Метод реконструкції відеозображень для підвищення ефективності доставки в інфокомунікаційних системах аеросегменту

A mobile video surveillance system is a video surveillance system adopts mobile clients to visualize surveillance videos over mobile networks. However, mobile networks and mobile clients have limited computational and network resources. The system combines moving object detection and video transcoding techniques to help users monitor remote site through video streaming over 3G communication networks. The moving object detection and tracking can skim off useful video clips. The communication networking services, comprising video transcoding, short text messaging, and mobile video streaming, transmit surveillance information into mobile appliances. Moving object detection is achieved by background subtraction of adaptive Gaussian mixture modeling, and particle filter tracking. A spatial-domain cascaded transcoder is developed to convert the filtered image sequence of detected objects into 3GPP video streaming format. Experimental results show that the system can successfully detect all events of moving objects for a complex surveillance scene, and the transcoder has high PSNR.

This work considers the possibilities of protecting weapons and military equipment from barrage ammunition. The main approaches to the modern classification of barrage munitions are revealed, it is noted that in the zone of hostilities of the aggressor country Russia against Ukraine, unmanned aerial vehicles and barrage munitions of various types and modifications are constantly used to destroy weapons and military equipment. It was noted that with the development of modern technologies, it became possible to manufacture unmanned aerial vehicles with smaller dimensions, but with better capabilities for conducting constant reconnaissance, delivering massive surprise strikes using barrage ammunition, which made it much more difficult to fight them with air defense means and forces. In this work, it is noted that scientific and technical progress has not bypassed unmanned aerial vehicles, new information transmission and processing technologies, the appearance of a new element base, their production has become cheaper, and their capabilities in combat use and technical equipment have improved significantly. Modernization and improvement of unmanned impact devices led to an increase in the flight range, that is, an increase in the useful effect of the engine, as well as a decrease in its noise, an increase in the energy consumption of power cells; improvement of navigation and management systems; improvement of the airframe to reduce weight, dimensions, effective scattering area. Means of fire impression were also modernized, guided missiles began to be placed on them, as well as means of radio-electronic warfare, improved technical means of intelligence, improved photo and video surveillance systems were installed. All this led to the improvement of methods of combat use of unmanned aerial vehicles and the growth of their role in modern armed conflicts. An analysis of the capabilities of air defense means to protect weapons and military equipment from unmanned attack vehicles and barrage munitions was carried out, and directions were developed to increase the efficiency of their destruction by various means of fire damage.

Unmanned Aerial Vehicles (UAVs), commonly known as drones, in recent years continue to gain popularity in various fields, ranging from the entertainment sector to the service industry. Their application areas are expanding every day, making them increasingly versatile. They are widely used in the defense industry, contributing to the have a voice of the countries that possess them. Given this context, object detection, tracking, and other customized tasks carried out using imagery obtained from UAVs have become significantly important. However, the images obtained from UAVs are generally low resolution and quality, as they need to be captured from a safe flight distance. This situation is a disadvantage for object detection applications. To reduce this disadvantage, various Super Resolution techniques have been developed. In this paper, the focus is on the critical importance of improvements in this field, especially within the defense sector, by utilizing ESRGAN and YOLO together to enhance the resolution of images captured from UAVs. The primary objective of this study is to enhance the efficacy of object detection by simultaneously augmenting the number of detected objects and improving the accuracy of the detection process. This research presents a comparative analysis of the outcomes achieved through two distinct approaches. Firstly, object detection is executed utilizing a pre-trained YOLO-V7 model on a LR image extracted from the VisDrone Dataset. Subsequently, the same YOLO-V7 model is deployed, but object detection is carried out on the SR version of the same LR image obtained from the ESRGAN network. The findings from this investigation unequivocally demonstrate that conducting object detection on the SR image not only results in a notable increase in the quantity of detected objects but also leads to a significant enhancement in the overall accuracy of the detection process.

The article discusses about the system of information support for unmanned ground vehicle and un-manned aerial vehicles with an increased degree of autonomy, arising application difficulties, is considered. Some methodological approaches to calculating the requirements of electronic protection for the data transmission systems of unmanned ground vehicle and unmanned aerial vehicles with an increased degree of autonomy are given.

The subject of study. A new algorithm for real-time data compression in a video surveillance system from an unmanned aerial vehicle is proposed. The algorithm makes it possible to obtain different degrees of video data compression using different codecs for specific video recording conditions (UAV altitude and flight speed, nature of the observed terrain, etc.), and also provides the ability to transmit video data to the flight control point via radio channel. A detailed analysis of the quality of the proposed algorithm for shooting in various video formats that meet international standards (HD and Full HD) was carried out, both in terms of visual perception and objective criteria. The purpose of the study was to conduct a comprehensive research on the problem of improving the quality of the video surveillance system from the UAV and to create a video compression algorithm that allows broadcasting video data to the control center in real time with high quality. Objectives. To develop and programmatically implement a video compression algorithm based on modern codecs for video surveillance sys­tems on board a UAV. To use modern video cameras and a microcomputer platform as hardware. To analyze the quality indicators of the algorithm in different conditions. Methods used. Evaluation of visual perception of video compression results, statistical analysis and processing of the parameters of the analyzed video sequence as a random spatio-temporal process. Results. A new video data compression algorithm for the UAV video surveillance system based on the Raspberry Pi 5 microcomputer using modern video compression codecs H.264 and H.265 was synthesized. The algorithm provides the required compression ratio and data transmission rate over the radio channel between the UAV and the flight control center. Detailed estimates of the quality of the algorithm are obtained. Conclusions. Scientific novelty of the results: A new universal video data compression algorithm for video surveillance systems on board UAVs with the ability to transmit video over a radio channel in real time has been proposed and implemented. The quality of the proposed algorithm is verified on real video surveillance results. The proposed algorithm is implemented in the Python software environment.

The rapid integration of unmanned aerial vehicles (UAVs) into civil, defense, and emergency response sectors has fundamentally transformed data collection and analysis methodologies in time-critical scenarios. UAVs have become indispensable tools in missions where human access is restricted, dangerous, or logistically challenging. Their capacity to capture high-resolution real-time aerial imagery makes them vital assets in search-and-rescue operations, post-disaster damage assessments, criminal investigations, and surveillance monitoring. This research addresses the fundamental challenge of implementing efficient real-time object detection and recognition systems that operate with minimal human intervention while accommodating the inherent computational limitations of onboard UAV hardware. This study proposes an innovative approach leveraging external computational resources by transmitting UAV video feeds to ground-based processing systems where advanced YOLOv8 neural network models perform sophisticated real-time object detection and classification. The YOLOv8 architecture represents an optimal balance between processing speed and detection accuracy. This methodology significantly enhances recognition capabilities without requiring modifications to existing UAV hardware configurations, ensuring broad compatibility and cost-effectiveness. The system demonstrates remarkable performance metrics: preprocessing time averaging 22.5 ms, inference time of 5.5 ms, and visualization rendering within 3 ms per frame. Multiple specialized datasets were utilized, including Top-View Drone Car Detection Dataset and YOLO HighVis Person Detection Dataset, ensuring robust multi-class object recognition capabilities. Experimental results conclusively confirm the technical and practical feasibility of seamlessly integrating AI-based detection systems into existing UAV monitoring infrastructures, achieving stable operation with accuracy fluctuations not exceeding 1% at 5m, 10% at 50m, and 35% at 100+ meters, thereby significantly enhancing the capabilities and effectiveness of future autonomous aerial surveillance platforms.

Missing data reconstruction in attitude for quadrotor unmanned aerial vehicle based on deep regression model with different sensor failures

The need to develop a method for calculating the secondary radiation characteristics of thin conductors located under the dielectric shell of an unmanned aerial vehicle is due to the growing requirements for the efficiency of detection and identification of hidden objects in modern conditions. Unmanned aerial vehicles used in military and civilian applications are often equipped with thin conductors that serve as antennas, sensors, or structural elements. Their interaction with electromagnetic radiation is complicated by the presence of a dielectric shell, which affects the nature of the secondary radiation, changing its spectral and polarisation characteristics. Accurate analysis of such characteristics is critical for enabling effective remote sensing, development of radar and electronic warfare systems, and optimisation of unmanned aerial vehicles design to meet the requirements for their stealth and functionality. The development of modelling methods that take into account the specific geometry and material properties of conductors and sheaths allows us to obtain more accurate data required to solve applied problems in the scientific, technical and defence sectors. When assessing the secondary radiation of aircraft-type unmanned aerial vehicles, the main source of radiation is usually the internal equipment under a dielectric single or multilayer shell. However, insufficient attention is paid to the thin conductors inside the wing. These conductors can make a significant contribution to the overall scattered field. Due to the fact that the wing of an unmanned aerial vehicle has dimensions that are significantly larger than the wavelength of the irradiating radar stations, it is essential to use high-frequency asymptotic methods to calculate its secondary radiation. The paper proposes to solve the calculation of scattering on electrically thin conductors using the Pocklington equation. The calculations were based on integral representations obtained using Lorentz's lemma and the Pocklington equation for thin-wire antennas. The article develops a method for calculating the scattering characteristics of thin conductors under a dielectric shell (e.g., a wing console of an unmanned aerial vehicle), which takes into account the interaction between the dielectric shell and thin conductors in the said structure. The application of the presented calculation method can contribute to obtaining a priori information about the scattering characteristics of the elements of an unmanned aerial vehicle, which is important for the tasks of detecting and identifying objects, in particular in radar systems.

The article is dealing with the future of the Unmanned Aerial Vehicles (UAVs), shows the current trends and characterizes the major tendency for future operational employment of the UAVs. The UAVs proved their technical feasibility, military applicability and operational value. Focal point of activities of the UAVs, until recently, consisted in performing the tasks aimed to gathering the important information for the planning process support. Reconnaissance UAVs can perform their missions both by day and by night, practically at any weather conditions on the range according to the command level. Combat UAVs, on top of that, are capable perform their missions against ground targets by jamming or to destroy them. Analyses of trends of operational employment of UAVs show to new possibilities for introduction of new types of UAVs (miniaturization) to armed forces structures. These new means will play the decisive role not only in the preparatory phases but also during the crisis situations resolution process. This article is part of the “Operational Concepts of the Armed Forms of the Czech Republic in Joint Operations”, a defence research project that is being solved at the University of Defence in Brno (CZ).

With the continuous development of the marine economy and the inland river transport, traditional vessels supervision methods have the shortcomings of short supervision range, small supervision scope, and high cost, which make it difficult to meet the requirements of modern maritime supervision. This paper proposes a novel maritime emergency search system based on unmanned aerial vehicle (UAV) and its landing platform. The system takes the base station with the cruise UAV, as the base point. Subsequently, TDMA system networking technology and wireless bridge communication technology are used to set up a local area network. Then, the improved three-dimensional raster processing is used to search the target waters. After that, several algorithms such as area optimization based on image filtering are applied to classify, integrate, and fit the waters. In addition, the base station distribution scheme and the emergency equipment intelligent management system software are designed to achieve more efficient and convenient management of the system. The system realizes unmanned, visualized, and normalized monitoring and management of the target waters through the coupling of UAV, aerial protection base stations and control terminals, and provides more detailed and accurate information for the development of search and rescue work.

Nowadays, the digital development of marine ranching requires a communication system with wide coverage, high transmission rate and stable communication links. It is known that fixed‐wing unmanned aerial vehicles (UAVs) have great advantages in long‐range applications. They have the potential to serve as low‐altitude communication platforms for maritime communication. In this study, with a developmental perspective, considering the intense growth of marine terminals in the future, a new clustering algorithm applied to cluster nonorthogonal multiple access (C‐NOMA) is proposed and its advantages are investigated. In addition, considering the limited energy of marine terminals, combining the wireless power communication (WPC) technology for the UAV to charge terminals, the charging and communication time are optimized with the Lagrange multiplier method and the bisection search method. After completing the above optimization content of charging and communication, combined with the optimization results, it is found the trajectory that maximizes the energy efficiency of the UAV with the convex optimization technique. Experimental results show that the proposed clustering algorithm has good throughput performance, better fairness and lower algorithm complexity, and the proposed trajectory optimization scheme has better energy efficiency.

One of the main goals of the new fifth generation of cellular networks (5G) is to provide the programmability, flexibility, and modularity that are required to create multiple logical networks, called network slices, each tailored for a given vertical use case, on top of a common network infrastructure. A particular network slice that is expected to be able to create a plethora of new opportunities and applications, is the Tactile Internet, aiming at guaranteeing e2e delays not greater than 1 ms to its users. Matching this very severe requirement does not only depend on the length of the physical path that information should follow flowing from sensors to actuators, but also on the size of each information. This is the case of a video-surveillance system that uses Unmanned Aerial Vehicles (UAV) to capture high-resolution images with a frame rate of thousands frames per second. Considering this kind of scenarios, this paper proposes a Tactile Internet architecture for video-surveillance systems. An analytical model based on Markov chains is defined to evaluate delays and job loss probability in order to allow designers to dimension system elements to be compliant with the Tactile Internet requirements.

<p>With consumer-grade unmanned aerial vehicles (UAVs) on the rise, which enable easy, time-flexible, and cost-effective acquisition of very high-resolution RGB data, the mapping of forest tree species using solely RGB imagery is of high interest, as it does not rely on sophisticated sensors, does not require extensive calibration and preprocessing and, therefore, enables the application by a wide audience. In combination with convolutional neural networks (CNNs), which particularly exploit spatial patterns and, therefore, highly benfit from very high-resolution remote sensing, this offers great potential for accurately mapping forest tree species.</p><p>Here, we present the findings of our recent study, in which we used very high-resolution RGB imagery from UAVs in combination with CNNs for the mapping of forest tree species. In this study, we used multicopter UAVs to obtain very high-resolution (<2 cm) RGB imagery over 51 ha of temperate forests in the Southern Black Forest region, and the Hainich National Park in Germany. To fully harness the end-to-end learning capabilities of CNNs, we used a semantic segmentation approach (U-net) that concurrently segments and classifies tree species from imagery. With a diverse dataset in terms of study areas, site conditions, illumination properties, and phenology, we accurately mapped nine tree species, three genus-level classes, deadwood, and forest floor (mean F1-score 0.73). We found that a coarser spatial resolution substantially reduced the model accuracy (mean F1-score of 0.26 at 32 cm resolution) and that larger tile sizes during CNN training negatively affected the model accuracies for underrepresented classes. Additional height information from normalized digital surface models slightly increased the model accuracy but simultaneously increased computational complexity and data requirements. Our results highlight the key role that UAVs can play in the mapping of forest tree species, given that air- and spaceborne remote sensing currently does not provide comparable spatial resolutions. Given the end-to-end learning capabilites of CNNs extensive preprocessing becomes partly obsolete, whereas the use of a large and diverse dataset facilitates a high degree of generalization of the CNN, thus fostering transferability. The synergy of high-resolution UAV imagery and CNN provide a fast and flexible yet accurate means of mapping forest tree species.</p><p>In this contribution, we will give an outlook on how the combination of UAV imagery and CNNs can be integrated with multitemporal satellite imagery (Sentinel-1 and Sentinel-2) in order to extrapolate the UAV-based tree species maps to larger areas.</p>

The Electronic Stability Program 32 Spy (ESP32 Drone) is a low-cost, wireless-enabled unmanned aerial vehicle (UAV) designed using the ESP32 microcontroller. The system focuses on achieving real-time flight stabilization through sensor fusion and PID-based control algorithms. By integrating IMU sensors, GPS modules, and wireless communication technologies such as Wi-Fi and Bluetooth, the drone ensures stable flight, remote monitoring, and telemetry transmission. The project aims to provide an affordable alternative to traditional flight controllers while maintaining reliable performance. The implementation includes real-time sensor processing, motor speed control through ESCs, and wireless communication for monitoring and control. This system is suitable for educational, research, and IoT-based drone applications

At present, the research on fault analysis based on text data focuses on fault diagnosis and classification, but it rarely suggests how to use that information to troubleshoot faults reported in unmanned aerial vehicles (UAVs). Selecting the exact troubleshooting procedure to address faults reported by UAVs generally requires experienced technicians with professional equipment. To improve the efficiency of UAV troubleshooting, this paper proposed a troubleshooting mode selection method based on SIF-SVM (Serial information fusion and support vector machine) using the text feature data from fault description records. First, Word2Vec was used in text data feature extraction. Second, in order to increase the amount of information in the modeling data, we used the information fusion method. SVM was then used to construct the classification model for troubleshooting mode selection. Finally, the effectiveness of the proposed model was verified by using the fault record data of a new fixed-wing UAV.