Military Objects Research Articles

Retraction Note: Military object detection in defense using multi-level capsule networks

Retraction Note: Military object detection in defense using multi-level capsule networks

Military Image Captioning for Low-Altitude UAV or UGV Perspectives

Low-altitude unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), which boast high-resolution imaging and agile maneuvering capabilities, are widely utilized in military scenarios and generate a vast amount of image data that can be leveraged for textual intelligence generation to support military decision making. Military image captioning (MilitIC), as a visual-language learning task, provides innovative solutions for military image understanding and intelligence generation. However, the scarcity of military image datasets hinders the advancement of MilitIC methods, especially those based on deep learning. To overcome this limitation, we introduce an open-access benchmark dataset, which was termed the Military Objects in Real Combat (MOCO) dataset. It features real combat images captured from the perspective of low-altitude UAVs or UGVs, along with a comprehensive set of captions. Furthermore, we propose a novel encoder–augmentation–decoder image-captioning architecture with a map augmentation embedding (MAE) mechanism, MAE-MilitIC, which leverages both image and text modalities as a guiding prefix for caption generation and bridges the semantic gap between visual and textual data. The MAE mechanism maps both image and text embeddings onto a semantic subspace constructed by relevant military prompts, and augments the military semantics of the image embeddings with attribute-explicit text embeddings. Finally, we demonstrate through extensive experiments that MAE-MilitIC surpasses existing models in performance on two challenging datasets, which provides strong support for intelligence warfare based on military UAVs and UGVs.

The Last Starfighter

Museum objects have the potential to act as powerful symbols of national identity and modernity. This article examines the three lives of Starfighter 637, a Norwegian military aircraft that flew in the Arctic during the Cold War before becoming an exhibit at the Norsk Luftfartsmuseum and finally being restored to fly again by a group of aviation enthusiasts. The case study of the Starfighter considers the role of socialisation in generating a military object’s meanings and value as cultural heritage, but also how museums can convey the kinetic experience of flying.

Устаревшая лексика в карачаево-балкарском нартском эпосе

This article considers the lexical layer of the language, the study of which contributes to a more in-depth understanding of the culture, life, traditions of the people. Outdated vocabulary, repre-sented by historicisms and archaisms, is one of the under-explored layers of vocabulary and is the most mobile level of language in which various changes constantly occur. The reasons for archa-ization can be different, i.e. they can be associated with social transformations in the life of socie-ty, and can also be because of linguistic laws. The article attempted to lexical semantic classifica-tion of outdated words found in the texts of the Karachay-Balkarian heroic epic “Narts.” The re-sults obtained showed that outdated words of the Karachay-Balkarian language can be combined into the following thematic groups depending on their belonging to certain spheres of life: mili-tary terminology, class position, human activity, objects and phenomena of nature, names of weight measure, distance, etc. It should be noted that in the texts of the Nart epic, in addition to outdated substantive vocabulary, there are adjectives and verbs. It should also be noted that, in ep-ic texts, lexical archaisms prevail over semantic ones.

Developing a technology for modeling radar portraits of complex-shape objects for intelligent recognition systems

The object of this study is the modeling of radar portraits (RPs) for intelligent recognition systems based on the use of faceted 3D models. In order to solve the problems of target identification in homing systems of high-precision missile weapons, a technology is needed that could make it possible to efficiently and quickly generate RPs of military objects of complex shape in the required quantity. The research results are based on a combination of separate component technologies, in particular: the devised technology of using faceted 3D models – their construction and further processing with invisible surfaces excluded from it for an arbitrary viewing angle. The basic part of the work is the development of an algorithm and technological procedures for the formation of a spatial tracing grid for the current observation angle. A feature of the proposed technology is the application of a facet selection algorithm using an array of tracing facets and the application of the Huygens-Fresnel principle to recognize objects of complex shape. The RP database of military objects of complex shape was built. The results of modeling faceted RP’s, in particular the armored boat "Gyurza-M", are given. The results of the experimental study showed the ability to recognize the type of military object of complex shape at the level of 80‒90 %, which makes the use of this technology appropriate for recognizing military objects of complex shape. The achieved high-speed and quality characteristics of RP generation of military objects of a complex shape makes it possible to assume that the main prospective field of practical application is the identification and visual interpretation of targets in homing systems of high-precision missile weapons

Relevance of the legal regulation of military infrastructure capital construction objects during of the initial period of the Great Patriotic war

The article attempts to summarize some aspects of the experience of legal regulation of capital construction of military infrastructure facilities, which was accumulated during the Great Patriotic War, especially during its most difficult, first period, when the initiative was in the hands of the aggressor, and the Soviet state faced the vital issue of rapid economic transition, including military-the construction complex — on the rails of functioning in conditions of a real military threat. The idea of the continuing relevance of this experience in the current situation of a real threat to the sovereignty of Russia is consistently carried out, proposals are formulated on the need, taking into account the specifics of the construction of the above-mentioned facilities, to codify the norms governing the capital construction of military defense infrastructure facilities in the Military Construction Code of the Russian Federation with the unity of command of the federal body in the field of defense — the Ministry of Defense of the Russian Federation at all stages capital construction of these facilities.

Inner perimeter of the town fortifications and its role in the townspace

The aim of the article is to trace changes in the development of the area of the former town fortifications as an example of space recycling and adapting the town to changing conditions. A case study was carried out of Wrocław, one of the few cities in Poland with an inner and outer perimeters of mediaeval fortifications. We used archaeological sources, architectural research, documents and iconography to reconstruct the changes in the urban plots created by the division of the fortification area. Town walls change function from the military objects through the workshops, residential buildings, gardens, roads, up to their demolition. The role of the inner moat is also described, with attention drawn to ecological aspects of its functioning. Efforts were made to determine the long-term impact of those transformations on the social structure and city development.

ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ ДЛЯ РОЗПІЗНАВАННЯ ТЕРОРИСТИЧНИХ ТА ВОРОЖИХ ВІЙСЬКОВИХ ОБ’ЄКТІВ

The development of artificial intelligence technologies and their application in solving military tasks is the main concept of scientific advancement in Ukraine. Despite the availability of existing solutions for classifying limited types of civilian objects, there is a need to develop new technologies to gain an advantage over the enemy. Existing artificial intelligence models available in the public domain are oriented towards diagnosing restricted types of civilian objects. This limitation prevents the use of these models for analyzing military enemy equipment. Automating the process of recognizing enemy military objects by constructing a deep learning classification model. The process of building a classification model for enemy military equipment using deep neural networks is considered. The Sequential model type is chosen as the basis, which includes batch normalization layers added after each convolutional layer before further processing. Accelerated hyperparameter search is provided by the Random Search tool. The quality of classification models was measured using classical accuracy metrics. The implementation of the proposed solutions is carried out using the Python programming language and the Python Imaging Library, TensorFlow, Keras Tuner, and Matplotlib libraries. A convolutional neural network classification model has been constructed capable of classifying enemy military tanks and vehicles. The model demonstrates the best accuracy metrics on training/testing datasets of 0.6/0.55 with errors of 0.658/0.705, respectively. A comparative analysis of classification models with different types of architectures showed the advantage of the model in terms of accuracy and error rate when using an architecture that includes batch normalization layers. The created classification model is recommended for further training on new data or for solving computer vision tasks.

A maintainable and iterative development approach of critical systems with FoCaLiZe

The life-cycle development of critical systems follows mainly a V-cycle process overlapped with proofs and/or verification techniques. At the first development stage, a critical system must be, precisely, and as completely as possible described. Thus, it is recommended to use a graphical modeling language standard such as the Unified Modeling Language (UML) and the Object Constraint Language (OCL), to which both customers and developers could contribute. Then, the graphical design with its formal specification (requirements) is mapped into a complete formal development environment, using an MDE (Model-driven engineering) approach. The formal environment has to assess the development until the generation of a secure code. In this context, we propose a life-cycle development approach that combines UML/OCL and the FoCaLiZe formal environment for the secure development of critical systems. The proposed approach ensures essential development constraints such as strict boundaries and traceability between development phases. It also ensures a secure maintenance phase using partial transformation techniques from UML/OCL to FoCaLiZe. The latter enhances the transformation from UML/OCL into FoCaLiZe to support incremental development and assist developers in correcting modeling errors. The proposed approach is mostly dedicated to secure and maintainable lifecycle development processes of critical systems. The formal environment assesses the development process until the generation of a secure code. To illustrate the proposed approach, we present the stages of development of a constrained system controlling military objects. The development of this system uses the iterative, incremental, and maintainability stages as described by the development approach, until the generation of secure code.

Understanding lexico-semantic opposition empty/full in official business texts: Quantitative and qualitative research

This paper discusses the lexical-semantic opposition EMPTY/FULL in the corpus of legal, military and medical texts. The study analyzes 130 official business texts, in which we examined the problem of defining and constructing the lexical-semantic opposition EMPTY/FULL. The analysis promoted the construction of quantitative sampling, which demonstrates the weight of the lexemes in the corpus of official business texts. The purpose of the proposed analysis was to determine the distribution of the lexical-semantic opposition EMPTY/FULL, especially to find out the total number of occurrences, the average number of occurrences and the edge limits of the lexeme EMPTY and the lexeme FULL in the corpus of texts. The main results of the study outline the diversity of the lexical-semantic opposition EMPTY/FULL in different contexts, that underlines the functional diversification of the lexeme EMPTY and lexeme FULL. The results showed that in legal texts lexemes EMPTY/FULL nominate a legal concept; in medical texts they perform the function of medical conditions assessment; within the limits of military texts, they establish an associative connection between the nomination and semantic components (military actions, targets and objects of military use, consequences of war). Another important finding was the determination of the dominant lexeme in the corpus of legal texts and the dominant lexeme in the corpus of military texts. The similarities between EMPTY and FULL are primarily due to their antonymy. However, their differences are fixed in their distinct core meanings, contextual usage, pragmatic significance and connotations. These differences make them adaptable in various contexts of military, legal and medical texts to convey precise and often contrasting information about the state of objects and concepts.

Towards lightweight military object detection

Military object military object detection technology serves as the foundation and critical component for reconnaissance and command decision-making, playing a significant role in information-based and intelligent warfare. However, many existing military object detection models focus on exploring deeper and more complex architectures, which results in models with a large number of parameters. This makes them unsuitable for inference on mobile or resource-constrained combat equipment, such as combat helmets and reconnaissance Unmanned Aerial Vehicles (UAVs). To tackle this problem, this paper proposes a lightweight detection framework. A CSP-GhostnetV2 module is proposed in our method to make the feature extraction network more lightweight while extracting more effective information. Furthermore, to fuse multiscale information in low-computational scenarios, GSConv and the proposed CSP-RepGhost are used to form a lightweight feature aggregation network. The experimental results demonstrate that our proposed lightweight model has significant advantages in detection accuracy and efficiency compared to other detection algorithms.

Ukrainian Thoughts and Feelings based on One Year War Content Analysis

In the age of remarkable technological innovations such as AI, quantum computing, IoT, and blockchain, one could argue that the world is becoming an increasingly connected, advanced, enlightened, and peaceful place. Nevertheless, paradoxically, conflicts and wars are still born deeply rooted in complex strategic, historical, and economic dynamics, and continue to arise finding a direct representation in both physical and digital battlefields through powerful and dynamic environments like social media. This environment enabled unprecedented connectivity and ideas, thoughts, ideologies, and experiences exchange shaping the narrative, amplifying ideas, feelings, and experiences while directly addressing the underlying causes, dynamics, and implications of conflicts and war through a multifaceted and multi-nuanced approach. The outbreak and ongoing war in Ukraine took the international community off guard representing a significant turning point in the contemporary era that received immediately intensive global attention, united global humanitarian, and strategic efforts to support and help the ones in need, and reflected its geopolitical and economic global complexities, dynamics, and implications. At the same time, a high degree of disinformation and misinformation exists surrounding different aspects about this war e.g., actions taken, and impact produced on civilians, which contributes to creating an altered perspective of reality. While different conventional media and social media outlets together with research, governmental, and practitioner efforts revealed and reflected on the nature, major incidents, and their known impact on civilians and civilian objects as well as military personnel and military objects, yet limited attention and a lower voice is given through dedicated studies to thoughts and emotions of Ukrainian users in unconventional social media platforms like Telegram. This represents the knowledge gap tackled in this research by building a set of Machine Learning-based solutions for analysing the discourses and sentiments of Ukrainian users using the Design Science Research methodology in a Data Science research approach. From this analysis, reflection is provided to important days and incidents experienced by Ukrainian population since February 2022 in the first year of war.

Military camouflaged object detection with deep learning using dataset development and combination

Camouflaged object detection (COD) is one of the emerging artificial intelligence technologies. COD identifies objects that require attention and time to detect with human eyes due to the similarity in texture or color to the surrounding environment. Despite the importance of camouflage and its detection in military, there is a lack of military camouflaged object detection research. Previous studies point out that the general COD has not been well studied due to the lack of camouflaged datasets, and the situation is worse in the military domain. This study aims at tackling the challenge in two directions. First, we carefully assemble the military camouflaged object (MCAM) dataset, including camouflaged soldiers and people as well as camouflaged military supplies for military COD. The experiment shows that MCAM can generate better performance results than the other benchmark datasets (CAMO, COD10K). Second, military (MCAM) and nonmilitary camouflage datasets (benchmark datasets) are combined and tested to overcome data scarcity. The experiment shows that the nonmilitary camouflage datasets are effective for military COD at a certain level, and a proper combination of military and nonmilitary camouflage datasets can improve the detection performance.

Memorial Law: National and International Aspects

Introduction. This article conducts an interdisciplinary study of memory policy, which includes terminological explication and legal analysis of concepts similar in meaning, assessment of the relevance and need to legalize terms related to memory policy, determination of the effectiveness of official memory policy, prediction of its political and social consequences in the long term and exploring the normative limits of national memorial legislation. The purpose of the study is the conceptualization of memorial law.Materials and methods. The main research method is participant observation of the processes of implementation in Russian society and abroad of the concepts of national, historical and social memory, as well as the construction of norms and regulations for establishing legal relations in the field of conservation of military memorial heritage objects. Methods of express diagnostics of problem situations, legal assessment of incidents, sociological and expert surveys (including questionnaires and testing), and modeling were used.Research results. As a result of the study, the need to develop a special federal law on the protection of the military memorial heritage of the Russian Federation, as well as the development of an international (universal) Charter (Convention) on the protection of military memorial heritage sites formed as a result of the Second World War, was substantiated. Discussion and conclusion. It has been established that there is no single position in the scientific community regarding the need for memorial legislation. This largely depends on the political and ideological preferences of a particular author. At the same time, it was concluded that in legal science the conceptual foundations for the formation of memorial law as a special legal complex for the preservation, transmission and protection of historical memory and memorial (war memorial) heritage have been poorly studied. In addition, both at the level of legal doctrine and according to law enforcement practice, the issue of the status and legal means of preserving military memorial heritage has been practically not studied. In this connection, the authors propose the development of a Union Nation Charter for the Protection of Military Memorial Heritage for the purpose of holistic and comprehensive legal regulation of the area under study.

Promotion of military tourism as a tool of socioeconomic development of a region (on the example of Kyiv Oblast)


 
 
 The article provides evidences to support the notion that development of military tourism is a driver of socioeconomic growth of a region. In the present study, we used the gen- eral scientific and sophisticated scientific methods of research, including structural-functional analysis, statistical method, and comparative analysis. The study results were substantiated based on the data of the State Committee of Statistics of Ukraine, State Border Guard Service of Ukraine, State Agency of Tourism Development of Ukraine, Kyiv Oblast Center of the Development of Tourism, and materials from other scientific and official sources. We provided a definition of military tourism, which is proposed to be understood as a special kind of tourism in which the tourists can visit war-related historical sites and modern notable military objects; join the realities of the contemporary life in the military and familiarize themselves with the military experience; and participate in safe military events or historical reconstructions. We analyzed the reasons of decline in the tourist flows that occurred over the recent three years and presented the main ways of increasing them. We analyzed types of military tours offered by the Ukrainian tour guides on the domestic tourism market and studied the level of involvement of the respondents in military tourism in Ukraine. We demonstrated that military tourism is on offer in Ukraine, but against the backdrop of martial law, the internal tourism has specifics related to permits and restrictions that are in force during the war in relatively safe western and central regions of the country. Assessment of perspectives of military-tourism development in Ukraine using SWOT analysis revealed that it has many advantages, the main being the resources that can be used in creating a competitive tourism product, while the shortcomings include inaccessibility of the landmarks, absence of finances and investments, and high cost of the tours. For the socioeconomic development of Kyiv Oblast, we propose the Ukraine Undefeatable tour, having accounted for the abovementioned requirements to organizing tours in Ukraine in the conditions of martial law. The tour was developed based on the objects we selected in Kyiv Oblast. We confirmed that the development of military tourism is one of the most efficient drivers of socioeconomic growth of the region, Kyiv Oblast in particular, because it promotes increases in socioeconomic indicators of the region’s development.
 
 

FAILURE MODEL OF NON-RESTORABLE COMPLEX TECHNICAL OBJECT OF MILITARY EQUIPMENT

Maintenance (MT) is a necessary component the process of operating a complex technical object of military equipment, intended for long-term operation in wartime and harsh operating conditions. The volume, content and timing of maintenance must be completely determined by the reliability properties of the object, conditions and modes of its use. Effective implementation of any maintenance operation is possible only if the design of the object provides special means for this purpose (for measuring the defining parameters) and ensures the accessibility and convenience of performing the operation. Complex technical objects in modern society are extremely important. Such objects belong to the class of restored objects long-term repeated use. They are usually expensive and require significant operating costs. To ensure the required level of failure-free operation during their operation, maintenance is usually carried out, the essence of which is the timely preventive replacement of elements that are in a pre-failure state, which is very important for military equipment. A characteristic feature of complex technical objects for special purposes (military equipment) is the presence in their composition of a large number (tens, hundreds of thousands) different types of components that have different levels of reliability, different patterns of their wear and aging processes. This feature requires a more subtle approach to organizing and planning maintenance during operation (of military equipment). The above statements fully justify the conclusion about need to determine the main characteristics of the maintenance system in early stages of its design, when it is still possible to make changes to design of the object. In this work, a model of failure-free operation non-repairable complex technical object of military equipment is developed. The work also confirms the general idea that data obtained fully confirm the assumption that a maintenance strategy that is not restored is more preferable in the case of unreliable (inaccurate) information about the reliability indicators of elements of a military equipment object.

Оbject recognition system based on the Yolo model and database formation

A system for recognizing objects that are captured in real time on a video camera in a noisy environment that changes to the surrounding conditions has been built. The method of filling the database for mobile military objects was studied. For object recognition, the YOLO v8 neural network is used, which allows you to track moving and identify objects that fall into the video from the video camera. This neural network makes it possible to track objects with a change in scale, during movement with obstacles. It has been analyzed that the recognition of objects is carried out on the basis of contour analysis, comparison with a template and detection and matching of features. Artificial intelligence methods based on YOLO v8 were used to recognize military equipment. Trained for different YOLO models using Adam W, Adam, SGD optimizers and 512x512, 640x640, 1024x1024 px image resolution. Improved object recognition is achieved by analyzing contours, comparing patterns, and comparing entered special points. Different image resolutions and optimizers have shown different effects on model performance, and standard evaluation metrics do not provide the most accurate view. The most effective optimizer is gradient descent (SGD), which has shown the best accuracy for combat vehicle recognition. The gradient is usually considered as the sum of the gradients caused by each training element and is used to adjust the model parameters. As a result of the development of the system, indicators with recognition accuracy (accuracy) of 92%, F1-estimate (F1 score) – 89%, average indicator of accuracy (mAP) – 90% were formed. A method of filling the data set and creating a classifier is proposed. A model of combat vehicle recognition was built. Graphs, results of recognition of moving objects in the Yolo8 x neural network are presented.

Explainable artificial intelligence models for enhancing classification reliability of ground weapon systems

This study focused on the development of a reliable artificial intelligence (AI) model to enhance the classification reliability of ground weapon systems for surveillance and reconnaissance applications. The proposed AI model overcomes the limited data availability of military objects such as tanks, canons, and multiple-launch rockets by leveraging transfer learning and fine-tuning techniques. A comprehensive evaluation of 35 deep learning models using the publicly available Military-Vehicles dataset on Kaggle identified MobileNet as the most suitable model for ground weapon system classification. The selected MobileNet model achieved an average F1 score of 92% when tested on a dataset comprising five types of ground-weapon systems. In addition, the application of the explainable AI technique Grad-CAM provided insights into the decision-making process of the proposed model and verified its reliability. Real-world evaluations using frames extracted from training videos demonstrated promising accuracy for tanks, canons, and multiple-launch rockets. However, challenges related to object occlusion and the absence of target objects in the images were observed, which resulted in misclassifications. Overall, this study contributes to the development of explainable and reliable AI models for enhancing the performance of ground surveillance and reconnaissance systems.

Military Decision-Making Process Enhanced by Image Detection

This study delves into the vital missions of the armed forces, encompassing the defense of territorial integrity, sovereignty, and support for civil institutions. Commanders grapple with crucial decisions, where accountability underscores the imperative for reliable field intelligence. Harnessing artificial intelligence, specifically, the YOLO version five detection algorithm, ensures a paradigm of efficiency and precision. The presentation of trained models, accompanied by pertinent hyperparameters and dataset specifics derived from public military insignia videos and photos, reveals a nuanced evaluation. Results scrutinized through precision, recall, map@0.5, mAP@0.95, and F1 score metrics, illuminate the supremacy of the model employing Stochastic Gradient Descent at 640 × 640 resolution: 0.966, 0.957, 0.979, 0.830, and 0.961. Conversely, the suboptimal performance of the model using the Adam optimizer registers metrics of 0.818, 0.762, 0.785, 0.430, and 0.789. These outcomes underscore the model’s potential for military object detection across diverse terrains, with future prospects considering the implementation on unmanned arial vehicles to amplify and deploy the model effectively.

ANTI-DRONE PROTECTION USING UNIVERSAL FRAME MASKS

The materials of the article reveal the features of using universal frame masks for the comprehensive protection of military equipment and objects from attacks by the enemy's attack drones.
 The article defines the essence and content of the complex use of universal frame masks of industrial production, which are intended for the installation of camouflage containers for military equipment and the covering of objects using standard camouflage kits (masking nets), including with the addition of protective anti- drone coatings.
 The purpose of the article is to reveal the features of the basic principles of using universal frame masks and protective anti-drone coating for mechanical protection against enemy kamikaze drone strikes, taking into account the tactical techniques of their application based on the experience of conducting combat operations.
 It is noted that small-sized means of destruction of the drone-"kamikaze" type are capable of causing significant losses to the personnel, weapons and military equipment of the Defense Forces. Therefore, in order to preserve the lives of the personnel of the units of the Defense Forces and to fulfill their tasks, more attention should be paid to anti-drone protection measures. At present, the study of modern approaches to the comprehensive use of universal frame masks, including those equipped with protective anti-drone coatings, is a relevant and necessary aspect for the implementation of the tasks of state protection.
 The article describes that currently domestic manufacturers have developed, patented and tested the industrial design of a universal frame mask on a prototype. This universal frame mask is intended for masking military equipment in trenches, shelters, on technological sites, parking lots, as well as for creating camouflage containers in the absence of natural ones.
 It is substantiated that the complex application of a universal frame mask with a camouflage coating for the installation of camouflage containers in combination with an anti-drone net 2.0/250/6 in 1 “RingGuard” provides a number of advantages in terms of increasing the effectiveness of camouflage measures and protection against enemy “kamikaze” drone strikes.
 It was determined that further scientific research in this field is seen in the study of the effectiveness of countermeasures against drones, namely regarding the detection and neutralization of drones at various stages of their flight to the target, including at the stage of direct attack on the target. Also, a promising direction is the substantiation of proposals for the creation of transportable sets of a universal frame mask with means of anti-drone protection for the regular equipment of the Defense Forces.