An automatic identification method and system for projectile head shape factors

Abstract

This invention discloses an automatic projectile nose shape factor recognition method and system, belonging to the field of image processing and target detection technology. The method includes: acquiring a projectile penetration image; performing semantic segmentation on the projectile penetration image using a preset image segmentation model to obtain a projectile binarized mask; performing projectile contour detection on the projectile binarized mask using a preset contour detection algorithm, and obtaining a set of projectile outer contour pixels based on the projectile contour detection results; extracting a projectile nose shape region contour point set based on the projectile outer contour pixel set; fitting a projectile nose shape curve based on the projectile nose shape region contour point set; and calculating the projectile nose shape factor based on the projectile nose shape curve. Using the technical solution of this invention, efficient, accurate, and automated extraction of projectile nose shape geometric features can be achieved.

Description

Technical Field

[0001] This invention relates to the field of image processing and target detection technology, and in particular to an automatic method and system for identifying projectile head shape factors. Background Technology

[0002] The projectile nose shape factor is a crucial feature parameter for measuring the sharpness of the projectile's nose in high-speed penetration models, and its efficient and accurate intelligent identification and extraction are of significant research importance. In application scenarios such as armor-piercing projectiles and high-speed aircraft, even small changes in the nose shape factor can have a significant impact on penetration depth and attitude stability. Therefore, high-precision and efficient automated identification of the nose shape factor has become a key technical challenge in intelligent projectile modeling and evaluation. Currently, the identification and extraction of the projectile nose shape factor mainly relies on manual intervention. Common methods include manually annotating the projectile contour in images, assisting with geometric fitting, and calculating the corresponding parameters accordingly.

[0003] These traditional methods are not only labor-intensive but also susceptible to operator bias, leading to poor repeatability. Furthermore, the lack of stability control in order selection and parameter adjustment during polynomial fitting can cause large fluctuations in fitting accuracy and poor stability, affecting the reliability of the recognition results. Simultaneously, when dealing with complex backgrounds, low image contrast, or drastic changes in projectile attitude, traditional manual or rule-based algorithms are insufficient in robustness and adaptability, easily affected by noise, uneven lighting, or occlusion, resulting in recognition failures or large errors. Moreover, these methods typically cannot be seamlessly integrated with subsequent geometric modeling and numerical analysis processes, making it difficult to support high-throughput processing of large numbers of projectile images and failing to meet the demands of current intelligent and automated development. Summary of the Invention

[0004] This invention provides an automatic identification method and system for projectile head shape factors, which solves the technical problems of existing projectile head shape factor identification and extraction schemes, which are not only labor-intensive, but also limited by the subjective judgment of operators, easily leading to poor repeatability and reliability of the identification results, making it difficult to support high-throughput processing of a large number of projectile images, and also difficult to meet the current needs of intelligent and automated development.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] On one hand, the present invention provides an automatic identification method for projectile head shape factors, comprising:

[0007] Obtain projectile penetration images;

[0008] The projectile penetration image is semantically segmented using a pre-defined image segmentation model to obtain a binarized mask image of the projectile.

[0009] A preset contour detection algorithm is used to detect the contour of the projectile in the binary mask image. Based on the contour detection results, the set of pixels of the projectile's outer contour is obtained.

[0010] Based on the pixel set of the outer contour of the projectile, extract the contour point set of the projectile's nose region;

[0011] The projectile nose shape curve is fitted based on the set of contour points of the projectile nose shape region;

[0012] The projectile nose shape factor is calculated based on the projectile nose shape curve.

[0013] Furthermore, the image segmentation model has an adaptive strategy, which can adaptively select the optimal segmentation strategy based on the image texture complexity, texture background, or target clarity, thereby automatically extracting the projectile region and generating a projectile binarized mask image.

[0014] Furthermore, the contour detection algorithm is an edge detection algorithm based on image structure information and region connectivity judgment.

[0015] Furthermore, a preset contour detection algorithm is used to perform projectile contour detection on the projectile binarized mask image. Based on the projectile contour detection results, a set of projectile outer contour pixels is obtained, including:

[0016] A preset contour detection algorithm is used to detect the contour of the projectile in the binary mask image, and the detected contour coordinates are mapped onto the original image for visualization.

[0017] Based on the visualization results, a contour matching scoring mechanism is introduced to quantitatively evaluate the overlap between the extracted projectile contour and the actual projectile contour, in order to verify the contour extraction accuracy. This forms a closed-loop mechanism from image semantic segmentation to contour geometric restoration and then to accuracy feedback, in order to obtain the pixel set of the projectile's outer contour.

[0018] Furthermore, based on the pixel set of the projectile's outer contour, the contour point set of the projectile's nose region is extracted, including:

[0019] The projectile image is uniformly set to head-down in the coordinate system;

[0020] Find the point with the largest Y-coordinate value in the image coordinate system from the set of pixels on the outer contour of the projectile, and use it as the starting point of the projectile's nose region.

[0021] Starting from the initial point, the system traverses along the sequence of contour points. During the traversal, it analyzes in real time the changes in tangent direction, curvature distribution, and polynomial fitting error between consecutive contour points. When the change in tangent direction exceeds a preset threshold, the change in curvature distribution exceeds a preset threshold, or the polynomial fitting error exceeds a preset threshold, the current point is automatically determined to be the termination point of the projectile's nose region, thereby achieving adaptive closure extraction of the projectile's nose region's edge segments.

[0022] Furthermore, the projectile nose shape curve is fitted based on the set of contour points of the projectile nose region, including:

[0023] Based on the contour point set of the projectile's nose region, an adaptive polynomial fitting model is used for modeling to fit the projectile's nose curve. The adaptive polynomial fitting model automatically searches for fitting schemes of different orders under the minimum mean square error criterion through an objective function optimization algorithm, and determines the optimal order and fitting coefficients based on the mean square error threshold. Furthermore, the adaptive polynomial fitting model supports the use of piecewise fitting and splicing strategies.

[0024] Furthermore, the fitting expression for the projectile nose profile curve is:

[0025] y = -0.00165x 2 +0.53477x+315.6507

[0026] Where y represents the ordinate of the point where the projectile's nose contour points are concentrated; and x represents the abscissa of the point where the projectile's nose contour points are concentrated.

[0027] Furthermore, based on the projectile nose profile curve, the projectile nose profile factor is calculated, including:

[0028] Based on the projectile nose shape curve, the geometric feature parameters required to calculate the projectile nose shape factor are extracted;

[0029] Based on the extracted geometric feature parameters, and using a preset physical calculation formula for the projectile nose shape factor, the system automatically performs a quantitative calculation of the projectile nose shape factor for the current stage and outputs the calculation results. The physical calculation formula is as follows:

[0030]

[0031] Where, N * Represents the nose shape factor; R represents half the maximum width of the warhead in the x-direction; y ′ y represents the first derivative of y; h represents the length of the projectile along the y direction; y represents the ordinate of the point where the projectile's nose contour is concentrated; x represents the abscissa of the point where the projectile's nose contour is concentrated.

[0032] On the other hand, the present invention also provides an automatic identification system for projectile head shape factors, comprising:

[0033] The image input module is used to acquire images of projectile penetration.

[0034] The image segmentation model is used to perform semantic segmentation on the projectile penetration image using a preset image segmentation model to obtain a binary mask image of the projectile.

[0035] The contour extraction module is used for:

[0036] A preset contour detection algorithm is used to detect the contour of the projectile in the binary mask image. Based on the contour detection results, the set of pixels of the projectile's outer contour is obtained.

[0037] Based on the pixel set of the outer contour of the projectile, extract the contour point set of the projectile's nose region;

[0038] The fitting modeling module is used to fit the projectile nose shape curve based on the set of contour points of the projectile nose shape region.

[0039] The head shape factor calculation module is used to calculate the head shape factor of the projectile based on the projectile's head shape curve.

[0040] Furthermore, the system also includes a result visualization and export module for visualizing and exporting the projectile head shape factor calculation results.

[0041] In another aspect, the present invention also provides an electronic device comprising a processor and a memory; wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement the above-described method.

[0042] In another aspect, the present invention also provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the above method.

[0043] The beneficial effects of the technical solution provided by this invention include at least the following:

[0044] This invention achieves high-precision segmentation and contour extraction of the projectile's nose region by introducing a visual segmentation model and edge detection technology. Combined with an automatic closed region recognition method based on region connectivity and geometric topological constraints, it can quickly locate the target nose region without manual intervention, improving processing efficiency and recognition stability. An adaptive multinomial fitting model, combined with an objective function optimization algorithm, dynamically determines the optimal fitting order and parameter configuration, achieving accurate modeling of complex contours. The system adopts a modular architecture design and an automatic process control mechanism, with each functional unit operating collaboratively. The entire process, from image import to nose factor output, is automated, achieving efficient, accurate, and automated extraction of projectile nose geometric features. A visual interface and result verification mechanism are also provided to enhance the system's transparency and controllability. Compared to traditional methods relying on manual analysis, this invention offers higher recognition accuracy, adaptability, and automation, making it suitable for projectile nose structure evaluation tasks under complex conditions. It has significant application value and technological advancement. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of the automatic identification method for projectile head shape factors provided in the embodiments of the present invention;

[0047] Figure 2 This is a schematic diagram of the projectile head shape provided in an embodiment of the present invention;

[0048] Figure 3 This is a system block diagram of the electronic device provided in the embodiments of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0050] First, it should be noted that in the embodiments of the present invention, the words "exemplarily," "for example," etc., are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the term "exemplarily" is intended to present the concept in a specific manner. Furthermore, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either one or the other.

[0051] First Embodiment

[0052] This embodiment provides an automatic identification method for projectile head shape factors, which can be implemented by an electronic device. The execution flow of this method is as follows: Figure 1 As shown, by integrating technologies such as visual segmentation, contour extraction, and polynomial curve fitting optimization, efficient, accurate, and automated extraction of the geometric features of the projectile's head shape can be achieved.

[0053] Specifically, the automatic identification method for projectile head shape factor includes the following steps:

[0054] S1, acquire projectile penetration image;

[0055] It should be noted that, in this embodiment, the image data of the projectile during the penetration process can be obtained from the projectile penetration cloud map generated by finite element simulation, or from the dynamic images collected by a high-speed camera in the actual projectile penetration experiment. This embodiment does not make specific limitations on this.

[0056] S2, use a preset image segmentation model to perform semantic segmentation on the projectile penetration image to obtain a binary mask image of the projectile;

[0057] It should be noted that in this embodiment, the image segmentation model is a pre-trained large-scale visual segmentation model (such as SAM or its variants), or a customized deep learning image segmentation network, used for semantic-level image segmentation. The model possesses an adaptive strategy, with general semantic perception capabilities or specialized training capabilities for projectile structural features. It can adaptively select the optimal segmentation strategy based on conditions such as image texture complexity, texture background, or target sharpness, thereby automatically and effectively extracting the projectile region and generating a high-quality binary mask image. This effectively suppresses interference from complex backgrounds and improves the robustness and versatility of mask segmentation.

[0058] S3, a preset contour detection algorithm is used to perform projectile contour detection on the projectile binarized mask image, and based on the projectile contour detection results, the projectile outer contour pixel set is obtained;

[0059] It should be noted that the contour detection algorithm used in S3 above can be any existing contour detection algorithm. Specifically, in this embodiment, the contour detection algorithm is an edge detection algorithm based on image structure information and region connectivity judgment. This algorithm combines pixel gradient direction consistency, pixel density distribution, region connectivity and edge closure criteria, which can effectively identify the continuous structure of contour curves in high curvature or weak contrast backgrounds, thereby enabling stable contour extraction in complex backgrounds or high curvature boundaries.

[0060] The specific implementation process is as follows: The system automatically extracts the pixel set of the projectile's outer contour and maps its coordinates onto the original image for visualization. To improve recognition accuracy, the system introduces a contour matching scoring mechanism to quantitatively evaluate the overlap between the extracted contour and the actual target contour, assisting users in quickly verifying contour accuracy. The process involves the mapped contour being automatically overlaid on the original image, and intelligent visualization verification of the contour matching score is supported, forming a closed-loop mechanism from image semantic segmentation to contour geometric reconstruction and then to accuracy feedback. This improves the robustness and completeness of contour extraction.

[0061] S4. Based on the outer contour pixel set of the projectile, extract the contour point set of the projectile's head region.

[0062] It should be noted that, in this embodiment, the initial reference point for the projectile's nose region is determined by uniformly setting the projectile image in the coordinate system so that the nose is facing downwards, and using the point corresponding to the maximum value of the Y-axis coordinate as the starting point of the projectile's nose region; for example... Figure 2 As shown, the projectile's nose point is pointing downwards. In the image coordinate system, the contour point corresponding to the maximum value of the Y-axis coordinate is taken as the starting point of the projectile's nose region. Based on the projectile's left-right symmetrical structure, only the right contour is selected for analysis, and the continuous contour segment starting from the starting point on this side is taken as the candidate boundary of the nose region.

[0063] Based on the above, the process of extracting the contour point set of the projectile's nose region in this embodiment is as follows:

[0064] The system automatically identifies the point with the largest Y-coordinate value in the image coordinate system from the contour point set, using it as the starting point of the projectile's head shape region, and traverses along the contour point sequence direction (e.g., counterclockwise). During the traversal, the system analyzes in real time the changes in tangent direction, curvature distribution characteristics, and polynomial fitting error based on a local sliding window between consecutive contour points. When a sudden change in tangent direction occurs or the fitting residual exceeds a set threshold, the system automatically determines the current point as the termination point of the projectile's head shape region. This achieves adaptive closure extraction of the projectile's head segment, avoiding manual boundary setting or template matching, significantly enhancing the system's adaptability to different projectile structures, and improving the algorithm's universality and robustness.

[0065] S5, the projectile nose shape curve is fitted based on the contour point set of the projectile nose shape region;

[0066] It should be noted that the algorithm model for fitting the projectile nose shape curve based on the contour point set of the projectile nose shape region in the above S5 can be any existing fitting model algorithm. Specifically, in this embodiment, an adaptive polynomial fitting model is used for accurate modeling of the nose shape region contour point set extracted in the above steps. The adaptive polynomial fitting model introduces a target optimization algorithm, which automatically searches for fitting schemes of different orders under the minimum mean square error criterion through the objective function optimization algorithm. The optimal order and fitting coefficient of the fitting polynomial are determined according to the fitting error threshold, ensuring that the modeling has good computational efficiency and generalization ability while meeting the accuracy requirements. The curve fitting expression in this embodiment is shown in Equation (1). In addition, for complex contour regions, the model supports the use of piecewise fitting and splicing strategies, which use piecewise fitting and boundary splicing algorithms to continuously and accurately model complex contour regions. This ensures the continuity and physical consistency of the fitted curve, while improving the overall modeling accuracy and efficiency.

[0067] y = f(x) = -0.00165x 2 +0.53477x+315.6507 (1)

[0068] Where y represents the ordinate of the point where the projectile's nose contour points converge; and x represents the abscissa of the point where the projectile's nose contour points converge. This curve represents the variation of the 1 / 2 region of the projectile's nose in the xy two-dimensional coordinate system.

[0069] S6, Calculate the projectile nose shape factor based on the projectile nose shape curve;

[0070] It should be noted that, in this embodiment, the implementation process of S6 is as follows:

[0071] Based on the fitted curve model, representative geometric feature parameters are automatically extracted, and combined with the physical calculation formula of the projectile nose shape factor, the quantitative calculation of the projectile nose shape factor at the current stage is automatically completed, and the parameter results that can be used for subsequent analysis, evaluation or structural optimization are output. The nose shape factor calculation formula is shown in (2). This calculation process supports parameterized configuration, template definition and result visualization, and is applicable to various projectile structures and application scenarios.

[0072]

[0073] Where, N * The shape factor represents the nose cone, showing consistency in reflecting the sharpness of the projectile's nose cone; R represents half the maximum width of the projectile in the x-direction; y ′ y represents the first derivative of the function y; h represents the length of the projectile along the y direction; y represents the ordinate of the point where the projectile's nose contour is concentrated; x represents the abscissa of the point where the projectile's nose contour is concentrated.

[0074] By encapsulating all the above steps into a unified recognition system framework and adopting modular design and automated control processes, the entire process of automatic recognition, from image import, mask segmentation, contour extraction, structural modeling to factor output, can be achieved without manual intervention, and has high precision, high reliability and high adaptability.

[0075] In summary, this embodiment constructs a full-process recognition scheme from image input to parameter output, integrating technologies such as visual segmentation, contour extraction, and polynomial curve fitting optimization. This scheme balances recognition accuracy, processing efficiency, stability, and system adaptability, constructing an intelligent closed-loop recognition framework from image input to nose shape factor output. It can provide technical support for tasks such as projectile structure modeling, ballistic analysis, and damage prediction, achieving efficient, accurate, and automated extraction of projectile nose shape geometric features. It is suitable for projectile nose shape factor recognition and evaluation tasks under complex operating conditions, possessing significant engineering application value and technological leadership.

[0076] Second Embodiment

[0077] This embodiment provides an automatic projectile nose shape factor recognition system. The automatic projectile nose shape factor recognition system adopts a modular design and includes the following modules:

[0078] The image input module is used to acquire images of projectile penetration.

[0079] The image segmentation model is used to perform semantic segmentation on the projectile penetration image using a preset image segmentation model to obtain a binary mask image of the projectile.

[0080] The contour extraction module is used for:

[0081] A preset contour detection algorithm is used to detect the contour of the projectile in the binary mask image. Based on the contour detection results, the set of pixels of the projectile's outer contour is obtained.

[0082] Based on the pixel set of the outer contour of the projectile, extract the contour point set of the projectile's nose region;

[0083] The fitting modeling module is used to fit the projectile nose shape curve based on the set of contour points of the projectile nose shape region.

[0084] The head shape factor calculation module is used to calculate the projectile head shape factor based on the projectile head shape curve.

[0085] The results visualization and export module is used for visualizing and exporting the calculation results of the projectile head shape factor.

[0086] This embodiment integrates the aforementioned functional modules into a unified software system platform, employing a modular design and automated process control mechanism to sequentially complete the entire process of image import, segmentation and recognition, contour extraction, structural modeling, and factor output. The system supports automated process control and the visualization and export of results, and features high precision, high stability, and high adaptability. It can automatically identify projectile head shape factors without human intervention, significantly improving engineering analysis efficiency.

[0087] It should be noted that the automatic projectile head shape factor recognition system of this embodiment corresponds to the automatic projectile head shape factor recognition method of the first embodiment described above; the functions implemented by each functional module in the automatic projectile head shape factor recognition system of this embodiment correspond one-to-one with the process steps in the automatic projectile head shape factor recognition method of the first embodiment described above; therefore, they will not be described again here.

[0088] Third Embodiment

[0089] This embodiment provides an electronic device, such as... Figure 3 As shown, the electronic device includes a processor and a memory; wherein the processor and the memory can be connected via a communication bus; the memory stores at least one instruction, which is loaded and executed by the processor to implement the method of the first embodiment described above. Furthermore, the electronic device may also include a transceiver, the processor and the transceiver can be connected via a communication bus, and the transceiver is used to communicate with other devices.

[0090] Below, in conjunction with Figure 3 A detailed introduction to each component of this electronic device is provided below:

[0091] The processor is the control center of the electronic device. The electronic device may include multiple processors, each of which can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The term "processor" can refer to a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), other general-purpose processors, application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), one or more field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor can perform various functions of the electronic device by running or executing software programs stored in memory and by calling data stored in memory.

[0092] In a specific implementation, as one example, the processor may include one or more CPUs, for example... Figure 3 CPU0 and CPU1 shown are, of course, merely illustrative examples.

[0093] The memory is used to store the software program that executes the solution of the present invention, and the processor controls its execution. For specific implementation methods, please refer to the above method embodiments, which will not be repeated here.

[0094] Optionally, the memory may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory may be integrated with the processor or may exist independently, and may be accessed through the interface circuit of the electronic device (…). Figure 3 (Not shown in the image) is coupled to the processor; however, this embodiment of the invention does not impose specific limitations on this.

[0095] The transceiver may include a receiver and a transmitter. Figure 3 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function. The transceiver can be integrated with the processor or exist independently, and is connected through the interface circuit of the electronic device (…). Figure 3 (Not shown in the image) is coupled to the processor, and this embodiment of the invention does not specifically limit this.

[0096] In addition, it should be noted that, Figure 3 The structure of the electronic device shown is not intended to limit the device. Actual devices may include more or fewer components than shown, or combine certain components, or have different component arrangements. Furthermore, the technical effects achieved by this electronic device when performing the method of the first embodiment described above can be referenced to the technical effects described in the first embodiment; therefore, they will not be repeated here.

[0097] Fourth embodiment

[0098] This embodiment provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the method of the first embodiment described above. The computer-readable storage medium may be a ROM, random access memory, CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc. The instruction stored therein can be loaded and executed by a processor in a terminal.

[0099] Furthermore, it should be noted that the present invention can be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention can take the form of a completely or partially hardware embodiment, a completely or partially software embodiment, or an embodiment combining software and hardware aspects. Moreover, when implemented in software, embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any usable medium accessible to a computer or a data storage device such as a server or data center containing one or more sets of usable media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive (SSD).

[0100] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0101] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element. Furthermore, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Additionally, the character " / " in this text generally indicates an "or" relationship between the preceding and following objects, but it can also indicate an "AND / OR" relationship. Please refer to the context for specific interpretations. "At least one" refers to one or more items, while "more than" refers to two or more items. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can be represented as: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0103] Furthermore, it is understood that in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0104] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0105] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of functional modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs. Additionally, the functional units in the various embodiments of this invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0106] If the method is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0107] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments of the present invention have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make several improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A method for automatic identification of projectile head shape factors, characterized in that, include: Obtain projectile penetration images; The projectile penetration image is semantically segmented using a pre-defined image segmentation model to obtain a binarized mask image of the projectile. A preset contour detection algorithm is used to detect the contour of the projectile in the binary mask image. Based on the contour detection results, the set of pixels of the projectile's outer contour is obtained. Based on the pixel set of the projectile's outer contour, extract the contour point set of the projectile's nose region; The projectile nose shape curve is fitted based on the set of contour points of the projectile nose shape region; The projectile nose shape factor is calculated based on the projectile nose shape curve.

2. The automatic identification method for projectile head shape factors as described in claim 1, characterized in that, The image segmentation model has an adaptive strategy that can adaptively select the optimal segmentation strategy based on the image texture complexity, texture background, or target clarity, thereby automatically extracting the projectile region and generating a binary mask image of the projectile.

3. The automatic identification method for projectile head shape factors as described in claim 1, characterized in that, The contour detection algorithm is an edge detection algorithm based on image structure information and region connectivity judgment.

4. The automatic identification method for projectile head shape factors as described in claim 3, characterized in that, A preset contour detection algorithm is used to detect the projectile contour in the binary mask image. Based on the projectile contour detection results, a set of projectile outer contour pixels is obtained, including: A preset contour detection algorithm is used to detect the contour of the projectile in the binary mask image, and the detected contour coordinates are mapped onto the original image for visualization. Based on the visualization results, a contour matching scoring mechanism is introduced to quantitatively evaluate the overlap between the extracted projectile contour and the actual projectile contour, in order to verify the contour extraction accuracy. This forms a closed-loop mechanism from image semantic segmentation to contour geometric restoration and then to accuracy feedback, in order to obtain the pixel set of the projectile's outer contour.

5. The automatic identification method for projectile head shape factors as described in claim 1, characterized in that, Based on the pixel set of the projectile's outer contour, the contour point set of the projectile's nose region is extracted, including: The projectile image is uniformly set to head-down in the coordinate system; Find the point with the largest Y-coordinate value in the image coordinate system from the set of pixels on the outer contour of the projectile, and use it as the starting point of the projectile's nose region. Starting from the initial point, the system traverses along the sequence of contour points. During the traversal, it analyzes in real time the changes in tangent direction, curvature distribution, and polynomial fitting error between consecutive contour points. When the change in tangent direction exceeds a preset threshold, the change in curvature distribution exceeds a preset threshold, or the polynomial fitting error exceeds a preset threshold, the current point is automatically determined to be the termination point of the projectile's nose region, thereby achieving adaptive closure extraction of the projectile's nose region's edge segments.

6. The automatic identification method for projectile head shape factors as described in claim 1, characterized in that, The projectile nose shape curve is fitted based on the contour point set of the projectile nose region, including: Based on the contour point set of the projectile's nose region, an adaptive polynomial fitting model is used for modeling to fit the projectile's nose curve. The adaptive polynomial fitting model automatically searches for fitting schemes of different orders under the minimum mean square error criterion through an objective function optimization algorithm, and determines the optimal order and fitting coefficients based on the mean square error threshold. Furthermore, the adaptive polynomial fitting model supports the use of piecewise fitting and splicing strategies.

7. The automatic identification method for projectile head shape factors as described in claim 1, characterized in that, The fitting expression for the projectile nose profile curve is: y=-0.00165x 2 +0.53477x+315.6507 Where y represents the ordinate of the point where the projectile's nose contour points are concentrated; and x represents the abscissa of the point where the projectile's nose contour points are concentrated.

8. The automatic identification method for projectile head shape factors as described in claim 1, characterized in that, Based on the projectile nose shape curve, the projectile nose shape factor is calculated, including: Based on the projectile nose shape curve, the geometric feature parameters required to calculate the projectile nose shape factor are extracted; Based on the extracted geometric feature parameters, and using a preset physical calculation formula for the projectile nose shape factor, the system automatically performs a quantitative calculation of the projectile nose shape factor for the current stage and outputs the calculation results. The physical calculation formula is as follows: Where, N * Represents the nose shape factor; R represents half the maximum width of the warhead in the x-direction; y ′ y represents the first derivative of y; h represents the length of the projectile along the y direction; y represents the ordinate of the point where the projectile's nose contour is concentrated; x represents the abscissa of the point where the projectile's nose contour is concentrated.

9. An automatic identification system for projectile head shape factors, characterized in that, include: The image input module is used to acquire images of projectile penetration. The image segmentation model is used to perform semantic segmentation on the projectile penetration image using a preset image segmentation model to obtain a binary mask image of the projectile. The contour extraction module is used for: A preset contour detection algorithm is used to detect the contour of the projectile in the binary mask image. Based on the contour detection results, the set of pixels of the projectile's outer contour is obtained. Based on the pixel set of the projectile's outer contour, extract the contour point set of the projectile's nose region; The fitting modeling module is used to fit the projectile nose shape curve based on the set of contour points of the projectile nose shape region. The head shape factor calculation module is used to calculate the head shape factor of the projectile based on the projectile's head shape curve.

10. The automatic projectile head shape factor recognition system as described in claim 9, characterized in that, The system also includes a result visualization and export module, which is used to visualize and export the calculation results of the projectile head shape factor.

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