CN112085651B - Automatic shock wave detection and tracking algorithm based on image self-adaptive threshold and feature extraction - Google Patents
Automatic shock wave detection and tracking algorithm based on image self-adaptive threshold and feature extraction Download PDFInfo
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Abstract
The invention discloses an automatic shock wave detection and tracking algorithm based on image self-adaptive threshold and feature extraction. The method converts the original schlieren or shadow image into 8-bit gray schlieren or shadow image respectively; preprocessing an 8-bit gray shade or shadow image by utilizing background image deduction, image filtering, image enhancement and sub-pixel interpolation in a frequency domain; converting the gray level schlieren or shadow image into a binary schlieren or shadow image by using an adaptive threshold algorithm; detecting all characteristic contours in the binary striae shadow or shadow image by using a contour detection algorithm; and fitting the shock wave characteristic profile by using the pixel vector coordinates of the shock wave characteristic profile according to the shock wave shape characteristics, thereby further reducing the error of shock wave detection. The method can improve the robustness, the automation degree and the precision of the multi-sequence schlieren or shadow image shock wave automatic detection and tracking.
Description
Technical Field
The invention belongs to the technical field of aeronautics and astronautics industry aerodynamics, and particularly relates to an automatic shock wave detection and tracking algorithm based on image self-adaptive threshold and feature extraction.
Background
The schlieren and shadow technology is used as a traditional flow field display means with low cost and high reliability, is an important test method for wind tunnel test and combustion field research in current engineering application, and is also an important tool for aerodynamics research on compressible flow characteristics such as shock waves, shear layers, vortex and the like.
Generally, the schlieren and shadow technique is only used as a qualitative flow field display analysis means. However, with the refinement of the aerodynamic research, in the field of the aerodynamic shock wave research, the unsteady shock wave characteristics such as the shock wave propagation speed, the shock wave oscillation frequency, the shock wave deformation and the like can be quantitatively analyzed based on the multi-sequence image result obtained by the high-speed schlieren or the shadow.
In order to accurately, efficiently and automatically detect, extract and track shock waves from high-speed schlieren or shadow images, a shock wave automatic detection and tracking image processing algorithm needs to be established to process multi-sequence schlieren or shadow sequence images. Some foreign scholars make a lot of beneficial attempts in the field, Estruch et al apply Canny edge extraction algorithm to automatic shock wave tracking detection, and evaluate the unsteady oscillation characteristics of the shock wave in the shock wave boundary layer interference based on multi-sequence schlieren images. Fujimoto et al propose an image processing algorithm based on feature point tracking, which tracks the shock wave position by identifying the feature points on the shock wave shape. The Curvature Scale Space (CSS) technique, a modern tool in the field of computer vision, extracts curvature zero crossings on a curve at varying scales to form a CSS image to describe the shape of an object. Smith et al utilize Curvature Scale Space (CSS) techniques to automatically extract the shock profile. From published data, the shock wave automatic detection and tracking algorithm based on multi-sequence schlieren or shadow images has poor robustness to image brightness change and shock wave shape change, and when different schlieren or shadow images are processed, calculation parameters need to be changed frequently, so that the robustness and the automation degree need to be improved urgently.
Currently, there is a need to develop an automatic shock wave detection and tracking algorithm based on image adaptive threshold and feature extraction.
Disclosure of Invention
The invention aims to solve the technical problem of providing an automatic shock wave detection and tracking algorithm based on image self-adaptive threshold and feature extraction.
The invention discloses an automatic shock wave detection and tracking algorithm based on image self-adaptive threshold and feature extraction, which comprises the following steps of:
a. respectively converting the original schlieren or shadow image into an 8-bit gray level schlieren or shadow image;
b. preprocessing an 8-bit gray-scale schlieren or shadow image by utilizing background image deduction, image filtering, image enhancement and sub-pixel interpolation in a frequency domain to obtain a gray-scale schlieren or shadow image;
c. converting the gray level schlieren or shadow image into a binary schlieren or shadow image by using an adaptive threshold algorithm;
d. based on the binary schlieren or shadow image, detecting all characteristic profiles including shock wave characteristic profiles and pseudo shock wave characteristic profiles in the binary schlieren or shadow image by using a profile detection algorithm, coding a serial number for each characteristic profile to obtain a multi-sequence schlieren or shadow image, and storing the pixel vector coordinate position of each characteristic profile;
e. calculating characteristic parameter characteristic values of the perimeter, the pixel number, the gravity center position, the area and the aspect ratio of each characteristic contour according to the pixel vector coordinates of each characteristic contour;
f. selecting criterion characteristic parameters for extracting the shock wave characteristic profile according to the unique characteristics of the shock wave characteristic profile in the sequence schlieren or shadow image, and extracting the shock wave characteristic profile from the pseudo shock wave characteristic profile according to the criterion characteristic parameters;
g. and fitting the shock wave characteristic profile by using the pixel vector coordinates of the shock wave characteristic profile according to the shock wave shape characteristics, thereby further reducing the error of shock wave detection.
Further, the criterion characteristic parameters in step f include aspect ratio and length.
Further, in the step g, a 5 th order polynomial least square fitting is adopted for fitting the shock wave characteristic profile.
The shock wave automatic detection tracking algorithm based on the image adaptive threshold and the feature extraction realizes the conversion from the gray level image to the binary image by adopting the adaptive algorithms with different thresholds aiming at the pixel points at different positions, thereby greatly improving the robustness of the shock wave detection on the image brightness change.
The shock wave automatic detection and tracking algorithm based on the image self-adaptive threshold and the feature extraction can improve the robustness, the automation degree and the precision of the shock wave automatic detection and tracking of the multi-sequence schlieren or shadow image.
Drawings
Fig. 1 is an original shadow image showing a flow field of an open shock tube obtained by an automatic shock detection and tracking algorithm based on image adaptive threshold and feature extraction in embodiment 1;
FIG. 2 is a preprocessed shadow image obtained by the automatic shock wave detection and tracking algorithm based on image adaptive threshold and feature extraction in embodiment 1;
FIG. 3 is a binary shadow image obtained by the adaptive threshold algorithm in the automatic shock wave detection and tracking algorithm based on image adaptive threshold and feature extraction in example 1;
FIG. 4 is a binary shadow image contour detection result obtained by the automatic shock wave detection and tracking algorithm based on image adaptive threshold and feature extraction in embodiment 1;
FIG. 5 is a shock wave feature contour extraction result obtained by the automatic shock wave detection and tracking algorithm based on image adaptive threshold and feature extraction in embodiment 1;
fig. 6 is a shock wave feature contour fitting result obtained by the shock wave automatic detection and tracking algorithm based on image adaptive threshold and feature extraction in embodiment 1.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
Example 1
In this embodiment, a high-speed shadow technology is used to display an external flow field of a nozzle of an open shock tube, and a shock profile and a position in a shadow image are extracted by using an automatic shock detection and tracking algorithm based on an image adaptive threshold and feature extraction provided by the present invention, and the specific process is as follows:
a. respectively converting the original schlieren or shadow image into an 8-bit gray level schlieren or shadow image;
this embodiment gives the original shadow image as shown in fig. 1, which is a 12-bit grayscale image with a spatial resolution of 320 × 216; for the convenience of subsequent image processing, whether the original schlieren or shadow image is a color image or a gray image, the original schlieren or shadow image is uniformly converted into an 8-bit single-color gray image, and the spatial resolution is kept unchanged.
b. Preprocessing an 8-bit gray-scale schlieren or shadow image by utilizing background image deduction, image filtering, image enhancement and sub-pixel interpolation in a frequency domain to obtain a gray-scale schlieren or shadow image;
and (3) deducting the background image in a frequency domain from the shadow image, interpolating the shadow image into 640 × 532 image space resolution by using a Lanczos subpixel interpolation algorithm, and then sequentially performing 3 × 3 median filtering and 5 × 5 bilateral filtering to obtain the preprocessed shadow image shown in fig. 2.
c. Converting the gray level schlieren or shadow image into a binary schlieren or shadow image by using an adaptive threshold algorithm;
converting the gray image into a binary shadow image, which is usually realized by adopting a fixed threshold algorithm, namely setting a threshold value for the whole gray image, setting the gray value of any pixel point to be higher than the threshold value, and setting the gray value to be 1; otherwise, if the gray value of the pixel point is lower than the threshold, the gray value is set to 0. The fixed threshold algorithm is less robust to brightness variations caused by non-uniform or unstable light sources.
The embodiment adopts the self-adaptive algorithms with different thresholds for different position pixel points to realize the conversion from the gray level image to the binary shadow image, and can greatly improve the robustness of shock wave detection on the brightness change of the image.
For the present embodiment, the method for calculating the threshold of the target pixel (x, y) is as follows: selecting a d multiplied by d rectangular pixel array by taking a pixel (x, y) as a center, setting the average value of the gray values of the pixels corresponding to the pixel array as the threshold value of a target pixel point (x, y), setting the gray value of the pixel (x, y) to be 1 when the gray value of the pixel (x, y) is higher than the average value; on the contrary, if the gray-level value of the pixel (x, y) is lower than the threshold, the gray-level value is set to 0, and the binary shadow image shown in fig. 3 is obtained.
d. Based on the binary schlieren or shadow image, detecting all characteristic profiles including shock wave characteristic profiles and pseudo shock wave characteristic profiles in the binary schlieren or shadow image by using a profile detection algorithm, coding a serial number for each characteristic profile to obtain a multi-sequence schlieren or shadow image, and storing the pixel vector coordinate position of each characteristic profile;
the present embodiment connects the continuous pixels having the same gray value. Each detected feature profile is numbered and the pixel vector coordinate position is stored, and 133 profiles are detected in the embodiment to obtain the binary shadow image profile detection result shown in fig. 4.
e. Calculating characteristic parameter characteristic values of the perimeter, the pixel number, the gravity center position, the area and the aspect ratio of each characteristic contour according to the pixel vector coordinates of each characteristic contour;
in this embodiment, characteristic parameter characteristic values of the perimeter, the number of pixels, the center of gravity position, the area, and the aspect ratio of 133 contours are calculated and detected.
f. Selecting criterion characteristic parameters for extracting the shock wave characteristic profile according to the unique characteristics of the shock wave characteristic profile in the sequence schlieren or shadow image, and extracting the shock wave characteristic profile from the pseudo shock wave characteristic profile according to the criterion characteristic parameters;
in this embodiment, the length is selected as a criterion feature parameter for feature extraction, only the profile with the longest length is retained, and other profiles are removed to obtain the shock wave feature profile extraction result shown in fig. 5.
g. And fitting the shock wave characteristic profile by using the pixel vector coordinates of the shock wave characteristic profile according to the shock wave shape characteristics, thereby further reducing the error of shock wave detection.
In the adaptive thresholding and contour detection, the shape of the detected shock wave is not smooth due to noise. In order to reduce the shock wave detection error, the present embodiment performs 5 th order polynomial least square fitting on the detected shock wave profile to obtain the shock wave characteristic profile fitting result as shown in fig. 6.
Although the embodiments of the present invention have been disclosed above, it is not limited to the applications listed in the description and the embodiments, but it can be applied to various fields suitable for the present invention. Additional modifications and refinements of the present invention will readily occur to those skilled in the art without departing from the principles of the present invention, and therefore the present invention is not limited to the specific details and illustrations shown and described herein without departing from the general concept defined by the claims and their equivalents.
Claims (3)
1. The automatic shock wave detection and tracking algorithm based on the image self-adaptive threshold and the feature extraction is characterized by comprising the following steps of:
a. respectively converting the original schlieren or shadow image into an 8-bit gray level schlieren or shadow image;
b. preprocessing an 8-bit gray-scale schlieren or shadow image by utilizing background image deduction, image filtering, image enhancement and sub-pixel interpolation in a frequency domain to obtain a gray-scale schlieren or shadow image;
c. converting the gray level schlieren or shadow image into a binary schlieren or shadow image by using an adaptive threshold algorithm; adopting self-adaptive algorithms with different thresholds to realize the conversion from the gray level image to the binary image aiming at the pixel points at different positions; the target pixel (x, y) threshold calculation method comprises the following steps: selecting a d multiplied by d rectangular pixel array by taking a pixel (x, y) as a center, setting the average value of the gray values of the pixels corresponding to the pixel array as the threshold value of a target pixel point (x, y), setting the gray value of the pixel (x, y) to be 1 when the gray value of the pixel (x, y) is higher than the average value; on the contrary, if the gray-level value of the pixel (x, y) is lower than the threshold, the gray-level value is set to 0;
d. based on the binary schlieren or shadow image, detecting all characteristic profiles including shock wave characteristic profiles and pseudo shock wave characteristic profiles in the binary schlieren or shadow image by using a profile detection algorithm, coding a serial number for each characteristic profile to obtain a multi-sequence schlieren or shadow image, and storing the pixel vector coordinate position of each characteristic profile;
e. calculating characteristic parameter characteristic values of the perimeter, the pixel number, the gravity center position, the area and the aspect ratio of each characteristic contour according to the pixel vector coordinates of each characteristic contour;
f. selecting criterion characteristic parameters for extracting the shock wave characteristic profile according to the unique characteristics of the shock wave characteristic profile in the sequence schlieren or shadow image, and extracting the shock wave characteristic profile from the pseudo shock wave characteristic profile according to the criterion characteristic parameters;
g. and fitting the shock wave characteristic profile by using the pixel vector coordinates of the shock wave characteristic profile according to the shock wave shape characteristics, thereby further reducing the error of shock wave detection.
2. The automatic shock wave detection and tracking algorithm based on image adaptive threshold and feature extraction as claimed in claim 1, wherein the criterion feature parameters in step f include aspect ratio and length.
3. The automatic shock wave detection and tracking algorithm based on image adaptive threshold and feature extraction as claimed in claim 1, wherein the step g of fitting the shock wave feature profile adopts 5 th order polynomial least square fitting.
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