CN115205369B - Anti-atmospheric turbulence lamp target image displacement extraction algorithm - Google Patents
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Abstract
The invention relates to the technical field of optical image measurement, in particular to an atmospheric turbulence resistant lamp target image displacement extraction algorithm, which comprises the following steps: s1, determining a rough calculation window of a lamp target object in an initial reference diagram, and calculating energy of the area; s2, extracting an energy concentration area in a roughing window; s3, detecting centroid and gray level square centroid in an effective window; s4, determining a weighted center positioning result of the lamp target in the reference picture; s5, repeating the steps S1-S4 in the subsequent deformation graph; s6, calculating center displacement; the invention provides self-adaptive calculation window selection based on energy accumulation, and simultaneously, final weighted center positioning is performed based on a least square support vector machine, and pixel displacement is obtained through target center positioning results before and after deformation. The displacement extraction algorithm can improve the robustness of the photogrammetry technology to atmospheric turbulence disturbance and the structural displacement measurement precision.
Description
Technical Field
The invention relates to the technical field of optical image measurement, in particular to an atmospheric turbulence resistant lamp target image displacement extraction algorithm.
Background
Atmospheric disturbances have proven to compromise the accuracy of precision optical measurements such as topography, displacement, and velocity. In the field of precise optical measurement, an image restoration and signal filtering method is usually adopted at present, the optical principle is not involved basically, and the deflection quantitative measurement to be carried out in the research is limited in disturbance eliminating effect. Whereas the correction method by camera system design is not suitable for single-phase measurement applications. The turbulence image processing method widely adopted in the military field is limited to target detection and cannot be directly applied to displacement measurement. Mahrt et al state that atmospheric disturbances can cause blurring and distortion of the target imaging, thereby increasing the target centering error.
At present, the common center positioning algorithm mainly comprises a centroid method, a fitting method, a least square method and the like. The research of the center positioning algorithm considering the atmospheric disturbance is mainly applicable to targets with uniform inherent gray distribution on the surface of a man-made design or structure, is not applicable to the calculation window self-adaptive determination method proposed by light source target positioning algorithm from the image processing angle by Yang and the like, and the real imaging region identification method based on the normal distribution 3 sigma principle in the infrared target radiation intensity measurement field, and effectively inhibits the interference of fuzzy degradation on the calculation region determination. However, the above-mentioned researches are only applicable to light source targets of regular shapes such as circles or rectangles, and the influence of imaging distortion which may occur is ignored. In addition, it is difficult for a single positioning algorithm to ensure center detection accuracy under distortion conditions. For the latter, a weighted positioning method has been developed. Because the method has high requirement on the determination of the weight, and the internal connection mechanism between the distorted target center influenced by the atmospheric disturbance and the characteristics of the distorted target center is complex, wang and the like propose a target center weighted positioning method based on BPNN, and the center detection results obtained based on various algorithms are weighted. However, the study still has two disadvantages: on the one hand, the BPNN is based on an empirical risk minimization criterion, has the problems of over fitting, poor popularization capability of a model, difficult determination of a network topology structure, easy trapping in local optimum and the like. The LS-SVM adopts the least square linear system error square sum as a loss function, and the equality constraint is used for replacing the inequality constraint, so that the solving process is changed into a set of equality equations, the problem of time-consuming quadratic programming of solving is avoided, the training speed is greatly increased, the LS-SVM does not need to be provided with fitting precision in advance, and the method has higher prediction precision; on the other hand, the existing weighted positioning method based on the BPNN still uses a fixed threshold value to determine an imaging area, and influences of imaging area changes caused by atmospheric disturbance on positioning results are ignored.
Disclosure of Invention
The invention aims to provide an atmospheric turbulence resistant lamp target image displacement extraction algorithm, which starts from the influence effect of atmospheric turbulence on lamp target imaging, selects and weights and positions from an effective calculation window respectively, and reduces the influence of imaging blurring and distortion.
The technical scheme adopted by the invention is as follows:
an atmospheric turbulence resistant lamp target image displacement extraction algorithm comprises the following steps:
s1: a rough calculation window mxn of the lamp target is determined in the initial reference map and the energy E of the region is calculated as in formula (1): g (x, y) represents the gray scale at the pixel coordinates (x, y).
S2: the energy concentration area is extracted in the rougher window. The region needs to satisfy both the conditions of equation (2) and equation (3), i.e., when the gray scale g (x, y) is greater than the threshold g T When calculating the energy, the energy is required to be more than eta times of the total energy, and if not, the g is required to be readjusted T Until both conditions are met. Where η represents the CCD camera diffuse spot energy concentration (η=80%). g T A new window decision threshold after the energy concentration is met is indicated.
S3: centroid and gray scale square centroid detection is performed within the active window. The centroid calculation uses equation (4), where the gray scale is a binarized gray scale.
For gray-level square centroid calculation, the gray-level value of the pixel itself needs to be employed. In order to reduce the influence of noise, gray optimization is required to be carried out on pixels in the extracted effective window, and a specific calculation process is shown in a formula (5). The gray scale square centroid calculation uses equation (6).
S4: and determining a weighted center positioning result of the lamp target object in the reference diagram. The resulting centroid (x 1 ,y 1 ) Sum gray scale square centroid (x 2 ,y 2 ) And the true center (x) 0 ,y 0 ) The weighted relation between them is expressed as:
wherein eta is i The weights of the two positioning results are represented. Because in actual measurement, an internal connection mechanism between a target center detection result influenced by atmospheric disturbance and a real center position is complex, the invention provides a method for solving the weight by means of a fitting function of a least squares support vector machine LS-SVM algorithm.
S5: S1-S4 are repeated in subsequent deformation maps. However, when determining the energy concentration region in the subsequent deformation map, it is necessary to adjust the threshold g on the basic assumption that "the concentrated energy of the target imaging remains unchanged T The aim is to reduce turbulence-induced fluctuations in the effective window. Then, the subsequent weighted center positioning is performed in the effective window.
S6: and calculating the center displacement. Assume that the weighted center positioning result in the reference map frame_0 is: (x) w0 ,y w0 ) The weighted center positioning result in the deformation map frame_i is: (x) wi ,y wi ) The displacement component is:
in the step S4, a least square support vector machine (LS-SVM) method is adopted for solving the weight values. It is necessary to pre-capture images of some stable lamp target targets in the field for LS-SVM learning before actual structural displacement measurement. Since there are almost no completely motionless targets in the actual structure, the present invention selects the target near the support or foundation, and uses the minimum variance of the target displacement time course as the target optimization function:
and obtaining the optimal weight value through pre-learning. The turbulence parameter changes are approximately considered to be small in a short time in the area, so that the weight value can be directly used for displacement extraction of a subsequent target.
The beneficial effects are that: compared with the prior art, the method has the following advantages: the monitoring error of the target image displacement of the lamp target caused by the atmospheric turbulence can be greatly reduced, so that the photogrammetry technology has higher precision and robustness against complex environments when being applied to the displacement measurement of the structure.
Drawings
Fig. 1 is a basic process diagram of lamp target image displacement extraction according to the present invention.
FIG. 2 is a schematic diagram showing the center positioning of the LED lamp target image in the displacement measurement of a cable-stayed bridge according to the method of the present invention.
Fig. 3 is a basic flow chart of the LS-SVM of the present invention.
Fig. 4 is a graph of displacement calculation results according to an embodiment of the present invention.
Fig. 5 is a field of an experimental experiment for improving the accuracy of algorithm center positioning according to the present invention: a test device arrangement (a) a camera arrangement; (b) a displacement stage and a target; (c) a camera field of view.
FIG. 6 shows the results of a verification test of the present invention: (a) stationary target positioning results of different algorithms; (b) results of positioning of moving objects by different algorithms.
Detailed Description
The present invention will be further described in detail with reference to the drawings and examples, which are only for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention.
As shown in fig. 1, an atmospheric turbulence resistant lamp target image displacement extraction algorithm comprises the following steps:
s1, determining a rough calculation window of a lamp target object in an initial reference diagram, and calculating energy of the area;
s2, extracting an energy concentration area in a roughing window;
s3, detecting centroid and gray level square centroid in an effective window;
s4, determining a weighted center positioning result of the lamp target in the reference picture;
s5, repeating the steps S1-S4 in the subsequent deformation graph;
s6, calculating the center displacement.
Taking displacement extraction of an LED lamp target image of a cable-stayed bridge as an example, the method comprises the following steps:
s1: in the initial reference map, a rough calculation window m×n of the mount-position lamp target is determined (fig. 2 (a)), and the energy E of the region is calculated.
S2: the energy concentration area is extracted in the rougher window (fig. 2 (c)).
S3: centroid and gray scale square centroid detection is performed within the active window.
S4: using the weighted positioning coordinates to obtain the centroid (x 1 ,y 1 ) Sum gray scale square centroid (x 2 ,y 2 ) And the true center (x) 0 ,y 0 ) The expression of the space is:
wherein eta is i The weights of the two positioning results are represented.
S5: at a later time, the image threshold is adjusted to ensure that the concentrated energy does not become a criterion, an adaptive energy concentration area (i.e., an effective calculation window) is obtained, and S3-S4 are repeated.
S6: and solving the optimal weight. And obtaining the pixel displacement of the stable target point at each moment based on the steps, taking the minimum displacement variance as an optimized objective function, and fitting the optimal weight value by means of a least square support vector machine LS-SVM algorithm, wherein a flow chart is shown in figure 3.
S7: and repeating S1-S5 for the lamp target at the deformed section position, and carrying the lamp target into the lamp target based on the weight value obtained in the S6. An image shift of the lamp target is obtained (fig. 4).
To further illustrate the superiority of the proposed method, experiments were carried out to verify that the experiments were carried out in the boulevard of a campus, the ground was a cement road, and that in the summer of 7 months, the temperature was high, which all caused severe near-ground atmospheric disturbances. The target adopted in the test is a 60W 850nm infrared lamp, and a bandpass filter is additionally arranged in front of the lens, so that natural stray light influence below 850nm can be effectively filtered. The range of the electric displacement table is 500mm, and the precision is 0.1mm.
S1, keeping the target at rest and continuously collecting for 5 minutes at the collection frequency of 2 frames per second, extracting the pixel coordinates of the center of the target by adopting a gray level square centroid method and a centroid method respectively, then adopting an LS-SVM method, taking the displacement fluctuation variance obtained by the weighted coordinates as a target, and fitting the weight value result of the coordinates to be: lambda (lambda) 1 ＝0.4,λ 1 The positioning results of the above three center extraction algorithms are shown in fig. 6 (a) =0.6.
S2, then, the target moves stepwise by 1mm under the control of the displacement table. The vertical displacement of the target is calculated by respectively adopting a gray level square centroid method, a centroid method and a weighting method, and the result is shown in fig. 6 (b). The data of the displacement table is taken as a real reference value, and obviously, the displacement measured value obtained by adopting the weighted positioning algorithm has better accuracy and stability.
The lamp target image deformation measuring method can effectively reduce the lamp target displacement measuring error caused by atmospheric turbulence. Is also beneficial to the popularization and the use of the device in the night measurement of structural displacement of bridges and the like
The foregoing has outlined and described the basic principles, features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (1)
1. An atmospheric turbulence resistant lamp target image displacement extraction algorithm is characterized by comprising the following steps of:
s1, determining a rough calculation window of a lamp target object in an initial reference diagram, and calculating energy of the area;
s2, extracting an energy concentration area in a roughing window;
s3, detecting centroid and gray level square centroid in an energy concentration area;
s4, determining a weighted center positioning result of the lamp target in the reference picture;
s5, repeating the steps S1-S4 in the subsequent deformation graph;
s6, calculating center displacement;
in said step S1, a rough calculation window mxn of the lamp target object is determined in the initial reference map, the energy E of the region is calculated using formula (1), where g (x, y) represents the gray scale at the pixel coordinates (x, y):
in said step S2, an energy concentration area, i.e. an effective calculation window, is extracted in the roughing window, which area needs to satisfy both the conditions of equation (2) and equation (3), i.e. when the gray level g (x, y) is greater than the threshold g T When calculating the energy, the energy is required to be more than eta times of the total energy, and if not, the g is required to be readjusted T Until both conditions are satisfied, where η represents the CCD camera speckle energy concentration, η=80%, g T Representing a new window decision threshold after the energy concentration is met;
in the step S3, centroid and gray level square centroid detection are carried out in an effective window, a formula (4) is adopted for centroid calculation, and gray level is binary gray level;
for gray level square centroid calculation, gray level values of pixels are required to be adopted, gray level optimization is required to be carried out on pixels in an extracted effective window for reducing influence of noise, a specific calculation process is shown as a formula (5), and the gray level square centroid calculation adopts a formula (6):
in the step S4, the solution of the weight value adopts a least square support vector machine method LS-SVM, and before the actual structural displacement measurement, images of some on-site stable lamp target targets are collected in advance, used for learning of the LS-SVM, targets near a support or a foundation are selected, and the variance of the target displacement time interval is the minimum as a target optimization function:
obtaining an optimal weight value through pre-learning;
in said step S4, the weighted center positioning result of the lamp target object in the reference map is determined, and the centroid (x 1 ,y 1 ) Sum gray scale square centroid (x 2 ,y 2 ) And the true center (x) 0 ,y 0 ) The weighted relation between them is expressed as:
wherein eta is i Weights representing two positioning results;
in said step S6, a center shift is calculated assuming that the weighted center positioning result in the reference map frame_0 is: (x) w0 ,y w0 ) The weighted center positioning result in the deformation map frame_i is: (x) wi ,y wi ) The displacement component is:
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4499597A (en) * | 1982-03-29 | 1985-02-12 | Hughes Aircraft Company | Small-object location utilizing centroid accumulation |
| US4727562A (en) * | 1985-09-16 | 1988-02-23 | General Electric Company | Measurement of scatter in x-ray imaging |
| US5022089A (en) * | 1990-01-19 | 1991-06-04 | Wilson Monti R | Method and apparatus for fast registration using crosshair register marks |
| US7860344B1 (en) * | 2005-05-06 | 2010-12-28 | Stochastech Corporation | Tracking apparatus and methods using image processing noise reduction |
| CN107610102A (en) * | 2017-08-24 | 2018-01-19 | 东南大学 | A kind of Displacement measuring method based on Tikhonov regularizations |
| CN109035326A (en) * | 2018-06-19 | 2018-12-18 | 北京理工大学 | High-precision location technique based on sub-pix image recognition |
| CN110864587A (en) * | 2019-11-08 | 2020-03-06 | 中国科学院长春光学精密机械与物理研究所 | Seeker aiming positioning method and aiming positioning system |
| CN111050652A (en) * | 2017-09-12 | 2020-04-21 | 皇家飞利浦有限公司 | Spectral (multi-energy) image data for image-guided applications |
| CA3038176A1 (en) * | 2019-03-27 | 2020-09-27 | 4DM Inc. | Object motion mapping from single-pass electro-optical satellite imaging sensors |
| CN112712542A (en) * | 2020-12-25 | 2021-04-27 | 武汉大学 | Foundation cloud picture motion prediction method combining block matching and optical flow method |
| CN112735163A (en) * | 2020-12-25 | 2021-04-30 | 北京百度网讯科技有限公司 | Method for determining static state of target object, road side equipment and cloud control platform |
| CN113192121A (en) * | 2021-04-16 | 2021-07-30 | 西安理工大学 | Light spot center sliding weighting centroid positioning method under atmospheric turbulence |
| CN114108717A (en) * | 2021-12-09 | 2022-03-01 | 上海勘察设计研究院(集团)有限公司 | Foundation pit enclosure top deformation monitoring system and method based on vision measurement |
| CN114166178A (en) * | 2021-12-09 | 2022-03-11 | 上海勘察设计研究院(集团)有限公司 | Real-time deformation monitoring method and system for frame section of tunnel shield machine under construction |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040208385A1 (en) * | 2003-04-18 | 2004-10-21 | Medispectra, Inc. | Methods and apparatus for visually enhancing images |
| US11587250B2 (en) * | 2021-06-21 | 2023-02-21 | University Of Electronic Science And Technology Of China | Method for quantitatively identifying the defects of large-size composite material based on infrared image sequence |
-
2022
- 2022-08-03 CN CN202210931549.7A patent/CN115205369B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4499597A (en) * | 1982-03-29 | 1985-02-12 | Hughes Aircraft Company | Small-object location utilizing centroid accumulation |
| US4727562A (en) * | 1985-09-16 | 1988-02-23 | General Electric Company | Measurement of scatter in x-ray imaging |
| US5022089A (en) * | 1990-01-19 | 1991-06-04 | Wilson Monti R | Method and apparatus for fast registration using crosshair register marks |
| US7860344B1 (en) * | 2005-05-06 | 2010-12-28 | Stochastech Corporation | Tracking apparatus and methods using image processing noise reduction |
| CN107610102A (en) * | 2017-08-24 | 2018-01-19 | 东南大学 | A kind of Displacement measuring method based on Tikhonov regularizations |
| CN111050652A (en) * | 2017-09-12 | 2020-04-21 | 皇家飞利浦有限公司 | Spectral (multi-energy) image data for image-guided applications |
| CN109035326A (en) * | 2018-06-19 | 2018-12-18 | 北京理工大学 | High-precision location technique based on sub-pix image recognition |
| CA3038176A1 (en) * | 2019-03-27 | 2020-09-27 | 4DM Inc. | Object motion mapping from single-pass electro-optical satellite imaging sensors |
| CN110864587A (en) * | 2019-11-08 | 2020-03-06 | 中国科学院长春光学精密机械与物理研究所 | Seeker aiming positioning method and aiming positioning system |
| CN112712542A (en) * | 2020-12-25 | 2021-04-27 | 武汉大学 | Foundation cloud picture motion prediction method combining block matching and optical flow method |
| CN112735163A (en) * | 2020-12-25 | 2021-04-30 | 北京百度网讯科技有限公司 | Method for determining static state of target object, road side equipment and cloud control platform |
| CN113192121A (en) * | 2021-04-16 | 2021-07-30 | 西安理工大学 | Light spot center sliding weighting centroid positioning method under atmospheric turbulence |
| CN114108717A (en) * | 2021-12-09 | 2022-03-01 | 上海勘察设计研究院(集团)有限公司 | Foundation pit enclosure top deformation monitoring system and method based on vision measurement |
| CN114166178A (en) * | 2021-12-09 | 2022-03-11 | 上海勘察设计研究院(集团)有限公司 | Real-time deformation monitoring method and system for frame section of tunnel shield machine under construction |
Non-Patent Citations (2)
| Title |
|---|
| Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system;Ribeiro D, et al;Engineering Structures;20141231;第164-180页 * |
| 基于相机扰动校正的桥梁结构变形测量方法与应用;于姗姗;东南大学;20220515;第1-139页 * |
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