Electronic confrontation reconnaissance interference training evaluation method based on image recognition

Abstract

The invention discloses an electronic confrontation reconnaissance interference training evaluation method based on image recognition, and belongs to the technical field of image analysis. According to the technical scheme, in the training process, an operation image of an operator on electronic countermeasure equipment is collected in real time, and the operation image is preprocessed; identifying a signal reconnaissance operation, a signal interference operation and an operation track of an operator from the operation image through an image identification model; evaluating the operation agility through the time sequence of the signal reconnaissance operation and the signal interference operation; evaluating operation accuracy through errors of the signal reconnaissance operation and the signal interference operation; the operation rationality is evaluated through the reciprocity of the operation track; and performing comprehensive scoring based on operation agility, operation accuracy and operation reasonability. By adopting the scheme of the invention, the objectivity, scientificity and high efficiency of electronic confrontation reconnaissance interference training evaluation can be effectively improved.

Description

Technical Field

[0001] The present application belongs to the field of electronic countermeasure technology, and more specifically, relates to an electronic countermeasure reconnaissance and jamming training evaluation method based on image recognition. Background Art

[0002] In the training of electronic countermeasure reconnaissance and jamming, the evaluation system plays a vital role. Signal reconnaissance and jamming work, as part of electronic countermeasure training, has many special characteristics. First, there are many types of signals and a wide spectrum in the work, including radar, communication, data link, etc., which requires operators to be able to skillfully identify and respond to various electromagnetic signals. Secondly, the work requires high timeliness. In a complex electromagnetic environment, operators need to make decisions and perform corresponding tasks quickly, and rapid response to signals is crucial. Finally, operators need to have a high degree of professional knowledge and skills to ensure accurate identification and confrontation of signals.

[0003] Traditional evaluation methods mainly use manual evaluation, that is, evaluation experts score and evaluate the operator's training based on the operator's training operations. However, this evaluation method is highly subjective and not scientific and efficient enough. Summary of the invention

[0004] In response to the above defects or improvement needs of the prior art, the present application provides an electronic countermeasure reconnaissance and interference training evaluation method based on image recognition, which aims to solve the technical problem of strong subjectivity of traditional evaluation methods in electronic countermeasure reconnaissance and interference training.

[0005] To achieve the above objectives, in a first aspect, the present application provides an electronic countermeasure reconnaissance jamming training evaluation method based on image recognition, which specifically includes the following steps:

[0006] During the training process, the operation images of the operator on the electronic countermeasure equipment are collected in real time, and the operation images are preprocessed;

[0007] Identifying the operator's signal reconnaissance operation, signal jamming operation and operation trajectory from the operation image through an image recognition model;

[0008] The operation agility is evaluated by the timing of the signal reconnaissance operation and the signal interference operation; the operation accuracy is evaluated by the errors of the signal reconnaissance operation and the signal interference operation; the operation rationality is evaluated by the reciprocity of the operation trajectory; and a comprehensive score is given based on the operation agility, operation accuracy and operation rationality.

[0009] Preferably, the signal reconnaissance operation and signal interference operation of the operator are identified from the operation image by an image recognition model, specifically:

[0010] The image recognition model is used to identify menu selections, button selections and input text in the operation interface from the operation image; based on the menu selections, button selections and input text, the reconnaissance parameter settings, signal searches and signal interception in the signal reconnaissance operation are determined; and the interference parameter settings and interference emissions in the signal interference operation are determined.

[0011] Preferably, the operation track is identified from the operation image by an image recognition model, specifically:

[0012] The image recognition model is used to recognize the operation points in the operation interface from the operation image, and the operation trajectory is obtained from the position time-series change trajectory of the operation point.

[0013] Preferably, the training method of the image recognition model comprises the following steps:

[0014] Collect training videos in different working scenarios, different training items and different weather conditions;

[0015] Annotate menu selections, button selections, text inputs, and operation points in the operation interface in each video frame of the training video;

[0016] The labeled video frames are used as training sets, and the image recognition model is obtained based on convolutional neural network training.

[0017] Preferably, the operation agility is evaluated by the timing of the signal reconnaissance operation and the signal interference operation, specifically:

[0018] The training start time is recorded as t0;

[0019] If signal interception is identified from the first operation image, the acquisition time of the first operation image is recorded as t1;

[0020] If interference emission is identified from the second operation image, the acquisition time of the second operation image is recorded as t2;

[0021] The smaller the t1-t0 value is, the higher the agility of the evaluated signal reconnaissance operation is;

[0022] The smaller the t2-t0 value is, the higher the agility of the evaluated signal interference operation is.

[0023] Preferably, the operation accuracy is evaluated by the operation errors of the signal reconnaissance operation and the signal interference operation, specifically:

[0024] Compare the reconnaissance parameter setting, signal search, and signal interception in the signal reconnaissance operation with the standard operation in the signal reconnaissance operation. The more identical steps there are, the higher the accuracy of the signal reconnaissance operation will be.

[0025] Compare the interference parameter settings and interference transmission in the signal interference operation with the standard operation in the signal interference operation. The more identical steps there are, the higher the accuracy of evaluating the signal interference operation.

[0026] Preferably, the rationality of the operation is evaluated by the reciprocity of the operation trajectory, specifically:

[0027] If there are more reciprocating trajectories per unit time in the operation trajectory, the rationality of the operation is lower.

[0028] Preferably, a comprehensive score is given based on operational agility, operational accuracy and operational rationality, specifically:

[0029] The comprehensive score is equal to the weighted sum of operational agility, operational accuracy, and operational rationality.

[0030] Preferably, the reconnaissance interference training evaluation method further includes:

[0031] If it is identified that the operator's signal reconnaissance operation or signal interference operation steps are different from the standard operation, the steps of the standard operation are displayed on the operation interface.

[0032] In a second aspect, the present application provides an electronic countermeasure reconnaissance and jamming training evaluation system based on image recognition, the system specifically comprising:

[0033] An image acquisition and processing module is used to acquire, in real time during the training process, the operation images of the operator on the electronic countermeasure equipment and to pre-process the operation images;

[0034] An operation recognition module, used to recognize the operator's signal reconnaissance operation, signal jamming operation and operation trajectory from the operation image through an image recognition model;

[0035] An operation evaluation module is used to evaluate the operation agility through the timing of the signal reconnaissance operation and the signal interference operation; evaluate the operation accuracy through the errors of the signal reconnaissance operation and the signal interference operation; evaluate the operation rationality through the reciprocity of the operation trajectory; and perform a comprehensive score based on the operation agility, operation accuracy and operation rationality.

[0036] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art:

[0037] (1) The present application uses an image recognition model to identify various electronic countermeasure reconnaissance and jamming operations of operators from electronic countermeasure training images, and comprehensively evaluates the operations based on the agility, accuracy and rationality of various operations, thereby replacing the traditional method of expert scoring in electronic countermeasure training, and improving the objectivity, scientificity and efficiency of the evaluation of electronic countermeasure training.

[0038] (2) This application uses an image recognition model to identify menu selections, button selections, input text, and operation points from the operation image; through menu selections, button selections, and input text, it can effectively identify the reconnaissance parameter settings, signal searches, signal interception, interference parameter settings, and interference transmissions in the operation; through the time-series change trajectory of the operation point position, it can effectively obtain the operator's operation trajectory. The above recognition rules fit the training actions of the trainees and provide a scientific and effective evaluation basis for subsequent plans.

[0039] (3) This application evaluates the agility of the operation through the temporal relationship of the operation, the accuracy of the operation through the error relationship of the operation, and the rationality of the operation through the reciprocity of the operation. The training effect is evaluated from not only one dimension, ensuring that the final evaluation result is comprehensive and objective. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flowchart of an electronic countermeasure reconnaissance and jamming training evaluation method based on image recognition provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] The terms "first" and "second" in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects. For example, a first operation image and a second operation image are used to distinguish different operation images rather than to describe a specific order of the operation images.

[0043] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0044] In the description of the embodiments of the present application, unless otherwise specified, “plurality” means two or more than two. For example, a plurality of operation images refers to two or more operation images, etc.

[0045] Next, the technical solutions provided in the embodiments of the present application are introduced.

[0046] like Figure 1 As shown, the embodiment of the present application includes the following steps:

[0047] S1. During the training process, the operation images of the operator on the electronic countermeasure equipment are collected in real time, and the operation images are preprocessed;

[0048] The acquisition methods mainly include high-definition cameras, infrared cameras or digital cameras, etc., through which the operator's operation images on the electronic countermeasure equipment are obtained, and the acquired images are preprocessed. The preprocessing includes but is not limited to one or a combination of noise reduction processing, binarization processing, grayscale processing, format conversion processing and cutting processing to ensure high-quality and analyzable image data.

[0049] S2. Identifying the operator's signal reconnaissance operation, signal jamming operation and operation trajectory from the operation image through an image recognition model;

[0050] Among them, the training method of the image recognition model includes the following steps:

[0051] S21. Collect training videos in different working scenarios, different training items and different weather conditions.

[0052] Among them, the training videos need to cover diverse scenarios such as different weather conditions, flight altitudes, signal densities, etc. to ensure the robustness of the image recognition model under various complex conditions.

[0053] S22, marking menu selection, button selection, text input and operation points in the operation interface in each video frame of the training video;

[0054] It is important to ensure that each menu selection, button selection, and text input is labeled so that the features of each labeled object can be learned during subsequent model training.

[0055] S23: Using the annotated video frames as a training set, and obtaining the image recognition model based on convolutional neural network training.

[0056] Among them, convolutional neural networks (CNNs) have achieved remarkable results in the field of computer vision with their excellent image feature extraction capabilities and classification accuracy. This choice is crucial for accurately extracting and classifying various signal sources in images. Through the combination of convolutional layers, pooling layers, and fully connected layers, high-level features in images can be learned, thereby achieving accurate identification of signal sources. For the learning of complex signal patterns, the hierarchical structure of CNN can better capture the spatial hierarchical information in images, making it excellent in image classification work.

[0057] In this embodiment, model compression technology is used to reduce the size of the deep learning model by removing redundant parameters, sparse weights, etc. This not only helps to store and load the model on the embedded device, but also reduces the computational complexity.

[0058] At the same time, the structure of the deep learning network is simplified, including reducing the number of layers, reducing the size of the convolution kernel, etc. In this way, the computational burden of the network can be reduced while maintaining high accuracy, making the algorithm more suitable for embedded systems.

[0059] Utilize the hardware acceleration function of embedded devices, such as GPU or dedicated deep learning chips, to improve the calculation speed of deep learning algorithms. By giving full play to the hardware performance, the real-time requirements can be better met.

[0060] S24. Identify menu selections, button selections, and input text in the operation interface from the operation image through the trained image recognition model; determine reconnaissance parameter settings, signal searches, and signal interception in signal reconnaissance operations based on the menu selections, button selections, and input text; and determine interference parameter settings and interference emissions in signal interference operations.

[0061] The following operations are identified based on the combination of menu selection, button selection, and input text in the operation interface recognized in the operation image:

[0062] During reconnaissance operations:

[0063] (1) Reconnaissance parameter setting: Set parameters such as signal frequency range, threshold level, frequency resolution, signal averaging times, pulse repetition period range, and pulse width range.

[0064] (2) Signal search: Search for targets according to different reconnaissance targets; perform network station sorting, intra-pulse identification, and inter-pulse identification.

[0065] (3) Signal interception: The signal is intercepted correctly and automatically displayed in the parameter list.

[0066] Disrupting operation:

[0067] (1) Interference parameter setting: setting signal frequency, power, spectral line width, interference bandwidth, interference mode, interference style, modulation style, interference direction, interference time, etc.

[0068] (2) Interference transmission: Click the interference button.

[0069] S25 . Identify the operation points in the operation interface from the operation image using the image recognition model, and obtain the operation trajectory from the position time-series change trajectory of the operation point.

[0070] S3. evaluating the operational agility through the timing of the signal reconnaissance operation and the signal jamming operation;

[0071] Specifically:

[0072] The training start time is recorded as t0;

[0073] If signal interception is identified from the first operation image, the acquisition time of the first operation image is recorded as t1;

[0074] If interference emission is identified from the second operation image, the acquisition time of the second operation image is recorded as t2;

[0075] The smaller the t1-t0 value is, the higher the agility of the evaluated signal reconnaissance operation is;

[0076] The smaller the t2-t0 value is, the higher the agility of the evaluated signal interference operation is.

[0077] The weighted sum of signal reconnaissance operation agility and signal jamming operation agility can be taken as the final operation agility.

[0078] evaluating the accuracy of the operation through errors of the signal reconnaissance operation and the signal jamming operation;

[0079] Specifically:

[0080] Compare the reconnaissance parameter setting, signal search, and signal interception in the signal reconnaissance operation with the standard operation of the signal reconnaissance operation. The more identical steps there are, the higher the accuracy of the signal reconnaissance operation will be.

[0081] Compare the interference parameter settings and interference transmission in the signal interference operation with the standard operation of the signal interference operation. The more identical steps there are, the higher the accuracy of evaluating the signal interference operation.

[0082] Evaluate the rationality of the operation through the reciprocity of the operation trajectory;

[0083] Specifically:

[0084] If there are more reciprocating trajectories per unit time in the operation trajectory, the rationality of the operation is lower.

[0085] Comprehensive scoring based on operational agility, operational accuracy and operational rationality;

[0086] Specifically, the comprehensive score is equal to the weighted sum of operation agility, operation accuracy and operation rationality. In this embodiment:

[0087] Comprehensive score = operation accuracy × 40% + operation agility × 30% + operation rationality × 30%

[0088] An evaluation system designed in this way can comprehensively consider all aspects of the dataset construction and annotation process, ensuring that the final evaluation results are comprehensive and objective, and helping to improve the accuracy and stability of the solution.

[0089] S4. If it is identified that the operator's signal reconnaissance operation or signal interference operation steps are different from the standard operation, the steps of the standard operation are displayed on the operation interface.

[0090] Provide real-time feedback to help operators improve their operating skills. By marking key areas on the interface or providing prompts through the interface, the model can provide timely guidance to operators, prompting them to make more optimized decisions during operation. This supports operators' learning and skill improvement.

[0091] The present application also implements an electronic countermeasure reconnaissance and jamming training evaluation system based on image recognition.

[0092] It should be understood that the above-mentioned system is used to execute the methods in the above-mentioned embodiments. The implementation principles and technical effects of the corresponding modules in the system are similar to those described in the above-mentioned methods. The working process of the system can refer to the corresponding process in the above-mentioned method and will not be repeated here.

[0093] Based on the method in the above embodiment, an embodiment of the present application provides an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The processor may call the logic instructions in the memory to execute the method in the above embodiment.

[0094] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0095] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0096] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0097] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0098] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0099] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a storage medium or transmitted through the storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0100] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0101] The above content is easily understood by those skilled in the art. The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. An electronic countermeasure reconnaissance jamming training evaluation method based on image recognition, characterized in that: The specific steps include: During the training process, the operation images of the operator on the electronic countermeasure equipment are collected in real time, and the operation images are preprocessed; Identifying the operator's signal reconnaissance operation, signal jamming operation and operation trajectory from the operation image through an image recognition model; The operation agility is evaluated by the timing of the signal reconnaissance operation and the signal interference operation; the operation accuracy is evaluated by the errors of the signal reconnaissance operation and the signal interference operation; the operation rationality is evaluated by the reciprocity of the operation trajectory; and a comprehensive score is given based on the operation agility, operation accuracy and operation rationality.

2. The electronic countermeasure reconnaissance jamming training evaluation method according to claim 1 is characterized in that: The signal reconnaissance operation and signal jamming operation of the operator are identified from the operation image by an image recognition model, specifically: The image recognition model is used to identify menu selections, button selections and input text in the operation interface from the operation image; based on the menu selections, button selections and input text, the reconnaissance parameter settings, signal searches and signal interception in the signal reconnaissance operation are determined; and the interference parameter settings and interference emissions in the signal interference operation are determined.

3. The electronic countermeasure reconnaissance jamming training evaluation method according to claim 1 is characterized in that: The operation track is identified from the operation image by using an image recognition model, specifically: The image recognition model is used to recognize the operation points in the operation interface from the operation image, and the operation trajectory is obtained from the position time-series change trajectory of the operation point.

4. The electronic countermeasure reconnaissance jamming training evaluation method according to claim 1, 2 or 3, characterized in that: The training method of the image recognition model comprises the following steps: Collect training videos in different working scenarios, different training items and different weather conditions; Annotate menu selections, button selections, text inputs, and operation points in the operation interface in each video frame of the training video; The labeled video frames are used as training sets, and the image recognition model is obtained based on convolutional neural network training.

5. The electronic countermeasure reconnaissance jamming training evaluation method according to claim 1 is characterized in that: The operational agility is evaluated by the timing of the signal reconnaissance operation and the signal jamming operation, specifically: The training start time is recorded as t0; If signal interception is identified from the first operation image, the acquisition time of the first operation image is recorded as t1; If interference emission is identified from the second operation image, the acquisition time of the second operation image is recorded as t2; The smaller the t1-t0 value is, the higher the agility of the evaluated signal reconnaissance operation is; The smaller the t2-t0 value is, the higher the agility of the evaluated signal interference operation is.

6. The electronic countermeasure reconnaissance jamming training evaluation method according to claim 1 is characterized in that: The operation accuracy is evaluated by the operation errors of the signal reconnaissance operation and the signal interference operation, specifically: Compare the reconnaissance parameter setting, signal search, and signal interception in the signal reconnaissance operation with the standard operation in the signal reconnaissance operation. The more identical steps there are, the higher the accuracy of the signal reconnaissance operation will be. Compare the interference parameter settings and interference transmission in the signal interference operation with the standard operation in the signal interference operation. The more identical steps there are, the higher the accuracy of evaluating the signal interference operation.

7. The electronic countermeasure reconnaissance jamming training evaluation method according to claim 1 is characterized in that: The rationality of the operation is evaluated by the reciprocity of the operation trajectory, specifically: If there are more reciprocating trajectories per unit time in the operation trajectory, the rationality of the operation is lower.

8. The electronic countermeasure reconnaissance jamming training evaluation method according to claim 1 is characterized in that: Comprehensive scoring is based on operational agility, operational accuracy and operational rationality, specifically: The comprehensive score is equal to the weighted sum of operational agility, operational accuracy, and operational rationality.

9. The electronic countermeasure reconnaissance jamming training evaluation method according to claim 1 is characterized in that: The reconnaissance interference training evaluation method also includes: If it is identified that the operator's signal reconnaissance operation or signal interference operation steps are different from the standard operation, the steps of the standard operation are displayed on the operation interface.

10. An electronic countermeasure reconnaissance jamming training and evaluation system based on image recognition, characterized in that: The system specifically comprises: An image acquisition and processing module is used to acquire, in real time during the training process, the operation images of the operator on the electronic countermeasure equipment and to pre-process the operation images; An operation recognition module, used to recognize the operator's signal reconnaissance operation, signal jamming operation and operation trajectory from the operation image through an image recognition model; An operation evaluation module is used to evaluate the operation agility through the timing of the signal reconnaissance operation and the signal interference operation; evaluate the operation accuracy through the errors of the signal reconnaissance operation and the signal interference operation; evaluate the operation rationality through the reciprocity of the operation trajectory; and perform a comprehensive score based on the operation agility, operation accuracy and operation rationality.

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