Use Of Unmanned Aerial Systems Research Articles

Toward Virtual Testing of Unmanned Aerial Spraying Systems Operating in Vineyards

In recent times, the objective of reducing the environmental impact of the agricultural industry has led to the mechanization of the sector. One of the consequences of this is the everyday increasing use of Unmanned Aerial Systems (UAS) for different tasks in agriculture, such as spraying operations, mapping, or diagnostics, among others. Aerial spraying presents an inherent problem associated with the drift of small droplets caused by their entrainment in vortical structures such as tip vortices produced at the tip of rotors and wings. This problem is aggravated by other dynamic physical phenomena associated with the actual spray operation, such as liquid sloshing in the tank, GPS inaccuracies, wind gusts, and autopilot corrections, among others. This work focuses on analyzing the impact of nozzle position and liquid sloshing on droplet deposition through numerical modeling. To achieve this, the paper presents a novel six degrees of freedom numerical model of a DJI Matrice 600 equipped with a spray system. The spray is modeled using Lagrangian particles and the liquid sloshing is modeled with an interface-capturing method known as Volume of Fluid (VOF) approach. The model is tested in a spraying operation at a constant velocity of 2 m/s in a virtual vineyard. The maneuver is achieved using a PID controller that drives the angular rates of the rotors. This spraying mission simulator was used to obtain insights into optimal nozzle selection and positioning by quantifying the amount of droplet deposition.

Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars

Precise altitude data are indispensable for flight navigation, particularly during the autonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respectively, whilst global positioning system (GPS) receivers provide the altitude from the mean sea level (MSL) marred with a slow update rate. To cater to the landing safety requirements, UASs necessitate precise altitude information above ground level (AGL) impervious to environmental conditions. Radar altimeters, a mainstay in commercial aviation for at least half a century, realize these requirements through minimum operational performance standards (MOPSs). More recently, the proliferation of 5G technology and interference with the universally allocated band for radar altimeters from 4.2 to 4.4 GHz underscores the necessity to explore novel avenues. Notably, there is no dedicated MOPS tailored for radar altimeters of UASs. To gauge the performance of a radar altimeter offering for UASs, existing MOPSs are the de facto choice. Historically, frequency-modulated continuous wave (FMCW) radars have been extensively used in a broad spectrum of ranging applications including radar altimeters. Modern monolithic millimeter wave (mmWave) automotive radars, albeit designed for automotive applications, also employ FMCW for precise ranging with a cost-effective and compact footprint. Given the technology maturation with excellent size, weight, and power (SWaP) metrics, there is a growing trend in industry and academia to explore their efficacy beyond the realm of the automotive industry. To this end, their feasibility for UAS altimetry remains largely untapped. While the literature on theoretical discourse is prevalent, a specific focus on mmWave radar altimetry is lacking. Moreover, clutter estimation with hardware specifications of a pure look-down mmWave radar is unreported. This article argues the applicability of MOPSs for commercial aviation for adaptation to a UAS use case. The theme of the work is a tutorial based on a simplified mathematical and theoretical discussion on the understanding of performance metrics and inherent intricacies. A systems engineering approach for deriving waveform specifications from operational requirements of a UAS is offered. Lastly, proposed future research directions and insights are included.

UAS-Based Thermal Photogrammetry for Microscale Surface Urban Heat Island Intensity Assessment in Support of Sustainable Urban Development (A Case Study of Lyulin Housing Complex, Sofia City, Bulgaria)

The urban heat island (UHI) and its intensity is one of the phenomena that are of determining importance for the comfort of living in cities and their sustainable development in the face of deepening climate change. The study is objectively difficult due to the large dynamics like land cover and the considerable diversity of land use patterns in urban areas. Most of the frequently used research practice approaches provide information with problematic spatial and temporal resolution, making them difficult to apply for sustainable urban planning purposes. This paper proposes to calculate SUHI intensity as the difference between the temperature of a given point within a city and the average minimum temperature of the land cover class with the lowest surface temperatures within the same urban area. The study presents the results of the application of thermal photogrammetry based on the use of unmanned aerial systems (UAS), combined with geographic information systems (GIS), in the study of surface urban heat island intensity (SUHI), at the local level for the largest housing complex in Bulgaria–Lyulin district of the capital of Sofia city. The studies were carried out during a heat wave in July 2023. A difference of 16.5 °C was found between locations with SUHI occurrence and of the peripheral non-build and natural land cover types within the urbanized area. The information benefits of locally addressed data and their direct applicability are discussed to support decision-making processes in the planning and management of urban areas, including their climate adaptation and sustainable development.

BEYOND GROUND CONTROL POINTS: COST-EFFECTIVE 3D BUILDING RECONSTRUCTION THROUGH GNSS-INTEGRATED PHOTOGRAMMETRY

Abstract. The process of 3D building modeling serves a multitude of practical and strategic purposes across diverse industries. Building a 3D model involves employing a range of techniques and technologies. Among these, the most used methods include 3D laser scanning and photogrammetry, whether applied at close-range or through the use of Unmanned Aerial Systems (UAS). In photogrammetry, ground control points (GCPs) are generally needed to scale and georeference the digital reconstruction process, but it is a timeconsuming practice or sometimes impractical or dangerous. This paper aims to evaluate the efficiency of two integrated devices to perform photogrammetric 3D reconstruction without GCPs. They are both composed by a Sony ZV1 camera coupled with two different RTK/PPK GNSS system: the Emlid Reach RS2 GNSS receiver and the Emlid Reach M2 module with a multi-band GNSS helical antenna. Different sets of images were acquired with the two proposed devices for the lever-arm estimation and to perform the 3D surveying of the Galata monastery historical monument. The accuracy of the process and derived dense point clouds is assessed by comparing them with GCPs and a reference point cloud derived by fusing an UAS and a high-resolution mobile laser scanning point cloud. The ultimate goal is to obtain a 3D building model without the use of GCPs in the process of bundle block adjustment with centimeter accuracy.

Factors Influencing the Cost of Utilizing Unmanned Aerial Systems for Construction Monitoring

Unmanned Aerial Systems (UAS) have emerged as a promising technology for construction monitoring due to their ability to collect data efficiently and provide valuable insights. However, the cost implications associated with the use of UAS in construction monitoring remain a significant concern. This research aims to investigate the factors that influence the cost of utilizing unmanned aerial systems for construction monitoring. By analyzing various cost-related factors such as equipment, training, data processing, and regulatory compliance, this study seeks to provide valuable insights into the financial considerations involved in adopting UAS technology for construction monitoring. The findings of this research can help project stakeholders make informed decisions and optimize cost-efficiency when implementing UAS in construction monitoring practices.

Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images.

Accurate estimation of fractional vegetation cover (FVC) is essential for crop growth monitoring. Currently, satellite remote sensing monitoring remains one of the most effective methods for the estimation of crop FVC. However, due to the significant difference in scale between the coarse resolution of satellite images and the scale of measurable data on the ground, there are significant uncertainties and errors in estimating crop FVC. Here, we adopt a Strategy of Upscaling-Downscaling operations for unmanned aerial systems (UAS) and satellite data collected during 2 growing seasons of winter wheat, respectively, using backpropagation neural networks (BPNN) as support to fully bridge this scale gap using highly accurate the UAS-derived FVC (FVCUAS) to obtain wheat accurate FVC. Through validation with an independent dataset, the BPNN model predicted FVC with an RMSE of 0.059, which is 11.9% to 25.3% lower than commonly used Long Short-Term Memory (LSTM), Random Forest Regression (RFR), and traditional Normalized Difference Vegetation Index-based method (NDVI-based) models. Moreover, all those models achieved improved estimation accuracy with the Strategy of Upscaling-Downscaling, as compared to only upscaling UAS data. Our results demonstrate that: (1) establishing a nonlinear relationship between FVCUAS and satellite data enables accurate estimation of FVC over larger regions, with the strong support of machine learning capabilities. (2) Employing the Strategy of Upscaling-Downscaling is an effective strategy that can improve the accuracy of FVC estimation, in the collaborative use of UAS and satellite data, especially in the boundary area of the wheat field. This has significant implications for accurate FVC estimation for winter wheat, providing a reference for the estimation of other surface parameters and the collaborative application of multisource data.

Detection of landscape features with visible and thermal imaging at the Castle of Puerta Arenas

There are some archaeological sites with hard accessibility which remain unexplored and barely documented. The use of unmanned aerial systems (UAS) alleviates this challenge with aerial observations monitored with distant remote control. In addition to acquiring images in the visible wavelengths, other devices can be coupled on aerial platforms to inspect beyond the remaining structure of an archaeological site. For instance, thermography has proven to be of great help in the detection of buried remains due to observed temperature anomalies. This work explores the Castle of Puerta Arenas fortress to build the first aerial 3D reconstruction of this site by using RGB and thermographic images collected from a UAS. Orthomosaics have been applied to hypothesize about the original shape of the fortress, whereas 3D reconstructions have been rather applied to visualization and analysis. In this regard, the explored remains have been processed as dense point clouds in the visible and long-wave infrared spectrum, with the latter leading to the detection of hypothetical and still unknown towers. The detection of anomalies has been automatized by performing statistical analyses, globally and limited to smaller 3D voxel neighbourhoods. As a result, the studied remains have been documented and observed from an unexplored perspective, helping their conservation and dissemination, as well as suggesting future excavations.

Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites.

Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers' unsafe behaviors and work conditions is considered not only a proactive but also an active method of removing safety and health hazards and preventing potential accidents on construction sites. The integration of sensor technologies and artificial intelligence for computer vision can be used to create a robust management strategy and enhance the analysis of safety and health data needed to generate insights and take action to protect workers on construction sites. This study presents the development and validation of a framework that implements the use of unmanned aerial systems (UASs) and deep learning (DL) for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework was validated using a pilot case study. Digital images of construction safety activities were collected on active construction sites using a UAS, and the performance of two different object detection deep-learning algorithms/models (Faster R-CNN and YOLOv3) for safety hardhat detection were compared. The dataset included 7041 preprocessed and augmented images with a 75/25 training and testing split. From the case study results, Faster R-CNN showed a higher precision of 93.1% than YOLOv3 (89.8%). The findings of this study show the impact and potential benefits of using UASs and DL in computer vision applications for managing safety and health on construction sites.

ПРО КОНЦЕПЦІЮ ОРГАНІЗАЦІЇ БОРОТЬБИ З БЕЗПІЛОТНИМИ ЛІТАЛЬНИМИ АПАРАТАМИ В МІСЦЯХ ЗБЕРІГАННЯ ЗАСОБІВ УРАЖЕННЯ

The history of small-sized, low-speed unmanned aerial vehicles, in contrast to the history of unmanned aerial vehicles, is not counted for half a century, but only for two dozen years. The first publications devoted to small-sized and low-speed unmanned aerial vehicles were published after 2008. The number of publications on the specified subject significantly increased in 2017-2018. It is during this period that military operations using small-sized and low-speed unmanned aerial vehicles become widespread. As an example, we can mention the attacks on the oil refinery in Saudi Arabia in September 2019, the attack on the airport in the city of Jizan in the same Saudi Arabia in July 2020, the attacks on the Russian air base in the Syrian city of Khmeimi in 2019-2020. Almost every day, from the regions of the planet where armed conflict is taking place, there are reports of the destruction of storage places of weapons and means of destruction, which affects the course of armed conflicts. It is significant that unmanned aircraft are increasingly being used to destroy weapons. The article highlights the factors that determine the effectiveness of the use of unmanned aerial systems for the destruction of weapons storage sites, and predicted dynamic changes in these factors. The rapid development of microelectronic technologies, which is currently observed, became the basis for the improvement of unmanned aircraft systems, the expansion of the range of tasks performed by unmanned aircraft, the emergence of new classes of devices and the improvement of the tactics of their use. The rapid development of microelectronics is also considered from the point of view of confirming Moore's law, forecasts for the near future, clarifications of the law for the present time, clarifications of forecasts for the next 15 years. Conceptually, the opinions of the authors regarding the minimization of losses of weapons of destruction are presented, the basis of which are their own observations of the statistics of losses from storage sites of weapons of destruction of the Armed Forces of Ukraine for the period from 2003 to 2018, the causes of losses. Keywords: means of defeat, unmanned aerial vehicle, storage places, logistic security.

Application of unmanned air systems in agriculture

The purpose of this article is to consider the use of unmanned aerial systems (UAS) in agriculture. UAVs have great potential in many agricultural tasks. This article summarizes the current state of drone technology and its applications in agriculture, including plant health monitoring, weed control, spraying, and other agricultural operations.

Assessing Drone Mapping Capabilities and Increased Cognitive Retention Using Interactive Hands-On Natural Resource Instruction

The use of Unmanned Aerial Systems (UAS), also known as drones, is increasing in geospatial science curricula within the United States. Four geospatial science faculty members within the Arthur Temple College of Forestry and Agriculture at Stephen F. Austin State University (SFASU), Texas, focus on applying imagery obtained from drones to map, monitor, and quantify natural resources. To produce society-ready foresters, natural resource managers, and environmental scientists, the geospatial science faculty employ an intensive one-on-one hands-on interactive approach in training future resource management professionals in how to effectively apply drone technology within natural resource endeavors. In particular, recent instruction has focused on training students how to evaluate the amount of overlap and sidelap percentages required within a drone flight to create the optimum orthophoto mosaic. Results indicate that the one-on-one interactive methodology employed by faculty at SFASU produce highly qualified drone pilots capable of providing the drone community with new insights on how to produce accurate orthophoto mosaics in a timely and efficient manner.

The use of UAS-based high throughput phenotyping (HTP) to assess sugarcane yield

A sensor mounted on an unmanned aerial system (UAS) may enable breeding programs’ selection efficiency. The objectives of this study were to investigate the use of UAS with red, green, and blue (RGB) camera to assess sugarcane yield and to investigate the direct and indirect influences of canopy features on cane yield of sugarcane. A trial was conducted from 2019 to 2021 at the Texas A&M AgriLife Research and Extension Center in Weslaco, Texas, and arranged in a complete randomized block design, consisting of 7 genotypes with 4 replications. Seven UAS image acquisitions were performed at the plant cane stage using an RGB sensor. At the first ratoon stage, five flights were conducted using RGB and multispectral sensors. Ground measurements were obtained, including pol, tons of sugar per hectare (TSH), and tons of cane per hectare (TCH). The results showed that the correlation of canopy features with pol at the elongation phase was poor, compared to those acquired at the maturity phase. Likewise, the most significant correlations between canopy features with TSH and TCH were found in the late elongation to maturity phase. In addition, a good agreement of the validation dataset between the observed and predicted pol (R2 0.58, RMSE 0.77%), TSH (R2 0.68, RMSE 2.24 Mg ha−1), and TCH (R2 0.66, RMSE 13.64 Mg ha−1) was observed. The canopy height model (CHM) and Normalized Difference Vegetation Index (NDVI) had the highest direct effect on TCH. Therefore, assessing sugarcane yield using UAS-based imagery looks promising for High Throughput Phenotyping (HTP) in sugarcane breeding programs.

AED delivery at night – Can drones do the Job? A feasibility study of unmanned aerial systems to transport automated external defibrillators during night-time

BackgroundIn their recent guidelines the European Resuscitation Council have recommended the use of Unmanned Aerial systems (UAS) to overcome the notorious shortage of AED. Exploiting the full potential of airborne AED delivery would mandate 24 h UAS operability. However, current systems have not been evaluated for nighttime use. The primary goal of our study was to evaluate the feasibility of night-time AED delivery by UAS.The secondary goal was to obtain and compare operational and safety data of night versus day missions. MethodsWe scheduled two (one day, one night) flights each to ten different locations to assess the feasibility of AED delivery by UAS during night-time. We also compared operational data (mission timings) and safety data (incidence of critical events) of night versus day missions. ResultsAll missions were completed without safety incident. The flights were performed automatically without pilot interventions, apart from manually choosing the landing site and correcting the descent. Flight distances ranged from 910 m to 6.960 m, corresponding mission times from alert to AED release between 3:48 min and 11:20 min. Night missions (T¯m:night = 7:26 ± 2:29 min) did not take longer than day missions (T¯m:day = 7:59 ± 2:27 min). Despite slightly inferior visibility of the target site, night landings (T¯land:night = 64 ± 15 sec) were on average marginally quicker than day landings (T¯land:day = 69 ± 11sec). ConclusionsOur results demonstrate the feasibility of UAS supported AED delivery during nighttime. Operational and safety data indicate no major differences between day- and night-time use. Future research should focus on integration of drone technology into the chain of survival.

Use of Unmanned Aerial System (UAS) Phenotyping to Predict Pod and Seed Yield in Organic Peanuts

Peanut (Arachis hypogaea L.) is a highly nutritious food that is an excellent source of protein and is associated with increased coronary health, lower risk of type-2 diabetes, lower risk of breast cancer and a healthy profile of inflammatory biomarkers. The domestic demand for organic peanuts has significantly increased, requiring new breeding efforts to develop peanut varieties adapted to the organic farming system. The use of unmanned aerial system (UAS) has gained scientific attention because of the ability to generate high-throughput phenotypic data. However, it has not been fully investigated for phenotyping agronomic traits of organic peanuts. Peanuts are beneficial for cardio system protection and are widely used. Within the U.S., peanuts are grown in 11 states on roughly 600,000 hectares and averaging 4500 kg/ha. This study’s objective was to test the accuracy of UAS data in the phenotyping pod and seed yield of organic peanuts. UAS data was collected from a field plot with 20 Spanish peanut breeding lines on July 07, 2021 and September 27, 2021. The study was a randomized complete block design (RCBD) with 3 blocks. Twenty-five vegetation indices (VIs) were calculated. The analysis of variance showed significant genotypic effects on all 25 vegetation indices for both flights (p r = 0.44) and seed yield (r = 0.64). These results can be used to further advance organic peanut breeding efforts with high-throughput data collection.

Use of unmanned aerial systems for measuring particulate matter in the lower atmosphere in urban areas

Air quality monitoring traditionally relies on ground stations and satellites, which may face limitations in capturing real-time data near pollution sources due to site complexity or physical barriers. To overcome these challenges, unmanned aerial vehicles (UAVs) equipped with air quality sensors offer new opportunities for comprehensive and rapid data collection in urban and industrial areas. In this study, we propose a novel approach by utilizing a UAV-based system to investigate the spatial distribution of particulate matter in the atmosphere. The monitoring site is strategically located at an intersection in Zagreb, known for its medium to high traffic density and its suitability for capturing the impact of vehicular emissions on air quality. It is essential to note that this assumption based on the observed conditions is not a precisely measured figure but rather an estimate based on observations and the context of the road. Traffic density may vary throughout the day, week, or year, depending on working hours, events, seasonal factors, and other influences affecting vehicular movement. The UAV is equipped with lightweight and low-cost sensors specifically designed for measuring airborne particles, ensuring accurate and reliable measurements. These sensors, integrated onto the UAV and placed on the upper side, minimize interference from propeller-generated airflow and turbulence, providing representative assessments of air quality conditions. By collecting precise data on the concentration of airborne particles, including PM1, PM2.5, and PM10, the study aims to gain insights into the spatial distribution of pollutants near the intersection and understand the potential impacts of traffic emissions on the surrounding environment. Additionally, the UAV's sensors can connect to smartphones or computers, enabling real-time data viewing and analysis, facilitating prompt adjustments and informed decision-making based on the current air quality conditions. The findings challenge the initial assumption as the concentration of particles does not decrease with increasing altitude, requiring further research to understand this phenomenon. Furthermore, future research will consider integrating meteorological data, such as temperature, humidity, air pressure, wind direction, and speed, from a nearby weather station. By analyzing the interplay between meteorological parameters and particle concentration, a comprehensive understanding of airborne particle dynamics can be achieved. This approach will contribute to the development of effective strategies for managing air quality, considering the complex interdependencies and factors influencing particle distribution in the air.

Diurnal Soybean Water Stress Computed Using Statistical-Based Thermal Indices With High-Frequency Unmanned Aircraft Flights

Highlights High-frequency UAS thermal data can identify the temporal nature of the spatial canopy stress patterns for soybean. Thermal indices were calculated using the statistical approach from the lower and upper bounds of confidence interval. The CWSI Histogram Approach (UAS) was compared to the CWSI Empirical Approach (IRT). The distribution of canopy temperature (using the inter-quartile range) may be useful for irrigation management. Abstract. The use of unmanned aerial systems (UAS) in the field of irrigation management has been increasing rapidly. Due to their ability to capture multi-temporal data over the field, new techniques for the calculation of the crop water stress index (CWSI) and degrees above non-stressed (DANS) using UAS have been evolving. In this study, a statistical CWSI approach (canopy temperature histogram method) was used to identify the diurnal crop water stress patterns in soybean crop at three different study sites in Nebraska. Two study sites were located in the Eastern Nebraska Research and Extension Center (ENREC) at Mead, Nebraska, having multiple irrigation treatments; the third site was located in the South Central Agricultural Laboratory (SCAL), Clay Center, Nebraska, having one uniform irrigation treatment. Based on the results obtained, the CWSI and DANS maps exhibited a clear diurnal pattern of crop water stress response from morning to afternoon, and recovery from late afternoon to evening, with variations between the treatments at ENREC and a similar trend on SCAL. ENREC had a stronger correlation between CWSI and DANS due to the wider range in canopy temperatures from having both irrigated and rainfed plots. When compared between deficit plots at ENREC and the irrigated treatment at SCAL, the study showed that the statistical approach was more reliable when there were differences in crop water stress among different treatments. The main advantage of using the statistical CWSI histogram approach compared to the conventional empirical CWSI approach is the reduced requirement of additional meteorological parameters and faster automation time. CWSI histogram distribution graphs were created for each flight to understand the temporal changes and reveal the mean CWSI values (approximately 0.49, 0.51, and 0.49, for ENREC1, ENREC2, and SCAL, respectively) and interquartile (IQR) range for the soybean crop. For a given field site, temporal changes in IQR were greater than temporal changes in mean CWSI. Besides the mean canopy temperature, the distribution of canopy temperature (using the IQR) may be useful for irrigation management. Keywords: Irrigation Management, Precision Agriculture, Python, Remote Sensing, Thermal Imagery, Unmanned Aircraft Systems.

KEY ISSUES RELATED TO THE USE OF UNMANNED AERIAL SYSTEMS

An important feature that characterizes modern armed conflicts is the large-scale use of aviation. Combat activities of the air force became an integral part of fights, battles and operations conducted in various conditions and in many cases determined their development and final result. Currently, in the center of the media discourse, including political and military, there are e.g., issues regarding the use of Unmanned Aerial Systems (UAV) during an armed conflict. The use of military UAVs in operations in Iraq, Libya and Ukraine confirmed their high usefulness in operations (conflicts) of various intensity. Drawing on practical examples, the article discusses key problems related to the use and integration of UAV. This can be a starting point for a discussion on the challenges associated with their use.
 The study used the document research method as well as quantitative and qualitative analysis. A review of the scientific literature on the complex problem of the use of UAVs in the conditions of armed conflict was made. Publicly available information provided by interested institutions was used.

АЛГОРИТМИ ОЦІНЮВАННЯ ЧАСУ СЕНСОМОТОРНИХ РЕАКЦІЙ ОПЕРАТОРІВ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ

The rapid development of science and technology is leading to a significant expansion of the application areas of unmanned aerial systems for various purposes. The key to their effective use of unmanned aerial systems is the quality training of operators, an important element of which is the professional psychological selection of candidates, in particular, the assessment of their sensorimotor reactions. This can be ensured by selecting and justifying appropriate assessment algorithms. The article proposes algorithms that make it possible to estimate the time of sensorimotor reactions of an operator of unmanned aerial systems of the first class based on the accumulation of statistical material and its mathematical processing based on the results of a full-scale experiment. This makes it possible to evaluate the numerical characteristics of the distribution of the duration of the average reaction time in three modes: skill development, under conditions of interference, and under conditions of skill reconstruction, and ultimately to obtain a generalized assessment. By analyzing random continuous values that acquire a value over a certain interval, it was possible to establish standards against which the obtained indicators of sensorimotor reaction time of the operator of unmanned aerial systems of the first class are compared and a conclusion is made about their suitability for training. The results of testing of the proposed algorithms were carried out, statistical series for three evaluation modes were obtained.For a visual representation of the series, the corresponding histograms of the distribution of the duration of the average reaction time were constructed. In order to eliminate the representativeness error, the statistical series were aligned by selecting a theoretical distribution curve for each, which reflects only the essential features of the statistical material. For this purpose, the distribution histogram was approximated by a fourth-degree polynomial. The interval of the theoretical density of the distribution, falling within which the sensorimotor reaction of an arbitrary person is considered normal, is established. For a given probability of such an event, the reliability of such an event is 0.95. To verify the effectiveness of the proposed methodology, an algorithm for estimating the time of sensorimotor reaction of the operator of unmanned aerial systems of class 1 in three modes was synthesized and the corresponding software implementing the developed algorithm was developed. Keywords: unmanned aircraft complexes; sensorimotor reaction; mean reaction time.

АЛГОРИТМ ФОРМУВАННЯ НАБОРУ АПРІОРНИХ ДАНИХ НЕЙРОННОЇ МЕРЕЖІ ДЛЯ ОБРОБКИ ЦИФРОВИХ АЕРОЗНІМКІВ

Analysis of the use of unmanned aerial systems in the combat zone indicates a constant increase in the requirements for intelligence data. One of the ways to increase the effectiveness of UaV application is the use of artificial intelligence methods. The basis for building a reliable neural network model is a large amount of various data, which helps to more accurately summarize information about the given task. The article considers an approach for increasing the accuracy of object image classification by convolutional neural networks based on data augmentation, which differs from existing ones in its adaptation to shooting factors and the specificity of aerial reconnaissance objects. An important and most time-consuming step in building an accurate machine learning model is finding and annotating the data that will be used to train and test the accuracy of the neural network. The accuracy and stability of the network in real conditions depends on the amount of collected data. To date, such methods of object image augmentation as geometric transformations, color correction, and spatial image filtering have been well described and analyzed. The possibilities of optimal combinations of data augmentation methods to achieve the desired generalization of poorly visible invariant features of objects remain unexplored at the moment. Taking into account the conducted research, an approach was proposed for the formation of a set of a priori data of a neural network for object recognition on digital aerial photographs, which will significantly reduce the complexity of the process of collecting the necessary data and replace it with magnification methods that are much simpler, consume less computing resources, and increase the accuracy of work convolutional neural networks, simulation of the use of the proposed approach was carried out. Keywords: unmanned aerial vehicle; convolutional neural network; data augmentation; automated processing of digital aerial photographs.

Unmanned aerial vehicle as a forensic technical tool and object of forensic research

use of unmanned aerial vehicles in the detection, investigation, and prevention of criminal offences. The purpose of this study was to investigate topical issues of using UAVs in two aspects – as a technical forensic tool and as a tool and means of committing a criminal offence. The research methodology included a set of general scientific and special methods that allow assuming and concluding on the specific features of using a UAV in the detection, investigation, and prevention of criminal offences. Thus, this study was based on a diagnostic method for cognition of social and legal phenomena and concepts in their development and interdependence. Along with this, general and special research methods were used, namely comparative legal, system-structural, statistical, logical, and other modern methods of scientific cognition. The theoretical framework of this study included studies of scientists and practitioners in the field of criminal procedure and forensics. The regulatory framework of the study included the norms and provisions of current regulations and their practical implementation in the law enforcement sphere. The study examined topical issues related to the UAV as a modern technical forensic tool and object of forensic research. The paper considered certain aspects and features of the use of UAVs in the detection, investigation, and prevention of criminal offences. To this end, various aspects of the use of UAVs as modern technical forensic tools were comprehensively analysed, distinguishing theoretical foundations of application, statutory regulation, organizational, technological, and scientific-methodological support. The features of using UAVs as a tool and means of committing criminal offences were determined. The study investigated typical traces left as a result of using UAVs, features of their logging and seizure. Considering investigative situations, the sequence, and specifics of conducting an inspection and seizure of a UAV and its elements at the initial stage of the investigation were determined, as well as an indicative list of issues for their expert investigation was provided. The significance of the results and practical value of this paper is that it highlights the specific features of using a UAV as a technical forensic tool and object of forensic research, formulates scientific and methodological recommendations for the use of unmanned aerial systems in the investigation, solving, and prevention of criminal offences. The study also defines the areas for improving Ukrainian legislation to regulate legal relations in the field of criminal justice on the use of unmanned aerial systems by law enforcement agencies