High-speed target detection method based on plane interference photoelectric imaging system

Abstract

The invention discloses a high-speed target detection method based on a plane interference photoelectric imaging system. The method comprises the following steps: step 1, designing parameters of the plane interference photoelectric imaging system; 2, calculating frequency spectrum acquisition data of the plane interference photoelectric imaging system and analyzing the imaging performance of the plane interference photoelectric imaging system; step 3, constructing a detection data model of a sampling frequency spectrum of the plane interference photoelectric imaging system; 4, designing a frequency-space domain target detection algorithm; and step 5, comparing and analyzing the target detection speed of the frequency-space domain so as to optimize system parameters. The sparse discrete spectrum of the observation scene collected by the plane interference photoelectric imaging system is adopted to construct the data model for high-speed target detection, and the method has the advantages that target detection can be directly carried out in the frequency domain, the data size of the detection model is small, and the target detection speed is high.

Description

Technical Field

[0001] The present invention belongs to the technical field of interference imaging and target detection, and relates to a high-speed target detection method, and specifically to a high-speed frequency domain target detection method based on interference imaging and target detection technology. Background Art

[0002] Using high-resolution remote sensing images for target detection is a research hotspot in the field of remote sensing and plays a very important role in the military field. With the improvement of the resolution of optoelectronic imaging systems, richer and more detailed optical information can be detected. However, the amount of image data will increase significantly with the increase in resolution, resulting in a significant decrease in information processing speed. Therefore, while maintaining the resolution of optoelectronic imaging systems, significantly improving the target detection speed has become a focus issue that needs to be solved urgently.

[0003] In order to significantly reduce the length, volume and weight of the optical imaging system, the United States proposed a planar interferometric photoelectric imaging system in 2012. The system uses a microlens array to collect light, implements interference pairing, phase adjustment, waveguide coupling, and coherent detection in photonic integrated circuits, and obtains spatial images through signal processing, which can reduce the length, volume and weight of the optical system by 10 to 100 times.

[0004] The planar interferometric photoelectric imaging system samples the observation scene in the frequency domain, and the amount of data obtained is much smaller than the amount of data obtained by spatial imaging at the same resolution. Taking advantage of the low amount of frequency domain data collected by the planar interferometric photoelectric imaging system, exploring high-speed frequency domain target detection methods has important theoretical significance and practical value. Summary of the invention

[0005] The present invention provides a high-speed target detection method based on a planar interference optoelectronic imaging system. The system uses a sparse discrete frequency spectrum of an observation scene collected by the planar interference optoelectronic imaging system to construct a data model for high-speed target detection.

[0006] The objective of the present invention is achieved through the following technical solutions:

[0007] A high-speed target detection method based on a planar interferometric photoelectric imaging system comprises the following steps:

[0008] Step 1, designing the parameters of the planar interferometric optoelectronic imaging system, wherein the parameters include the arrangement of the microlens array, the working band, the object distance, the microlens aperture, the system aperture, the longest baseline size, the number of microlenses on each interferometer arm, the number of interferometer arms, and the number of spectral channels;

[0009] Step 2: Calculate the spectrum acquisition data of the planar interferometric optoelectronic imaging system and analyze its imaging performance. The specific steps are as follows:

[0010] Step 21: Calculate the coordinates of each microlens detection unit:

[0011] Using the radial arrangement of microlenses, based on the geometric relationship, the radius of the circle enclosed by the innermost layer of the microlens array is calculated as:

[0012]

[0013] Where d is the microlens aperture, α is the angle between two adjacent PICs, and the center position coordinates of each microlens are:

[0014]

[0015] Where i represents the number of microlenses, j represents the number of interference arms, and x lens ,y lens Respectively represent the horizontal and vertical coordinates of the microlens;

[0016] Step 22: Calculate the detection spectrum of each wavelength channel obtained by pairing microlenses two by two according to the baseline pairing rule:

[0017] According to the baseline pairing rule, the mth microlens and the nth microlens on the jth PIC are paired, and the calculation method of the spatial frequency point (u, v) detected by them is:

[0018]

[0019] Where, λ is the operating wavelength, Z is the object distance;

[0020] Step 23: Superimpose the spectrum of each channel;

[0021] Step 24: Perform inverse Fourier transform on the superimposed spectrum to obtain an imaging result;

[0022] Step 3: Construct a detection data model of the sampling spectrum of the planar interferometric optoelectronic imaging system: There are a large number of zero elements in the sampling spectrum modulus matrix. In order to eliminate the zero elements of non-sampling points, the spectrum modulus of each sampling vector is arranged row by row to form a small spectrum modulus matrix. The speed of target detection using the small spectrum modulus matrix is ​​higher than that of target detection using the spatial domain image.

[0023] Step 4: Design a frequency-space domain target detection algorithm based on the combination of directional gradient histogram and support vector machine. The specific steps are as follows:

[0024] Step 41: Given an observation scene data set, according to step 2, a scene imaging data set A of a planar interferometric optoelectronic imaging system is obtained;

[0025] Step 42: Take the modulus of each sampling vector in the spectrum data of data set A and arrange them row by row to form a small matrix data set B;

[0026] Step 43: Extract directional gradient histogram features from data set A and data set B respectively to obtain data set A1 and data set B1;

[0027] Step 44: Use support vector machine to perform classification training on data set A1 and data set B1 respectively, and count the detection time;

[0028] Step 5. Compare and analyze the target detection speed in the frequency and space domain designed in step 4, and then optimize the system parameters: modify the design parameters of the planar interferometric optoelectronic imaging system, that is, re-execute steps 1 to 4 for each given observation scene data set, optimize the system parameters while ensuring that the target detection accuracy is higher than 90%, and further improve the detection speed until the maximum speed is reached.

[0029] If other detection algorithms are used, such as the famous YOLO (You Only Look Once) algorithm, step 4 is replaced by:

[0030] Design a frequency-space domain target detection algorithm based on YOLO. The specific steps are as follows:

[0031] Step 41: Given an observation scene data set, according to step 2, a scene imaging data set A of a planar interferometric optoelectronic imaging system is obtained;

[0032] Step 42: Take the modulus of each sampling vector in the spectrum data of data set A and arrange them row by row to form a small matrix data set B;

[0033] Step 43: Feature labeling of data set A and data set B respectively;

[0034] Step 44: Use the YOLO algorithm to perform classification training and detection on data set A and data set B respectively, and count the detection time.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] The present invention uses the sparse discrete frequency spectrum of the observation scene collected by the planar interference optoelectronic imaging system to construct a data model for high-speed target detection, which has the advantages of being able to directly detect targets in the frequency domain, having a small amount of detection model data, and having a fast target detection speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The flowchart of the design and imaging simulation program for the planar interferometric optoelectronic imaging system;

[0038] Figure 2 is the light distribution map of the observed scene instance;

[0039] Figure 3It is the sampling spectrum diagram of the planar interferometric optoelectronic imaging system;

[0040] Figure 4 is a three-dimensional graph of the sampled spectrum modulus matrix;

[0041] Figure 5 It is an imaging image formed by a planar interferometric optoelectronic imaging system on the observed scene;

[0042] Figure 6 Image after the sampled spectrum data model is constructed;

[0043] Figure 7 The figure is a flowchart of the specific steps of a high-speed target detection method based on a planar interferometric optoelectronic imaging system. DETAILED DESCRIPTION

[0044] The technical solution of the present invention is further described below in conjunction with the accompanying drawings, but is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention should be included in the protection scope of the present invention.

[0045] The present invention provides a high-speed target detection method based on a planar interferometric optoelectronic imaging system. Figure 7 As shown, the method comprises the following steps:

[0046] Step 1: Design the parameters of the planar interferometric optoelectronic imaging system.

[0047] In 2013, the United States proposed the concept of planar interferometric photoelectric imaging system. In recent years, the research results have elaborated on the imaging principle and performance of planar interferometric photoelectric imaging system (for example: Qiuhui Chu et al, Numerical simulation and optimal design of Segmented Planar Imaging Detector for Electro-Optical Reconnaissance, Optics Communications, 2017, 405: 288–296. http: / / dx.doi.org / 10.1016 / j.optcom.2017.08.021). Every two microlenses on each interference arm of the planar interferometric photoelectric system are paired to form an interference baseline. The light radiation of the observed scene is collected by the microlens array and then forms an ultra-narrowband spectrum through the array band grating. The ultra-narrowband spectrum obtained by a pair of microlenses enters the coupling module to form interference fringes, which are converted into electrical signals by the photodetector in the coupling module. Finally, the electrical signals are combined into spectrum data, and the image of the observed scene can be obtained by inverse Fourier transform.

[0048] Here, the parameter design results and their significance are given in combination with examples. The design parameter names, symbols and parameter values ​​of the planar interferometric optoelectronic imaging system are shown in Table 1. The physical meanings of each parameter have been discussed in a large number of literatures (for example: Qiuhui Chu et al, Numerical simulation and optimal design of Segmented Planar Imaging Detector for Electro-Optical Reconnaissance, Optics Communications, 2017, 405: 288–296, http: / / dx.doi.org / 10.1016 / j.optcom.2017.08.021). The parameter settings and symbol settings here follow the results of previous literatures. This design example is a remote sensing imaging system, so the object distance Z is large, and l and p also increase to a certain extent.

[0049] Table 1

[0050]

[0051]

[0052] Step 2: Calculate the spectrum acquisition data of the planar interferometric optoelectronic imaging system and analyze its imaging performance.

[0053] The flowchart of the imaging design simulation program of the planar interferometric optoelectronic imaging system is as follows: Figure 1 As shown, the simulation steps include the input of system parameters (microlens array arrangement, working band, object distance, microlens aperture, system aperture, longest baseline size, number of microlenses on each interference arm, number of interference arms, number of spectral channels), calculation of the coordinates of each microlens detection unit, calculation of the detection spectrum of each wavelength channel obtained by pairing microlenses according to the baseline pairing rule, superposition of the spectrum of each channel, and finally Fourier inverse transformation of the superimposed spectrum to obtain the imaging result. Using the radial arrangement of microlenses, according to the geometric relationship, the radius of the circle enclosed by the innermost layer of the microlens array can be calculated as:

[0054]

[0055] Where d is the microlens aperture, and α is the angle between two adjacent PICs. The center position coordinates of each microlens are:

[0056]

[0057] Where i represents the number of microlenses, j represents the number of interference arms, and x lens ,y lensRepresent the horizontal and vertical coordinates of the microlens respectively. According to the baseline pairing rule, the mth microlens and the nth microlens on the jth PIC are paired, and the calculation method of the spatial frequency point (u, v) detected by them is:

[0058]

[0059] Where λ is the operating wavelength and Z is the object distance.

[0060] An example of the light distribution of the observed scene is Figure 2 As shown, the microlens array adopts the uniform pairing rule. According to formula (3), the value of the spatial frequency point (u, v) can be calculated, as shown in Figure 3 A sampling spectrum diagram of the planar interferometric optoelectronic imaging system is shown, where different colors correspond to different spectral channels. Figure 3 The corresponding matrix dimension is 300×300. Figure 3 The corresponding matrix modulus can be obtained Figure 4 The dimension shown is 300×300 for the sampled spectrum matrix. Figure 3 The inverse Fourier transform of the spectrum shown can be obtained Figure 5 The reconstructed image shown has pixel data of a 300×300 matrix. Figure 5 That is Figure 2 The image of the observed scene formed by the plane interferometric photoelectric imaging system can be seen from the sparse sampling of the plane interferometric photoelectric imaging system. Figure 5 Blurring and degradation of the image shown.

[0061] Step 3: Construct a detection data model of the sampling spectrum of the planar interference optoelectronic imaging system.

[0062] Figure 4 There are a lot of zero elements in the sampled spectrum modulus matrix shown in the figure. To eliminate the zero elements of non-sampling points, the spectrum modulus of each sampling vector is arranged row by row to form a small matrix of 129×38. Figure 6 shown. Figure 6 The amount of data only accounts for Figure 5 5.45% of the data volume, so Figure 6 The spectral model shown in the figure can detect targets faster than the Figure 5 The speed of object detection in the spatial domain image shown is high.

[0063] Step 4: Design a frequency-spatial domain target detection algorithm.

[0064] There are many types of target detection algorithms. This paper only uses the detection algorithm based on the combination of directional gradient histogram and support vector machine as an example to illustrate. Figure 2Similar satellite images are used as observation scene datasets, and the number of datasets is no less than 300. Figure 1 The process shown obtains the scene imaging data set A of the planar interference optoelectronic imaging system. Then, each sampling vector in the spectrum data of data set A is modulo and arranged row by row to form a 129×38 small matrix data set B. Directed gradient histogram feature extraction is performed on data set A and data set B respectively, and data set A1 and data set B1 can be obtained respectively. Then, support vector machine is used to perform classification training on data set A1 and data set B1 respectively, and the detection time is counted. The present invention only describes the target detection algorithm based on the combination of directional gradient histogram and support vector machine as an example, and other detection algorithms can also be used, but the target detection algorithm used must be able to simultaneously perform target detection on the space-frequency domain model.

[0065] Step 5: Compare and analyze the detection speed in the frequency and space domains, and then optimize the system parameters.

[0066] Repeat step 4, build different types of scene sample data sets, conduct a lot of simulation analysis and algorithm research, and the current conclusion is: Under the condition of ensuring that the target detection accuracy is greater than 92%, the frequency domain target detection speed is 5 to 8 times the spatial domain target detection speed. However, this result is not the optimal solution. The improvement in frequency domain detection speed is related to the characteristics of the observation scene. In order to further improve the frequency domain detection speed and obtain the highest frequency domain target detection speed, it is necessary to modify the design parameters of the planar interferometric optoelectronic imaging system, that is, re-execute steps 1 to 4 for each given observation scene data set, and optimize the system parameters to further improve the detection speed while ensuring that the target detection accuracy is higher than 90%, until the speed reaches the highest.

[0067] A desktop computer with a CPU main frequency of 3.6GHz (4 cores, 8 threads) and 8G memory is used for spatial and frequency domain target detection. The time consumption results when the detection accuracy is higher than 96% are: the spatial domain detection time is 87.1271 milliseconds, and the frequency domain detection time is 12.5463 milliseconds. The frequency domain detection speed is increased to 6.94 times the spatial domain detection speed.

Claims

1. A high-speed target detection method based on a planar interferometric optoelectronic imaging system, characterized in that The method comprises the following steps: Step 1, designing the parameters of the planar interferometric optoelectronic imaging system, wherein the parameters include the arrangement of the microlens array, the working band, the object distance, the microlens aperture, the system aperture, the longest baseline size, the number of microlenses on each interferometer arm, the number of interferometer arms, and the number of spectral channels; Step 2, calculating the spectrum acquisition data of the planar interferometric optoelectronic imaging system and analyzing its imaging performance; Step 3: construct a detection data model of the sampling spectrum of the planar interference optoelectronic imaging system; Step 4: Design a frequency-space domain target detection algorithm based on the combination of directional gradient histogram and support vector machine; Step 5: Compare and analyze the target detection speed in the frequency and space domain designed in step 4, and then optimize the system parameters.

2. The high-speed target detection method based on the planar interference optoelectronic imaging system according to claim 1 is characterized in that The specific steps of step 2 are as follows: Step 21: Calculate the coordinates of each microlens detection unit: Using the radial arrangement of microlenses, based on the geometric relationship, the radius of the circle enclosed by the innermost layer of the microlens array is calculated as: Where d is the microlens aperture, α is the angle between two adjacent PICs, and the center position coordinates of each microlens are: Where i represents the number of microlenses, j represents the number of interference arms, and x lens ,y lens Respectively represent the horizontal and vertical coordinates of the microlens; Step 22: Calculate the detection spectrum of each wavelength channel obtained by pairing microlenses two by two according to the baseline pairing rule: According to the baseline pairing rule, the mth microlens and the nth microlens on the jth PIC are paired, and the calculation method of the spatial frequency point (u, v) detected by them is: Where, λ is the operating wavelength, Z is the object distance; Step 23: Superimpose the spectrum of each channel; Step 24: Perform inverse Fourier transform on the superimposed spectrum to obtain the imaging result.

3. The high-speed target detection method based on the planar interference optoelectronic imaging system according to claim 1 is characterized in that The specific steps of step three are as follows: there are a large number of zero elements in the sampled spectrum modulus matrix. In order to eliminate the zero elements of non-sampling points, the spectrum modulus of each sampling vector is arranged row by row to form a small spectrum modulus matrix. The speed of target detection using the small spectrum modulus matrix will be higher than the speed of target detection using the spatial domain image.

4. The high-speed target detection method based on a planar interferometric optoelectronic imaging system according to claim 1 is characterized in that The specific steps of step 4 are as follows: Step 41: Given an observation scene data set, according to step 2, a scene imaging data set A of a planar interferometric optoelectronic imaging system is obtained; Step 42: Take the modulus of each sampling vector in the spectrum data of data set A and arrange them row by row to form a small matrix data set B; Step 43: Extract directional gradient histogram features from data set A and data set B respectively to obtain data set A1 and data set B1; Step 4: Use support vector machine to perform classification training on data set A1 and data set B1 respectively, and count the detection time.

5. The high-speed target detection method based on a planar interferometric optoelectronic imaging system according to claim 1 is characterized in that The step 4 is replaced by: designing a frequency-space domain target detection algorithm based on YOLO, the specific steps are as follows: Step 41: Given an observation scene data set, according to step 2, a scene imaging data set A of a planar interferometric optoelectronic imaging system is obtained; Step 42: Take the modulus of each sampling vector in the spectrum data of data set A and arrange them row by row to form a small matrix data set B; Step 43: Feature labeling of data set A and data set B respectively; Step 44: Use the YOLO algorithm to perform classification training and detection on data set A and data set B respectively, and count the detection time.

6. The high-speed target detection method based on a planar interferometric optoelectronic imaging system according to claim 1, characterized in that The specific steps of step five are as follows: modify the design parameters of the planar interferometric optoelectronic imaging system, that is, re-execute steps one to four for each given observation scene data set, optimize the system parameters while ensuring that the target detection accuracy is higher than 90%, and further improve the detection speed until the maximum speed is reached.

Images