МЕТОД РОЗПІЗНАВАННЯ ОБ'ЄКТІВ У РЕАЛЬНОМУ ЧАСІ ДЛЯ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ У ЗАВДАННЯХ РОЗСЛІДУВАНЬ І ПОШУКОВИХ

Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object's class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system's accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.

This study aims to consider the improvement of technical inspection methods in the context of military conflict. Taking into account the factor of human safety in the survey process, the search for optimal methods and technologies for data collection and analysis is carried out, in particular, the use of modern technologies such as unmanned aerial vehicles (UAVs) and geographic information systems (GIS) is considered. The use of drones allows for quick and efficient surveys of hard-to-reach areas and the collection of high-quality damage data. The integration of UAVs with GIS provides a systematic view of large-scale destruction problems and helps to make informed decisions on how to address the consequences of armed aggression. Despite the advantages of using these technologies, their limitations should be taken into account. The combination of different survey methods allows to obtain the most complete and accurate information about the condition of buildings and structures, which is key in making decisions on the restoration and reconstruction of the affected areas. The study examined the practical use of these technologies within the framework of the Rebuild ua project, which digitized the main destructions in Chernihiv, Sumy, and Kyiv regions. The use of unmanned aerial vehicles in combination with geographic information technologies allows to systematize, analyze, obtain visual information and images with geometric data, which simplifies the process of damage assessment and decision-making during the survey procedure. GIS technologies are also capable of analyzing geographic data, visualizing damage on a map, locating damaged objects and assessing their damage. The integration of GIS technologies and unmanned aerial vehicles with other systems makes it possible to simplify decision-making on the priorities of liquidation and recovery.

: With the recent draw down of the US military after the end of the Cold War, the US Department of Defense (DoD) is placing considerable emphasis on employing a lighter, leaner, and more lethal military force to accomplish the strategic objectives mandated by political leadership. As a force enabler for military forces, Unmanned Aerial Vehicles (UAVs) recently demonstrated their potential during NATO military operations in Kosovo. Their activities were limited to merely gathering enemy intelligence. As a result of advancements in UAVs capabilities, UAVs expanded their operations by directly assisting in combat missions. The resulting logical question that evolved out of the Kosovo operations is, can UAVs be more actively integrated into other military functions to enhance mission accomplishment? More specifically, can UAVs be incorporated into the function of gaining and maintaining control of the air for US forces? To completely answer this question, this research paper will first analyze the current background of UAVs as seen in recent military operations. Secondly, the function of counterair will be examined to identify the deficiencies US forces have in obtaining control of the air. Third, the present and future capabilities that UAVs can bring to the fight will be identified. Fourth, a detailed examination of which UAVs payloads can be incorporated into the function of counterair will be accomplished. Fifth, this paper will show that UAVs can be effectively integrated to enhance US military weapons systems accomplishment of the function of counterair. Finally, this author will briefly discuss one possible plan to integrate UAVs in the function of counterair to overcome the noted deficiencies.

Integration of Cognitive Radio Technology with unmanned aerial vehicles: Issues, opportunities, and future research challenges

In the field of autonomous transportation systems, the integration of Unmanned Aerial Vehicles (UAVs) in emergency response scenarios is important for enhancing the operational efficiency and the victims’ positioning. This article presents a novel Positioning, Navigation, and Timing (PNT) framework, named HEROES , which leverages the UAV and integrated sensing and communication technologies to address the challenges in post-disaster environments. Our approach focuses on a comprehensive post-disaster scenario involving multiple victims, first responders, UAVs, and an emergency control center. HEROES enables UAVs to function as anchor nodes and facilitate the precise positioning of the victims while simultaneously collecting critical data from the disaster area. We further introduce a reinforcement learning model based on the Optimistic Q-learning with Upper Confidence Bound algorithm, enabling the victims and first responders to autonomously select the most advantageous UAV connections based on their channel gain, shadowing probability, and positional characteristics. Furthermore, HEROES is based on a satisfaction game-theoretic model to enhance the sensing, communication, and positioning functionalities. Our analysis reveals the existence of various satisfaction equilibria, including minimum efficient satisfaction equilibrium, ensuring that the UAVs meet their quality of service constraints at minimal operational costs. Extensive experimental results validate the scalability and performance of HEROES, demonstrating significant improvements over existing state-of-the-art methods in delivering PNT services during humanitarian emergencies.

This article provides a comprehensive review of the legal frameworks governing Unmanned Aerial Vehicles (UAVs), highlighting the intricate balance between fostering innovation and addressing the multifarious challenges posed by UAV integration into civilian airspace. It delves into the international legal context, analyzing the efforts of regulatory bodies like the International Civil Aviation Organization (ICAO) to harmonize UAV regulations across different jurisdictions. A detailed exploration of national regulatory frameworks in countries such as the United States, the European Union, China, and India showcases the diversity of legal approaches and the common challenges they face, including safety, privacy, and airspace management. The article further scrutinizes regulatory challenges, emphasizing the rapid pace of technological advancements in UAVs and the implications for safety, security, and privacy. Additionally, it explores the legal implications of commercial and civil UAV use, focusing on licensing, certification, and sector-specific considerations. By examining case studies and emerging technologies, the article underscores the need for adaptive, forward-looking legal frameworks that can accommodate the evolving landscape of UAV technology while ensuring public safety and privacy. This comprehensive analysis aims to contribute to the ongoing dialogue among policymakers, industry stakeholders, and legal scholars about developing coherent, effective legal structures for UAVs in an era of rapid technological change.

Uneven-aged mixed forests have been recognized as important contributors to biodiversity conservation, ecological stability, carbon sequestration, the provisioning of ecosystem services, and sustainable timber production. Recently, numerous studies have demonstrated the applicability of integrating remote sensing datasets with machine learning for forest management purposes, such as forest type classification and the identification of individual trees. However, studies focusing on the integration of unmanned aerial vehicle (UAV) datasets with machine learning for mapping of tree species groups in uneven-aged mixed forests remain limited. Thus, this study explored the feasibility of integrating UAV imagery with semantic segmentation-based machine learning classification algorithms to describe conifer and broadleaf species canopies in uneven-aged mixed forests. The study was conducted in two sub-compartments of the University of Tokyo Hokkaido Forest in northern Japan. We analyzed UAV images using the semantic-segmentation based U-Net and random forest (RF) classification models. The results indicate that the integration of UAV imagery with the U-Net model generated reliable conifer and broadleaf canopy cover classification maps in both sub-compartments, while the RF model often failed to distinguish conifer crowns. Moreover, our findings demonstrate the potential of this method to detect dominant tree species groups in uneven-aged mixed forests.

The development of an unmanned aircraft system (UAS) traffic management (UTM) system for the safe integration of unmanned aerial vehicles (UAVs) requires pre-flight conflict detection and resolution (CDR) methods to provide collision-free flight paths for all UAVs before takeoff. A popular solution consists in adapting multi-agent path finding (MAPF) techniques. However, standard MAPF solvers consider only a fixed takeoff time and fixed uniform speed for each UAV flight path, which can lead to inefficiencies in the resolution of instances. Therefore, in this article, we propose incorporating scheduling elements into MAPF solvers, which allows us to adjust the takeoff times and speeds of each UAV to solve conflicts. We introduce two time-related resolution techniques: 1) takeoff scheduling, whereby the start time of a UAV agent is delayed, and 2) speed adjustment, wherein the speed of a UAV agent is decreased over a segment on its flight path. Importantly, we present a distinction of conflict types, which enables us to combine replanning resolution to the aforementioned temporal resolution techniques. We evaluate our proposed approaches on a realistic, high-density UAV delivery scenario in Tokyo, Japan. We show that the combination of takeoff scheduling, replanning, and speed-adjustment resolution techniques improves the efficiency of route planning by reducing the average delay per flight path and the number of rejected flight paths.

The proliferation of unmanned aerial vehicles (UAVs) has driven the emergence of urban air mobility (UAM) as a solution to alleviate the increasing demand for UAVs applications in various domains. The concept of UAM envisions a future where UAVs play a pivotal role in short-distance transportation, delivery services, surveillance, emergency rescue, and more. However, the rapid growth of UAVs poses significant challenges arising from the need to ensure the safe coexistence of UAVs with other manned/unmanned aerial vehicles. To ensure the safe integration of UAVs into low-altitude urban airspace and support high-density on-demand mobility, this research focuses on handling the safety-centric challenges related to UAV collision risk assessment, obstacle avoidance, and conflict resolution in complex urban environments.
\nOne of the main challenges is the intrusion of UAVs into airport runways. Airports serve as critical hubs for civil aircraft takeoff and landing operations. However, the increasing presence of UAVs has raised concerns about intruding into airports, leading to flight delays and temporary runway closures. To quantify the potential interference of UAVs on civil aircraft, a collision-course based trajectory planning model is proposed to evaluate the collision risk between manned civil aircraft and intruding UAVs. In this risk assessment model, the trajectory of the intruding UAV is formulated using a collision-course concept, taking into account the limited trajectory information available about intruding UAVs. The planned trajectory of the UAVs is then used to predict its relative position concerning civil aircraft. To account for the possible disturbances of the relative distance, a stochastic position prediction model based on Brownian motion is developed. The collision risk is assessed based on this stochastic kinematic model, providing a quantitative measure of the potential collisions. The proposed model is validated through extensive simulations involving different UAV initial positions, position updates, and different sizes of collision zone tests, as well as comparative tests with the Monte-Carlo algorithm, demonstrating its robustness in assessing the collision risk of the airport restricted area. To facilitate the application, the model is finally used to develop a platform to generate runway heatmaps, accounting for airport runway risk distributions. 
\nAnother safety-centric challenge is ensuring autonomous obstacle avoidance and conflict resolution of UAVs. To enable UAV autonomous safe operations in uncontrolled urban low-altitude airspace, a comprehensive collision-free path planning framework is developed to address the avoidance of both static obstacles and dynamic threats. Firstly, to handle static obstacles, a novel algorithm of 3D voxel jump point research is introduced to generate a global reference path for UAV navigation to avoid static obstacles, particularly for a free flight at different altitudes. Additionally, to optimize the reference path for specific mission requirements, techniques such as trajectory de-diagonalization, reconstruction, and smoothness are proposed to reduce the risk of static collisions, shorten the fly range, and enable UAVs to negotiate turns more efficiently. Secondly, real-time conflict resolution actions based on the Markov decision process are developed to avoid local dynamic threats encountered along the optimized desired path. This allows the host UAV to dynamically adjust its course to a resolution trajectory while maintaining its planned speed without hovering to avoid the potential collision with the approaching intruders. By avoiding near mid-air collisions (NMAC) and preventing the UAV from hovering, the proposed method ensures the timely completion of UAV missions. Simulation results demonstrate that the developed method successfully achieves UAV autonomous global path planning and local real-time conflict resolution in complex urban low-altitude environments, thereby avoiding both static obstacles and dynamic threats. 
\nMoreover, to bridge the research gap of UAV hard conflict resolution in mitigating NMAC risks arising from local dynamic threats, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. To improve UAV operating environments in urban airspace, a concept of tiered aerial passages is first designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity and optimizes the utilization of urban airspace. For UAV tiered aerial passage operations, a situational NMAC risk assessment module coupled with a conflict resolution decision-making module is formulated, enabling the host UAV to evaluate and respond to real-time conflicts, ensuring avoidance of potential collisions. To assess NMAC risks, a general risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the overtaking, approaching head-on, and converging UAV encounters are analyzed, and situational risk potential scores are created using the Gaussian kernel function in the CRF algorithm, considering advisory, caution, and warning risk alert levels. To resolve conflict, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed scheme is validated by simulating geometric encounter situations and comparative tests with the previous hard resolution technique. The results indicate that the proposed method outperforms UAV NMAC risk assessment and conflict resolution. Even in the most dangerous unacceptable resolution decision-making scenarios across three conflict encounters, the proposed algorithm achieves NMAC rates of 0.036, 0.070, and 0.043, which are lower than the corresponding NMAC rates associated with the hard resolution approach. Together with the acceptable and tolerable scenarios, the proposed negotiation resolution effectively tailors different risk requirements to accommodate diverse UAV missions. 
\nWith a primary focus on ensuring the safety of the UAM architecture, this research endeavors to assess UAV NMAC risks and efficiently manage potential conflicts. The proposed work are thoroughly validated to demonstrate their effectiveness in real-world scenarios. As a result, this study significantly contributes to the establishment of a robust UAM architecture, facilitating the realization of safe and reliable UAM.

The integration of unmanned aerial vehicles (UAVs) with wireless communication systems greatly enhances the application area of intelligent transportation systems (ITS). One of the mission-critical applications enabled by these systems is disaster management, where communication functionality can provide users with supplementary information such as escape routes, time to rescue, etc. In this paper, considering a large area with several locations of interest, we formulate and solve the optimization problem of delivering critical information by optimizing the UAV speed and the order of visits to the locations of interest so that the mission time is minimized. The formulated problem is a mixed integer nonlinear program, which is quite challenging to solve. To reduce the complexity of solution algorithms, we propose two circular trajectory designs. The first algorithm finds the optimal velocity and radius of the circular trajectories of the UAV. The second algorithm finds the optimal connections to join the individual circular trajectories. An important practical benefit of using circular trajectory design is that it can be implemented through either fixed-wing or rotary-wing UAVs. Our numerical results reveal that, with practical simulation parameters, the proposed single circular trajectory design provides a time saving of at least 30% as compared to existing zigzag trajectory design. Moreover, the proposed multiple circular trajectory design cuts the total mission completion time by at least 7 times as compared to proposed single circular trajectory design.

ABSTRACTThe integration of unmanned aerial vehicles (UAVs) into cellular networks has emerged as a promising solution to enhance connectivity and service quality in both urban and remote areas. In this paper, we propose a comprehensive framework that combines multi‐agent deep learning with backhaul traffic optimization to effectively manage resources in UAV‐enabled communication networks. By leveraging the capabilities of intelligent reflecting surfaces (IRS) and cell‐free communication strategies, our approach aims to optimize backhaul traffic, ensuring seamless data transmission and improved network throughput. Our methodology involves a dynamic resource allocation mechanism that utilizes multi‐agent deep learning to accurately predict network demands and adaptively allocate resources. The process begins with the collection of real‐time network data, including user demand, traffic patterns, and UAV positions. This data is then fed into a deep learning model, where multiple agents collaboratively analyze and predict future network requirements. Based on the predictions, the resource allocation mechanism dynamically adjusts the distribution of resources, such as bandwidth and power, to meet the anticipated demand. This adaptive strategy enables the network to efficiently handle varying traffic loads, reducing congestion and latency. Furthermore, our backhaul traffic optimization technique focuses on minimizing the energy consumption of UAVs while maximizing their coverage and connectivity. By optimizing the flight paths and altitudes of UAVs, we ensure that they provide optimal coverage with minimal energy expenditure. Additionally, the IRS‐assisted communication further enhances signal quality, reducing the need for high‐power transmissions and thus conserving energy. Our simulations show that our framework improves network throughput, energy efficiency, and reliability. It offers a promising way to manage resources in future UAV‐enabled communication networks.

The improvement of energy efficiency and spectral efficiency in networks is achieved by seamlessly integrating energy harvesting, cognitive radio technologies, and Non-Orthogonal Multiple Access (NOMA) techniques. These complementary strategies optimize resource usage and address challenges related to energy consumption. Additionally, the adaptability and versatility of Unmanned Aerial Vehicles (UAVs) offer innovative solutions for enhancing coverage performance, thereby improving connectivity, efficiency, and reliability. We introduce a novel approach called the Deep Reinforcement Learning-Random Walrus (DRL-RW) algorithm, which combines Deep Reinforcement Learning (DRL) with the Random Walrus optimization (RWO) technique for efficient spectrum resource allocation and energy harvesting management in dynamic environments. The DRL-RW algorithm enables UAVs to learn optimal spectrum-sharing strategies and energy harvesting policies, while the RWO enhances the algorithm's adaptability and speed in exploring diverse solutions. Simulation results demonstrate the effectiveness of the DRL-RW algorithm, showing significant improvements in several performance metrics, including reduced energy consumption, enhanced computation time, improved convergence, increased signal-to-noise ratio, higher throughput, extended network lifetime, and increased harvested energy. These findings underscore the efficacy of the DRL-RW approach in addressing challenges associated with energy management in cognitive radio networks. The integration of UAVs, NOMA networks, and this novel algorithm represents a promising direction for developing energy-efficient communication systems.

This paper introduces a virtual reality (VR)-based unmanned aerial vehicle (UAV) simulator designed to support immersive training and early-stage human factors evaluations in rescue and emergency mission contexts. The system simulates a leader-follower UAV configuration, where a human operator controls a lead drone through a VR headset and joystick, while autonomous drones maintain any geometric formations using onboard sensing and dynamic obstacle avoidance [1]. This swarm-based coordination reflects real-world search and rescue scenarios, where rapid decision-making, spatial awareness, and teamwork are essential.The simulator is developed with a focus on human-in-the-loop design, providing an immersive teleoperation experience that places the user in high-pressure environments with dynamic spatial constraints. Visual and auditory feedback, along with the ability to switch between multiple drone perspectives, is intended to support situational awareness, mission control, and error recovery in complex terrain.From a human factors perspective, the simulator serves as a flexible testbed for evaluating cognitive and ergonomic variables in safety-critical tasks. It allows the assessment of operator workload, interface usability, attention allocation, and situation awareness under realistic but controlled conditions. Subjective evaluation tools such as the NASA Task Load Index (NASA-TLX) and the Situation Awareness Rating Technique (SART) [2] can be embedded directly within the VR experience. At the same time, objective data from head and hand movement, control inputs, and UAV performance are recorded to gain insight into operator behavior.The simulator integrates high-fidelity UAV dynamics using closed-loop reference model adaptive controllers [3], and can be equipped with tools such as eye tracking, physiological sensors for workload estimation, and visual attention assessment methods like ATTENDO [4]. These additions support deeper analysis of how operators acquire information, prioritize tasks, and shift attention during mission-critical events.Overall, the VR-based UAV simulator addresses a growing need to evaluate and enhance human performance in multi-agent control systems used in emergency operations. It offers a scalable, immersive environment that supports both training and cognitive engineering. Its modular structure allows for future integration of real UAVs, digital mapping capabilities, and adaptive interfaces, making it a valuable platform for advancing human-centered UAV system design.

Real-time object detection is critical for unmanned aerial vehicles (UAVs) performing various tasks. However, efficiently deploying detection models on UAV platforms with limited storage and computational resources remains a significant challenge. To address this issue, we propose ELNet, an efficient and lightweight object detection model based on YOLOv12n. First, based on an analysis of UAV image characteristics, we strategically remove two A2C2f modules from YOLOv12n and adjust the size and number of detection heads. Second, we propose a novel lightweight detection head, EPGHead, to alleviate the computational burden introduced by adding the large-scale detection head. In addition, since YOLOv12n employs standard convolution for downsampling, which is inefficient for extracting UAV image features, we design a novel downsampling module, EDown, to further reduce model size and enable more efficient feature extraction. Finally, to improve detection in UAV imagery with dense, small, and scale-varying objects, we propose DIMB-C3k2, an enhanced module built upon C3k2, which boosts feature extraction under complex conditions. Compared with YOLOv12n, ELNet achieves an 88.5% reduction in parameter count and a 52.3% decrease in FLOPs, while increasing mAP50 by 1.2% on the VisDrone dataset and 0.8% on the HIT-UAV dataset, reaching 94.7% mAP50 on HIT-UAV. Furthermore, the model achieves a frame rate of 682 FPS, highlighting its superior computational efficiency without sacrificing detection accuracy.

Unmanned aerial vehicles (UAVs) often operate under stringent resource constraints while requiring real-time object detection, which can lead to failures in cluttered backgrounds or when targets are small or partially occluded. To address these challenges, we introduce AAB-FusionNet, a real-time detection model specifically designed for UAV edge computing platforms. At its core is the Adaptive Attention Block (AAB), which employs an Adaptive Saliency-based Attention (ASA) mechanism to highlight the most discriminative tokens while a lightweight MBConv sub-layer refines local spatial features. This saliency-driven framework ensures the network remains focused on critical cues despite complex aerial imagery. To further boost performance, AAB-FusionNet utilizes a Multi-layer Feature Fusion Network that integrates three key components: Attentive Inverted Bottleneck Aggregation (AIBA) to restore significant details at multiple scales, DySample for preserving spatial fidelity during feature alignment, and the Dual-Attention Noise Mitigation (DNM) module to suppress environmental noise through complementary channel and spatial attention. Experiments on diverse aerial datasets confirm that AAB-FusionNet achieves robust detection, especially for small or partially occluded objects, while offering real-time inference on low-power hardware. Overall, AAB-FusionNet effectively balances accuracy, computational efficiency, and adaptability, making it ideally suited for UAV scenarios demanding fast, reliable object detection and robust and consistent performance.•Incorporates an Adaptive Saliency-based Attention mechanism to emphasize critical visual cues.•Introduces a Multi-layer Feature Fusion Network for detail restoration, feature alignment, and noise mitigation.•Demonstrates real-time, high-accuracy detection on low-power UAV platforms, particularly for small or occluded targets.