Laser night vision device image acquisition device and method

Abstract

The invention relates to a laser night vision device image acquisition device and method, and belongs to the technical field of night vision device data acquisition. Image quality evaluation is carried out on image data information in a target area to obtain an image quality evaluation result of each image; according to the image quality evaluation result of the image, the image collected in one period is analyzed, an analysis result is obtained, a first image processing strategy or a second image processing strategy is generated according to the analysis result, finally, the image is processed according to the first image processing strategy or the second image processing strategy, and the processed image is obtained. And the processed image is used for updating the image in each period. According to the method, the evaluation result of the image quality is obtained by evaluating the quality of the image, and then the image data is acquired according to the evaluation result of the image quality, so that the definition of the laser night-vision device during data acquisition can be improved, and the working reasonability of the laser night-vision device is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of laser night vision devices, and in particular to an image acquisition device and method for laser night vision devices. Background Art

[0002] The laser night vision system is composed of infrared laser illuminator, zoom lens, ultra-low illumination infrared camera, red laser transmitter and other equipment, which are assembled by scientific and advanced means. The composition of laser night vision device: internal industrial-grade embedded control electronic system, which can realize high stability control of camera zoom, focus, video switching, pan / tilt / rotation, and the overall shell is made of super-strong aluminum alloy to reach IP66 protection level, ensuring the long-term operation of the equipment in the harsh environment of the field. The laser night vision system adopts ultra-low illumination infrared camera design, combined with laser infrared lighting system, to achieve all-weather real-time monitoring in color during the day and black and white at night. Adjustable laser spot: using special laser electric lens, the illumination angle and light intensity of the laser spot can be adjusted in real time, realizing night vision without blind spots. Long monitoring distance: up to 1500-5000 meters. Secondly, the laser night vision system can be used in dark environment without any lighting including starlight. The focal length of the laser night vision system is continuously variable from 10-500mm. This system is widely used in places that require long-distance night vision monitoring, such as oil fields, flood control, forest fire prevention, urban environment testing, oil fields, seawalls, public security, railway locomotives, etc. However, nowadays, the data collected by laser night vision devices in a time series is often unclear, which is not conducive to monitoring the predetermined area. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a laser night vision device and method for collecting images.

[0004] To achieve the above object, the technical solution adopted by the present invention is: The first aspect of the present invention provides a laser night vision device image acquisition device, comprising: a data acquisition module, a data analysis module, a strategy generation module and a data update module. The data acquisition module is responsible for setting the image acquisition frequency and controlling the laser night vision device according to the image acquisition frequency, so as to collect image data information in the target area through the laser night vision device; The data analysis module is responsible for performing image quality evaluation on the image data information in the target area to obtain an image quality evaluation result of each image, and analyzing the images collected in a cycle according to the image quality evaluation result to obtain an analysis result; The strategy generation module is responsible for generating a first image processing strategy or a second image processing strategy according to the analysis result; The data updating module is responsible for processing the image according to the first image processing strategy or the second image processing strategy, acquiring the processed image, and updating the image in each cycle with the processed image.

[0005] A second aspect of the present invention provides a method for collecting images of a laser night vision device, comprising the following steps: Setting an image acquisition frequency, and controlling the laser night vision device according to the image acquisition frequency, so as to acquire image data information in the target area through the laser night vision device; By performing image quality evaluation on the image data information in the target area, an image quality evaluation result of each image is obtained, and images collected in a cycle are analyzed according to the image quality evaluation result of the image to obtain an analysis result; generating a first image processing strategy or a second image processing strategy according to the analysis result; The image is processed according to the first image processing strategy or the second image processing strategy, a processed image is acquired, and the processed image is used to update the image in each cycle.

[0006] Furthermore, in the laser night vision device image acquisition method, an image acquisition frequency is set, and the laser night vision device is controlled according to the image acquisition frequency, and the image data information in the target area is collected by the laser night vision device, specifically: Initialize the image acquisition frequency of the current laser night vision device within a cycle, obtain the clarity data information collected by the laser night vision device under each image acquisition frequency, introduce a genetic algorithm, and set a genetic algebra based on the genetic algorithm; Obtaining clarity data at the current image acquisition frequency of the laser night vision device according to the image acquisition frequency of the current laser night vision device and clarity data information collected by the laser night vision device at each image acquisition frequency; Setting definition data requirement information, and determining whether the image acquisition frequency of the current laser night vision device is greater than the definition data requirement information, and if so, outputting the image acquisition frequency of the current laser night vision device; When the image acquisition frequency of the current laser night vision device is greater than the definition data requirement information, inheritance is performed based on the genetic algebra to readjust the image acquisition frequency of the current laser night vision device until it is greater than the definition data requirement information; The laser night vision device is controlled according to the current image acquisition frequency of the laser night vision device, and image data information in the target area is collected by the laser night vision device.

[0007] Furthermore, in the laser night vision device image acquisition method, the image quality evaluation result of each image is obtained by performing image quality evaluation on the image data information in the target area, specifically: Setting a definition evaluation index threshold range, introducing a decision tree model, constructing a root node according to the image data information in the target area, and continuously splitting the root node based on the definition evaluation index threshold range to obtain a plurality of leaf nodes; Calculate the Mahalanobis distance value between the sample data in each leaf node, and when the Mahalanobis distance value between the sample data in each leaf node is not greater than the Mahalanobis distance threshold, output the leaf node, and when the Mahalanobis distance value between the sample data in each leaf node is greater than the Mahalanobis distance threshold, continue to split the root node; The clarity membership corresponding to each leaf node is obtained, and the image quality evaluation membership of each image is obtained according to the clarity membership corresponding to each leaf node, and the image quality evaluation membership of each image is output as an image quality evaluation result.

[0008] Furthermore, in the laser night vision device image acquisition method, the images acquired in one cycle are analyzed according to the image quality evaluation result of the image to obtain the analysis result, specifically: An evaluation index for the number of abnormal clarity is set, and an image quality evaluation result of each image collected by the current laser night vision device within one cycle is obtained according to the image quality evaluation result of the image; The number of images whose image quality evaluation results are lower than a preset image quality evaluation result is obtained according to the image quality evaluation results of each image collected by the current laser night vision device within one cycle; Determining whether the number of images whose image quality evaluation results are lower than a preset image quality evaluation result is greater than the clarity abnormality number evaluation index; When the number of images whose image quality evaluation results are lower than the preset image quality evaluation results is greater than the clarity abnormality number evaluation index, the image data collected in the corresponding period is regarded as abnormal image data; When the number of images whose image quality evaluation results are not lower than the preset image quality evaluation results is greater than the clarity abnormality number evaluation index, the data collected in the corresponding period is used as normal image data, and an analysis result is generated based on the abnormal image data and the normal image data, and the analysis result is output.

[0009] Furthermore, in the laser night vision device image acquisition method, a first image processing strategy or a second image processing strategy is generated according to the analysis result, specifically: When the analysis result is normal image data, a first image processing strategy is generated according to the normal image data, and the first image processing strategy is displayed in a preset manner; When the analysis result is abnormal image data, a second image processing strategy is generated according to the abnormal image data, and the second image processing strategy is displayed in a preset manner.

[0010] Furthermore, in the laser night vision device image acquisition method, the image is processed according to the first image processing strategy or the second image processing strategy to obtain the processed image, and the processed image is used to update the image in each cycle, specifically: When the image processing strategy is the second image processing strategy, the image data information with the highest definition collected in each time stamp within a cycle is obtained, and feature extraction is performed on the image data information with the highest definition collected in each time stamp within the cycle; By extracting features, an image region whose definition data is lower than a preset definition threshold data is obtained, and the image region whose definition data is lower than the preset definition region is cropped to obtain image data information after a single process and a cropped image region; Acquire image data identical to the cropped area whose clarity data is not less than a preset clarity threshold data from the remaining images, and splice the image data identical to the cropped area whose clarity data is not less than the preset clarity threshold data into the image data information processed once for reconstruction; When the clarity data of each image area in the image data information after the one-time processing is not lower than the preset clarity threshold data, the image reconstruction is completed, the processed image is obtained, and the processed image is used to update the image in each cycle.

[0011] Furthermore, the laser night vision device image acquisition method further includes: When the image processing strategy is the first image processing strategy, image data information collected in each time stamp within a cycle is obtained, and definition data of the image data information collected in each time stamp within the cycle is obtained; The clarity data of the image data information collected in each time stamp within the cycle are sorted to obtain the image data corresponding to the highest clarity data, and the image within each cycle is updated according to the image data corresponding to the highest clarity data.

[0012] A third aspect of the present invention provides a computer-readable storage medium, comprising a laser night vision goggles image acquisition method program, and when the laser night vision goggles image acquisition method program is executed by a processor, the steps of any one of the laser night vision goggles image acquisition methods are implemented.

[0013] The present invention solves the defects existing in the background technology and has the following beneficial effects: The present invention sets an image acquisition frequency and controls the laser night vision device according to the image acquisition frequency, collects image data information in the target area through the laser night vision device, and then evaluates the image quality of the image data information in the target area to obtain the image quality evaluation result of each image, analyzes the images collected in one cycle according to the image quality evaluation result, obtains the analysis result, and then generates a first image processing strategy or a second image processing strategy according to the analysis result, and finally processes the image according to the first image processing strategy or the second image processing strategy to obtain the processed image, and updates the image in each cycle with the processed image. The present invention evaluates the quality of the image to obtain the evaluation result of the image quality, and then processes the image data according to the image quality evaluation result, which can improve the clarity of the laser night vision device when performing data acquisition and improve the rationality of the laser night vision device. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0015] Figure 1 The schematic diagram of the structure of the laser night vision device image acquisition device is shown; Figure 2 The overall flow chart of the laser night vision device image acquisition method is shown; Figure 3 A partial flow chart of the laser night vision device image acquisition method is shown.

[0016] In the figure: 10. Data collection module, 20. Data analysis module, 30. Strategy generation module, 40. Data update module. DETAILED DESCRIPTION

[0017] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0019] like Figure 1 As shown, the first aspect of the present invention provides a laser night vision device image acquisition device, comprising: a data acquisition module 10, a data analysis module 20, a strategy generation module 30 and a data update module 40, The data acquisition module 10 is responsible for setting the image acquisition frequency and controlling the laser night vision device according to the image acquisition frequency, and collecting image data information in the target area through the laser night vision device; The data analysis module 20 is responsible for performing image quality evaluation on the image data information in the target area, obtaining the image quality evaluation result of each image, and analyzing the images collected in one cycle according to the image quality evaluation result to obtain the analysis result; The strategy generation module 30 is responsible for generating the first image processing strategy or the second image processing strategy according to the analysis result; The data updating module 40 is responsible for processing the image according to the first image processing strategy or the second image processing strategy, acquiring the processed image, and updating the image in each cycle with the processed image.

[0020] like Figure 2 As shown, the second aspect of the present invention provides a laser night vision device image acquisition method, comprising the following steps: S102: Setting an image acquisition frequency, and controlling the laser night vision device according to the image acquisition frequency, and collecting image data information in the target area through the laser night vision device; S102: performing image quality evaluation on the image data information in the target area to obtain an image quality evaluation result of each image, and analyzing the images collected in one cycle according to the image quality evaluation result to obtain an analysis result; S104: Generate a first image processing strategy or a second image processing strategy according to the analysis result; S106: Process the image according to the first image processing strategy or the second image processing strategy, obtain the processed image, and update the image in each cycle with the processed image.

[0021] It should be noted that the present invention obtains image quality evaluation results by evaluating the quality of images, and then performs image data according to the image quality evaluation results, which can improve the clarity of the laser night vision device during data acquisition and improve the rationality of the operation of the laser night vision device.

[0022] Furthermore, in the laser night vision device image acquisition method, an image acquisition frequency is set, and the laser night vision device is controlled according to the image acquisition frequency, and image data information in the target area is collected by the laser night vision device, specifically: Initialize the image acquisition frequency of the current laser night vision device within a cycle, obtain the clarity data information collected by the laser night vision device under each image acquisition frequency, introduce a genetic algorithm, and set the genetic algebra based on the genetic algorithm; Obtaining clarity data at the current image acquisition frequency of the laser night vision device according to the current image acquisition frequency of the laser night vision device and clarity data information collected by the laser night vision device at each image acquisition frequency; Set the clarity data requirement information, and determine whether the image acquisition frequency of the current laser night vision device is greater than the clarity data requirement information. If so, output the image acquisition frequency of the current laser night vision device; When the image acquisition frequency of the current laser night vision device is greater than the definition data requirement information, inheritance is performed based on the genetic algebra to readjust the image acquisition frequency of the current laser night vision device until it is greater than the definition data requirement information; The laser night vision device is controlled according to the current image acquisition frequency of the laser night vision device, and image data information in the target area is collected through the laser night vision device.

[0023] It should be noted that, in fact, when collecting data, it is necessary to collect data multiple times in one cycle, such as collecting data 20 times in 1 second, so as to merge the data and form an image. As for a video frame, in fact, the video frame is also formed by a time series of image data to form a dynamic demonstration video frame. At each moment, it is actually just one image.

[0024] It should be noted that the clarity data of an image at least includes the spatial resolution, color depth, contrast, brightness and other data of the image. Generally speaking, the higher the image sampling frequency, the greater the amount of data obtained by sampling, and the higher the spatial resolution of the image. According to the sampling theorem, the sampling frequency must be greater than or equal to twice the highest frequency component in the source image in order to restore the image. The two important parameters that affect image quality are image resolution and color depth. The higher the image resolution, the deeper the color depth. It can be seen that the image sampling frequency will affect the clarity data of the image. This method can dynamically adjust the frequency of data acquisition according to the clarity requirements, thereby improving the rationality of the operation of the laser night vision device.

[0025] Therefore, this method also includes: Obtain historical clarity change feature data of the laser night vision device when collecting data through big data, and construct a clarity feature data prediction model, and input the historical clarity change feature data of the laser night vision device when collecting data into the clarity feature data prediction model for training; By training, a prediction accuracy threshold is set, prediction accuracy data of the clarity feature data prediction model is obtained, and it is determined whether the prediction accuracy data of the clarity feature data prediction model is greater than the prediction accuracy threshold; When the prediction accuracy data of the clarity feature data prediction model is greater than the prediction accuracy threshold, the training is completed, the model parameters of the clarity feature data prediction model are saved, and the clarity feature data prediction model is output; Obtaining clarity change characteristic data of the laser night vision device within a preset time, and inputting the clarity change characteristic data of the laser night vision device within the preset time into the clarity characteristic data prediction model for prediction, and obtaining the clarity data of the laser night vision device under each acquisition frequency in the current timestamp; The clarity data of the laser night vision device under the current image acquisition frequency is updated according to the clarity data of the laser night vision device under each acquisition frequency in the current timestamp, and the image acquisition frequency of the laser night vision device is readjusted.

[0026] It should be noted that since the performance of laser night vision goggles will degrade after a certain number of years of use, this method can update the clarity data of the laser night vision goggles at the current image acquisition frequency according to the clarity data of the laser night vision goggles at each acquisition frequency in the current timestamp, thereby improving the control accuracy of the laser night vision goggles.

[0027] Furthermore, in the laser night vision device image acquisition method, the image quality evaluation result of each image is obtained by performing image quality evaluation on the image data information in the target area, specifically: Set the clarity evaluation index threshold range, introduce the decision tree model, build the root node according to the image data information in the target area, and continuously split the root node based on the clarity evaluation index threshold range to obtain several leaf nodes; Calculate the Mahalanobis distance value between the sample data in each leaf node. When the Mahalanobis distance value between the sample data in each leaf node is not greater than the Mahalanobis distance threshold, output the leaf node. When the Mahalanobis distance value between the sample data in each leaf node is greater than the Mahalanobis distance threshold, continue to split the root node. The definition membership corresponding to each leaf node is obtained, and the image quality evaluation membership of each image is obtained according to the definition membership corresponding to each leaf node, and the image quality evaluation membership of each image is output as the image quality evaluation result.

[0028] It should be noted that this method can quickly evaluate the image data at each moment, thereby quickly obtaining the image quality evaluation result; Furthermore, in the laser night vision device image acquisition method, the images acquired in one cycle are analyzed according to the image quality evaluation results to obtain the analysis results, specifically: Set an evaluation index for the number of abnormal clarity times, and obtain the image quality evaluation results of each image collected by the current laser night vision device within one cycle according to the image quality evaluation results of the images; The number of images whose image quality evaluation results are lower than a preset image quality evaluation result is obtained according to the image quality evaluation results of each image collected by the current laser night vision device within one cycle; Determine whether the number of images whose image quality evaluation results are lower than a preset image quality evaluation result is greater than an evaluation index of the number of abnormal clarity; When the number of images with image quality evaluation results lower than the preset image quality evaluation results is greater than the clarity abnormality number evaluation index, the image data collected in the corresponding period is regarded as abnormal image data; When the number of images whose image quality evaluation results are not lower than the preset image quality evaluation results is greater than the clarity abnormality number evaluation index, the data collected in the corresponding period is used as normal image data, and an analysis result is generated based on the abnormal image data and the normal image data, and the analysis result is output.

[0029] It should be noted that, in a time series only, if the image quality of the collected image data is lower than the evaluation index, it means that the images collected in this time series are all abnormal. This method can be used to analyze the data within a period, so as to formulate relevant image correction strategies.

[0030] Furthermore, in the laser night vision device image acquisition method, a first image processing strategy or a second image processing strategy is generated according to the analysis result, specifically: When the analysis result is normal image data, a first image processing strategy is generated according to the normal image data, and the first image processing strategy is displayed in a preset manner; When the analysis result is abnormal image data, a second image processing strategy is generated according to the abnormal image data, and the second image processing strategy is displayed in a preset manner; like Figure 3 As shown, further, in the laser night vision device image acquisition method, the image is processed according to the first image processing strategy or the second image processing strategy, the processed image is obtained, and the processed image is updated in each cycle, specifically: S202: When the image processing strategy is the second image processing strategy, the image data information with the highest definition collected in each time stamp within a cycle is obtained, and feature extraction is performed on the image data information with the highest definition collected in each time stamp within a cycle; S302: by extracting features, obtaining an image region whose definition data is lower than a preset definition threshold data, and cropping the image region whose definition data is lower than the preset definition region, to obtain image data information after a single process and a cropped image region; S304: acquiring image data identical to the cropped area whose clarity data is not less than a preset clarity threshold data from the remaining images, and splicing the image data identical to the cropped area whose clarity data is not less than the preset clarity threshold data into the once processed image data information for reconstruction; S306: When the clarity data of each image area in the image data information after one processing is not lower than the preset clarity threshold data, the image reconstruction is completed, the processed image is obtained, and the processed image is used to update the image in each cycle.

[0031] It should be noted that the collected images can be further processed by this method, which can improve the monitoring effect of the laser night vision device at night and make the collected image data clearer. Among them, since the remaining images may be clear, the image data of the same cropped area with clarity data not less than the preset clarity threshold data obtained from the remaining images is the image area described by the same pattern, the same image, etc. as the cropped area.

[0032] Furthermore, the laser night vision device image acquisition method further includes: When the image processing strategy is the first image processing strategy, image data information collected in each time stamp within a cycle is obtained, and definition data of the image data information collected in each time stamp within a cycle is obtained; By sorting the clarity data of the image data information collected in each time stamp within a cycle, the image data corresponding to the highest clarity data is obtained, and the image within each cycle is updated according to the image data corresponding to the highest clarity data.

[0033] It should be noted that this method can process data within a cycle, select the most appropriate image to represent the data of the current timestamp, and improve the night vision effect of the laser night vision device.

[0034] In addition, the method may further include: Acquire the layout position information of each laser night vision device in the target area and the working range information of each laser night vision device, and determine the actual working range information of each laser night vision device according to the layout position information of each laser night vision device in the target area and the working range information of each laser night vision device; Obtaining clarity change characteristic data of each laser night vision device within a preset time, inputting the clarity change characteristic data of each laser night vision device within the preset time into the clarity characteristic data prediction model for prediction, and obtaining the clarity data of each laser night vision device under each acquisition frequency; Set the clarity tracking data threshold and configure the maximum acquisition frequency to obtain the clarity data of the laser night vision device under the maximum data acquisition frequency. The laser night vision device whose clarity data under the maximum data acquisition frequency is greater than the clarity tracking data threshold is preliminarily selected as the working laser night vision device. According to the actual working range information of each laser night vision device, the actual working range information of the working laser night vision device is obtained, and the working range requirement information of the laser night vision device is obtained, and a number of laser night vision devices are randomly selected to work, and the total actual working range information is counted; When the total actual working range information is greater than the working range requirement information of the laser night vision device, the working laser night vision device is output, and the working laser night vision device is controlled to collect data; When the total actual working range information is not greater than the working range requirement information of the laser night vision device, several laser night vision devices are reselected to work until the total actual working range information is greater than the working range requirement information of the laser night vision device.

[0035] It should be noted that this method can further improve the rationality of the operation of the laser night vision device and complete the monitoring work according to the predetermined task requirements.

[0036] A third aspect of the present invention provides a computer-readable storage medium, including a laser night vision goggles image acquisition method program, which, when executed by a processor, implements any one of the steps of the laser night vision goggles image acquisition method.

[0037] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0038] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0039] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0040] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0041] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0042] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A laser night vision device image acquisition device, characterized in that: include: Data collection module, data analysis module, strategy generation module and data update module, The data acquisition module is responsible for setting the image acquisition frequency and controlling the laser night vision device according to the image acquisition frequency, so as to collect image data information in the target area through the laser night vision device; The data analysis module is responsible for performing image quality evaluation on the image data information in the target area to obtain an image quality evaluation result of each image, and analyzing the images collected in a cycle according to the image quality evaluation result to obtain an analysis result; The strategy generation module is responsible for generating a first image processing strategy or a second image processing strategy according to the analysis result; The data updating module is responsible for processing the image according to the first image processing strategy or the second image processing strategy, acquiring the processed image, and updating the image in each cycle with the processed image.

2. A laser night vision device image acquisition method, characterized in that: The following steps are involved: Setting an image acquisition frequency, and controlling the laser night vision device according to the image acquisition frequency, so as to acquire image data information in the target area through the laser night vision device; By performing image quality evaluation on the image data information in the target area, an image quality evaluation result of each image is obtained, and images collected in a cycle are analyzed according to the image quality evaluation result of the image to obtain an analysis result; generating a first image processing strategy or a second image processing strategy according to the analysis result; The image is processed according to the first image processing strategy or the second image processing strategy, the processed image is acquired, and the processed image is used to update the image in each cycle.

3. The method for collecting images of a laser night vision device according to claim 2, characterized in that: The image acquisition frequency is set, and the laser night vision device is controlled according to the image acquisition frequency, and the image data information in the target area is collected by the laser night vision device, specifically: Initialize the image acquisition frequency of the current laser night vision device within a cycle, obtain the clarity data information collected by the laser night vision device under each image acquisition frequency, introduce a genetic algorithm, and set a genetic algebra based on the genetic algorithm; Obtaining clarity data at the current image acquisition frequency of the laser night vision device according to the image acquisition frequency of the current laser night vision device and clarity data information collected by the laser night vision device at each image acquisition frequency; Setting definition data requirement information, and determining whether the image acquisition frequency of the current laser night vision device is greater than the definition data requirement information, and if so, outputting the image acquisition frequency of the current laser night vision device; When the image acquisition frequency of the current laser night vision device is greater than the definition data requirement information, inheritance is performed based on the genetic algebra to readjust the image acquisition frequency of the current laser night vision device until it is greater than the definition data requirement information; The laser night vision device is controlled according to the current image acquisition frequency of the laser night vision device, and image data information in the target area is collected by the laser night vision device.

4. The method for collecting images of a laser night vision device according to claim 2, characterized in that: By performing image quality evaluation on the image data information in the target area, an image quality evaluation result of each image is obtained, specifically: Setting a definition evaluation index threshold range, introducing a decision tree model, constructing a root node according to the image data information in the target area, and continuously splitting the root node based on the definition evaluation index threshold range to obtain a plurality of leaf nodes; Calculate the Mahalanobis distance value between the sample data in each leaf node, and when the Mahalanobis distance value between the sample data in each leaf node is not greater than the Mahalanobis distance threshold, output the leaf node, and when the Mahalanobis distance value between the sample data in each leaf node is greater than the Mahalanobis distance threshold, continue to split the root node; The clarity membership corresponding to each leaf node is obtained, and the image quality evaluation membership of each image is obtained according to the clarity membership corresponding to each leaf node, and the image quality evaluation membership of each image is output as an image quality evaluation result.

5. The method for collecting images of a laser night vision device according to claim 2, characterized in that: The images collected in one cycle are analyzed according to the image quality evaluation result of the image to obtain the analysis result, specifically: An evaluation index for the number of abnormal clarity is set, and an image quality evaluation result of each image collected by the current laser night vision device within one cycle is obtained according to the image quality evaluation result of the image; The number of images whose image quality evaluation results are lower than a preset image quality evaluation result is obtained according to the image quality evaluation results of each image collected by the current laser night vision device within one cycle; Determining whether the number of images whose image quality evaluation results are lower than a preset image quality evaluation result is greater than the clarity abnormality number evaluation index; When the number of images whose image quality evaluation results are lower than the preset image quality evaluation results is greater than the clarity abnormality number evaluation index, the image data collected in the corresponding period is regarded as abnormal image data; When the number of images whose image quality evaluation results are not lower than the preset image quality evaluation results is greater than the clarity abnormality number evaluation index, the data collected in the corresponding period is used as normal image data, and an analysis result is generated based on the abnormal image data and the normal image data, and the analysis result is output.

6. The method for collecting images of a laser night vision device according to claim 2, characterized in that: Generate a first image processing strategy or a second image processing strategy according to the analysis result, specifically: When the analysis result is normal image data, a first image processing strategy is generated according to the normal image data, and the first image processing strategy is displayed in a preset manner; When the analysis result is abnormal image data, a second image processing strategy is generated according to the abnormal image data, and the second image processing strategy is displayed in a preset manner.

7. The method for collecting images of a laser night vision device according to claim 2, characterized in that: Processing the image according to the first image processing strategy or the second image processing strategy, acquiring the processed image, and updating the image in each cycle with the processed image, specifically: When the image processing strategy is the second image processing strategy, the image data information with the highest definition collected in each time stamp within a cycle is obtained, and feature extraction is performed on the image data information with the highest definition collected in each time stamp within the cycle; By extracting features, an image region whose definition data is lower than a preset definition threshold data is obtained, and the image region whose definition data is lower than the preset definition region is cropped to obtain image data information after a single process and a cropped image region; Acquire image data identical to the cropped area whose clarity data is not less than a preset clarity threshold data from the remaining images, and splice the image data identical to the cropped area whose clarity data is not less than the preset clarity threshold data into the image data information processed once for reconstruction; When the clarity data of each image area in the image data information after the one-time processing is not lower than the preset clarity threshold data, the image reconstruction is completed, the processed image is obtained, and the processed image is used to update the image in each cycle.

8. The method for collecting images of a laser night vision device according to claim 7, characterized in that: Also includes: When the image processing strategy is the first image processing strategy, image data information collected in each time stamp within a cycle is obtained, and definition data of the image data information collected in each time stamp within the cycle is obtained; The clarity data of the image data information collected in each time stamp within the cycle are sorted to obtain the image data corresponding to the highest clarity data, and the image within each cycle is updated according to the image data corresponding to the highest clarity data.

9. A computer-readable storage medium, characterized in that: It includes a laser night vision goggles image acquisition method program, and when the laser night vision goggles image acquisition method program is executed by a processor, the steps of the laser night vision goggles image acquisition method as described in any one of claims 2-8 are implemented.

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