8) and splicing the strip chart 2 and the strip chart 1 according to the rotation and translation parameters, and fusing images near the seam.
The method for matching quality analysis comprises the following steps:
1) judging the number of the matched feature point pairs after denoising, and considering that the matching quality is poor when the number of the matched feature point pairs is lower than a certain threshold;
2) if the number of the feature point pairs is enough, the mapped 4 vertexes form a quadrangle according to the homography matrix, the shape characteristic of the quadrangle is judged, and whether the matching quality is too poor is judged according to the characteristics of the side length, the length-width ratio, the ratio of the upper side length to the lower side length and the like of the quadrangle.
The fusion method adopts a feather algorithm, and pixel values of the two images are added according to a certain weight value in an overlapping area near a seam to synthesize a new image.
Example 2
Referring to fig. 1-2, the present invention provides a real-time fire scene situation awareness method based on an unmanned aerial vehicle, comprising the following steps:
firstly, automatically planning a route for an area to be observed through a ground station;
and secondly, splicing adjacent images in the flight process of the flight path. And extracting features through a SURF algorithm, and filtering noise points through RANSAC to obtain accurate and reliable matching points. If the image with the scarce characteristics appears, a multi-characteristic block matching algorithm is adopted to realize matching. In order to avoid errors caused by incomplete synchronization of pos data and images when the airplane turns at the corner of the flight path, the splicing of the two strips is performed in enough time, and therefore the splicing is not performed on the flight path of the corner. After each route finishes flying, generating a strip chart;
thirdly, splicing the strips in a corner route by using a multi-feature block matching algorithm;
finally, after the task is finished, generating an orthostatic map and overlapping the orthostatic map to a satellite map; and automatically detecting and marking the fire scene in the image through a ground station button to obtain the area of the fire and the length of the fire line. In the ground station, the situation map can be observed from various angles by the operation of the mouse wheel and the keys.
In summary, the following steps: according to the invention, the camera azimuth information recorded when the unmanned aerial vehicle takes a picture is utilized, the images are matched, spliced and fused through an image processing algorithm, and a situation map which is nearly seamless is obtained immediately after the flight is finished and the relevant information of the fire source is obtained, so that effective information is provided for ground monitoring personnel. Compared with the prior art, the unmanned aerial vehicle rapid scene situation sensing method and the unmanned aerial vehicle rapid scene situation sensing device realize rapid scene situation sensing of the unmanned aerial vehicle, and improve the scene rescue efficiency.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (6)
1. A real-time fire scene situation perception method based on an unmanned aerial vehicle is characterized in that: the method comprises the following steps:
s1, automatically planning and generating a plurality of air routes by the unmanned aerial vehicle and taking off;
s2, in each route, performing frame extraction on the images, and performing real-time matching, splicing and fusion to generate a strip chart;
s3, matching, splicing and fusing the two strip graphs in adjacent routes to generate a big graph;
s4, after the task is finished, overlapping the spliced normal posture image to a satellite map, and carrying out fire source detection on the normal posture image;
and S5, finally, marking the orientation information of the fire source.
2. The real-time fire scene situation awareness method based on the unmanned aerial vehicle as claimed in claim 1, wherein: and marking the fire source in the step S5, generating a fire scene situation diagram by adopting an image splicing technology, and marking and monitoring the fire source by adopting an image processing technology.
3. The real-time fire scene situation awareness method based on the unmanned aerial vehicle as claimed in claim 3, wherein: in each route, the method for matching, splicing and fusing adjacent images to generate the strip chart comprises the following steps:
1) SURF feature point detection is carried out on two adjacent frames of images, matching denoising is carried out based on a RANSAC method, and a homography matrix is generated;
2) the matching quality analysis is carried out and,
if the matching quality is not good, the matching based on a plurality of characteristic blocks is carried out again on the two frames of images to obtain a homography matrix;
if the two images cannot be matched, calculating a homography matrix by combining pos information of the two images;
trimming the homography matrix to enable the homography matrix to be suitable for meeting the splicing of the long sequence images;
3) and splicing according to the homography matrix, and fusing images near the seam.
4. The real-time fire scene situation awareness method based on the unmanned aerial vehicle as claimed in claim 3, wherein: in the adjacent routes, the method for matching, splicing and fusing the two strip charts to generate the large chart comprises the following steps:
1) in the overlapping region of the strip chart 1 and the strip chart 2, a fixed arrangement and a fixed number (n) of ROI interesting regions are extracted as a template set
2) Respectively matching the templates by using a normalized correlation coefficient matching method
Matching with the strip chart 2, and obtaining a coordinate set C of a matching central point in the strip chart 21～Cn；
3) To coordinate set C1～CnPerforming cluster analysis to remove outliers and retain m interior points C1～Cm；
4) To coordinateCollection C1～CmPerforming a least squares based line fit to obtain equation L1；
5) To coordinate set C1～CmAnd line L1Separately calculate C1～CmTo L1And screening out the coordinates of which the distance exceeds a threshold value to obtain p coordinate sets C subjected to denoising1～Cp；
6) To coordinate set C1～CpAnd performing least square method-based line fitting again to obtain line equation L2；
7) According to the coordinate set C1～CpAnd equation L of a straight line2From L2Calculating the rotation and translation parameters of the strip chart 2 relative to the strip chart 1 by the slope;
8) and splicing the strip chart 2 and the strip chart 1 according to the rotation and translation parameters, and fusing images near the seam.
5. The real-time fire scene situation awareness method based on the unmanned aerial vehicle as claimed in claim 3, wherein: the method for matching quality analysis comprises the following steps:
1) judging the number of the matched feature point pairs after denoising, and considering that the matching quality is poor when the number of the matched feature point pairs is lower than a certain threshold;
2) if the number of the feature point pairs is enough, the mapped 4 vertexes form a quadrangle according to the homography matrix, the shape characteristic of the quadrangle is judged, and whether the matching quality is too poor is judged according to the characteristics of the side length, the length-width ratio, the ratio of the upper side length to the lower side length and the like of the quadrangle.
6. The real-time fire scene situation awareness method based on the unmanned aerial vehicle as claimed in claim 4, wherein: the fusion method adopts a feather algorithm, and pixel values of two images are added according to a certain weight value in an overlapping area near a seam to synthesize a new image.
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