Use Of Unmanned Aerial Vehicles Research Articles

The Use of Unmanned Aerial Vehicles in the 3D Documentation of Historical and Cultural Heritage: The Case of Ceyhan Kurtkulağı Caravanserai

The Use of Unmanned Aerial Vehicles in the 3D Documentation of Historical and Cultural Heritage: The Case of Ceyhan Kurtkulağı Caravanserai

Quantifying intertidal macroalgae stocks in the NW Iberian Peninsula using unmanned aerial vehicle (UAV) multispectral imagery

Intertidal macroalgae play a fundamental role in providing various ecosystem services, and they must be managed effectively for conservation purposes. Using remote sensing techniques to monitor macroalgae is a promising way of optimizing efforts in relation to logistics and personnel. This study aimed to quantify the cover and biomass of the predominant macroalgae in a rocky intertidal zone (of area 5.9 ha) in NW Spain. Drone monitoring with multispectral sensors, in situ photography and biomass sampling were conducted in June and July, 2021 (i.e. in summer, when macroalgae are most abundant in the study area). The macroalgae were classified using Minimum Distance, Spectral Angle Mapping and Maximum Likelihood algorithms, the latter of which proved the most accurate. The fresh weight of macroalgae determined in the quadrat sampling was regressed on the cover of July to enable subsequent estimation of biomass from the classification maps. The area occupied by brown algae (phylum Ochrophyta) in July 2021 was estimated to be 5574 m2 with a biomass of 28.5 t. The remote sensing method, which did not require in-depth knowledge of programming, proved to be a rapid, effective means of estimating macroalgal cover. The method may be useful for managing commercial macroalgal stocks and monitoring protected marine areas.

A Sensor Fusion Approach to Observe Quadrotor Velocity.

The growing use of Unmanned Aerial Vehicles (UAVs) raises the need to improve their autonomous navigation capabilities. Visual odometry allows for dispensing positioning systems, such as GPS, especially on indoor flights. This paper reports an effort toward UAV autonomous navigation by proposing a translational velocity observer based on inertial and visual measurements for a quadrotor. The proposed observer complementarily fuses available measurements from different domains and is synthesized following the Immersion and Invariance observer design technique. A formal Lyapunov-based observer error convergence to zero is provided. The proposed observer algorithm is evaluated using numerical simulations in the Parrot Mambo Minidrone App from Simulink-Matlab.

Comparison of UAV and WorldView-3 Imagery for Mangrove Species Mapping in Mundupesisir Coastal, Cirebon

Abstract One of the efforts for monitoring and managing mangrove areas is mapping the mangrove areas at the species level. This aims to explore the potential and efforts for rehabilitation, as well as an inventory of the mangrove areas biodiversity. The study area was located in the mangrove Kasih Sayang area, Mundupesisir Village, Cirebon Regency, West Java. Remote sensing image data which have high-resolution can be utilized to more efficiently map mangrove species. The approach that can be used is the Geographic Object-Based Imagery Analysis (GEOBIA) method. This research aims to (1) map the mangrove species in Mundupesisir Village, Cirebon using Unmanned Aerial Vehicle (UAV) data and WorldView-3 imagery, and (2) compare the results and accuracy assessment values of mangrove species from UAV data and WorldView-3 imagery. UAV data (0.0179 m) has a visible image (RGB) band, while WorldView-3 is a multispectral image that has 8 bands (2 m) and a panchromatic band (0.5 m). Field data collection was obtained and collected at purposive random sampling to identify different species based on their physical characteristics. The GEOBIA approach used includes segmentation and classification processes. Multiresolution segmentation algorithm was used in the segmentation process. The classification used the Nearest Neighbor algorithm based on segmentation results. The results of the GEOBIA approach successfully mapped three dominant species in the mangrove Kasih Sayang area, Mundupesisir, Cirebon Regency, namely Avicennia marina, Rhizophora mucronata, and Acrostichum aureum.

Approaches to the formation of an image database in the explosive ordnance identification system for further training of a neural network

The use of unmanned aerial vehicles to detect explosive objects is a promising area of demining. Currently, a significant role in identifying detected objects remains with the operator (human). To solve the problem of identifying images of explosive objects, it is advisable to equip unmanned aerial vehicles or other robotic systems with appropriate software and hardware with built-in elements of artificial intelligence that would allow to recognize detected objects with a certain probability by their characteristic local features (shape/image, body material, body temperature) and classify them without operator participation. Recognition consists in calculating the relevance measure between the descriptions of the object and the reference, whose images are given as sets of descriptor vectors, and optimizing the values of this measure for the reference database. The article discusses the main algorithms (methods) for image identification and selects those that have higher performance and are built according to the binary type, which simplifies the calculation process and reduces costs. In general, the combination of ORB and BRISK descriptors gives better results, since it takes into account color distortion, poor lighting, noise, and different projections. The paper proposes approaches to forming a database of explosive ordnance images, which will further serve as an input layer for training a neural network to identify explosive ordnance. In the course of the work, a program code was written that makes it possible to test the performance of combined methods (algorithms) of image identification in terms of database formation, storage and testing.

Assessment of Slope Mapping by Using Unmanned Aerial Vehicle (UAV)

Assessment of Slope Mapping by Using Unmanned Aerial Vehicle (UAV)

A multi-branch dual attention segmentation network for epiphyte drone images

Acquiring images of epiphytes growing on trees using Unmanned Aerial Vehicles (UAVs) enables botanists to efficiently collect data on these important plant species. Despite the advantages offered by UAVs, challenges such as complex backgrounds, uneven lighting inside the tree canopy, and accessibility issues hinder the acquisition of quality images, resulting in acquiring images datasets of heterogenous quality. AI/Deep Learning algorithms can be used to segment target plants in these images for selecting sampling locations. Existing DL models require large volume of data for training, and they tend to prioritize local features over global ones, impacting segmentation accuracy, particularly on smaller, heterogeneous quality image datasets. To overcome these limitations, we propose a multi-branch dual attention segmentation network designed to effectively handle small datasets with heterogeneous quality. The proposed network incorporates dedicated branches for extracting both global and local features, utilizing spatial and channel attention mechanisms to focus on important regions. Through a fusion process and a decoder with crossed fusion technique, this network effectively combines and enhances features from multiple branches, resulting in improved segmentation performance. Output obtained from the trained model demonstrated major improvements in predicting the boundary regions and class labels, even in close-range, low-light, and zoomed/cropped images. The average Intersection over Union (IoU) scores of the trained model was 5% higher for images acquired close range, 48% higher for images in low-light conditions, and 68% higher for zoomed/cropped images when compared to those obtained from TransUnet, a state-of-the-art vision transformer model trained on epiphyte dataset. The proposed network can be used for segmenting epiphytes in images of heterogeneous quality as well as identifying targets in images acquired in domains such as agriculture and forestry.

Deep Learning-Based Detection and Segmentation of Damage in Solar Panels

Renewable energy can lead to a sustainable future and solar energy is one the primary sources of renewable energy. Solar energy is harvested mainly by photovoltaic plants. Though there are a large number of solar panels, the economic efficiency of solar panels is not that high in comparison to energy production from coal or nuclear matter. The main risk involved in solar plants is the high maintenance cost involved in maintaining the plants. To help reduce this issue, automated solutions using Unmanned Aerial Vehicles (UAVs) and satellite imagery are proposed. In this research work, we propose a novel deep learning architecture for the segmentation of solar plant aerial images, which not only helps in automated solar plant maintenance, but can also be used for the area estimation and extraction of solar panels from an image. Along with this, we also propose a transfer learning-based model for the efficient classification of solar panel damage. Solar panel damage classification has a lot of applications. It can be integrated into monitoring systems, raising alerts when there is severe damage or damage of a certain type. The adaptive UNet model with Atrous Spatial Pyramid Pooling (ASPP) module that performed the dilated convolutions that we proposed achieved an overall accuracy of 98% with a Mean Intersection-Over-Union (IoU) Score of 95% and took under a second to process an image. Our classification model using Visual Geometry Group 19 (VGG19) as the backbone for feature extraction has achieved a classification accuracy of 98% with an F1 score of 99%, thus detecting the five classes of damage, including undamaged solar panels, in an efficient manner.

Distribution network insulator detection based on improved ant colony algorithm and deep learning for UAV

Distribution network insulator detection based on improved ant colony algorithm and deep learning for UAV

Diagnostic study of nitrogen nutrition in cotton based on unmanned aerial vehicle RGB images

Nitrogen fertilizer levels significantly affect crop growth and development, necessitating precision management. Most studies focus on nitrogen nutrient estimation using vegetation indices and textural features, overlooking the diagnostic potential of color features. Hence, we investigated cotton nitrogen nutrition status using unmanned aerial vehicle (UAV) image features and the nitrogen nutrient index (NNI). Random frog algorithm - and random forest-screened image feature sets significantly correlated with the NNI, which were substituted into four machine learning algorithms for NNI estimation modeling. The composite scores (F) of optimal image feature sets were calculated using the coefficient of variation method for comprehensive cotton nitrogen nutrient diagnosis. Validation of the model for determining the critical nitrogen concentration in cotton yielded a coefficient of determination R2 = 0.89, root mean square error RMSE = 0.50 g (100 g)-1, and mean absolute error MAE = 0.44, demonstrating improved performance. Additionally, our novel NNI estimation model constructed based on the optimal image feature sets exhibited R2c = 0.97, RMSEc = 0.02, MAEc = 0.02, R2v = 0.85, RMSEv = 0.05, and MAEv = 0.04. Polynomial fitting of the composite index with NNI indicated that the model was reliable and yielded the following diagnostic criterion: 0.48 < F2 < 0.67 indicated nitrogen overapplication, whereas F2 < 0.48 or F2 > 0.67 indicated nitrogen deficiency. This study demonstrates the superior effectiveness of using UAV RGB image feature sets for NNI estimation and the quick, accurate diagnosis of cotton nitrogen levels, which will help guide nitrogen fertilizer application.

Estimation of Evaporation and Drought Stress of Pistachio Plant Using UAV Multispectral Images and a Surface Energy Balance Approach

Water scarcity is a critical abiotic stress factor for plants in arid and semi-arid regions, impacting crop development and production yield and quality. Monitoring water stress at finer scales (e.g., farm and plant), requires multispectral imagery with thermal capabilities at centimeter resolution. This study investigates drought stress in pistachio trees in a farm located in Yazd province, Iran, by using Unmanned Aerial Vehicle (UAV) images to quantify evapotranspiration and assess drought stress in individual trees. Images were captured on 10 July 2022, using a Matrix 300 UAV with a MicaSense Altum multispectral sensor. By employing the Surface Energy Balance Algorithm for Land (SEBAL), actual field evapotranspiration was accurately calculated (10 cm spatial resolution). Maps of the optimum crop coefficient (Kc) were developed from the Normalized Difference Vegetation Index (NDVI) based on standard evapotranspiration using the Food and Agriculture Organization (FAO) 56 methodology. The comparison between actual and standard evapotranspiration allowed us to identify drought-stressed trees. Results showed an average and maximum daily evaporation of 4.3 and 8.0 mm/day, respectively, in pistachio trees. The real crop coefficient (Kc) for pistachio was 0.66, contrasting with the FAO 56 standard of 1.17 due to the stress factor (Ks). A significant correlation was found between Kc and NDVI (R2 = 0.67, p < 0.01). The regression model produced a crop coefficient map, valuable to support precise irrigation management and drought prevention, considering the heterogeneity at the farm scale.

Multi-Target Optimization Strategy for Unmanned Aerial Vehicle Formation in Forest Fire Monitoring Based on Deep Q-Network Algorithm

Forest fires often pose serious hazards, and the timely monitoring and extinguishing of residual forest fires using unmanned aerial vehicles (UAVs) can prevent re-ignition and mitigate the damage caused. Due to the urgency of forest fires, drones need to respond quickly during firefighting operations, while traditional drone formation deployment requires a significant amount of time. This paper proposes a pure azimuth passive positioning strategy for circular UAV formations and utilizes the Deep Q-Network (DQN) algorithm to effectively adjust the formation within a short timeframe. Initially, a passive positioning model for UAVs based on the relationships between the sides and angles of a triangle is established, with the closest point to the ideal position being selected as the position for the UAV to be located. Subsequently, a multi-target optimization model is developed, considering 10 UAVs as an example, with the objective of minimizing the number of adjustments while minimizing the deviation between the ideal and adjusted UAV positions. The DQN algorithm is employed to solve and design experiments for validation, demonstrating that the deviation between the UAV positions and the ideal positions, as well as the number of adjustments, are within acceptable ranges. In comparison to genetic algorithms, it saves approximately 120 s.

Enhancing Tower Crane Safety: A UAV-Based Intelligent Inspection Approach

Tower cranes play a crucial role in the construction industry, facilitating the vertical and horizontal movement of materials and aiding in building construction, especially for high-rise structures. However, tower crane accidents can lead to severe consequences, highlighting the importance of effective safety management and inspection. This paper presents a novel approach to tower crane safety inspections using Unmanned Aerial Vehicles (UAVs) equipped with high-definition cameras and an intelligent inspection APP system. The system utilizes real-time kinematic (RTK) positioning and digital image processing to perform efficient and comprehensive inspections, reducing the reliance on manual labor and associated risks. A case study demonstrated the method’s practicality and effectiveness, with the UAV inspections capable of identifying 76.3% of major hazards, 64.8% of significant hazards, and 76.2% of general hazards within a 30-minute timeframe. Preliminary identification rates were also promising. Despite the initial requirement for manual drone piloting and the current manual review of images, the approach shows significant potential for enhancing safety in the construction industry. Future work will focus on integrating AI for hazard recognition and automating the inspection process further. The proposed method marks a step forward in tower crane safety management, offering a more efficient and accurate alternative to traditional inspection methods.

Anti-unmanned aerial vehicle detection system for airports: aviation and national security perspective

Unmanned Aerial Vehicles gained significant popularity in the last decade as demonstrated by their wide usage in various fields. From around the year 2001, the usage of unmanned aerial vehicles’ technology was mainly confined to law enforcement agencies such as the military, police, and customs. In the contemporary, terrorists have also been observed to be using unmanned aerial vehicles to attack aviation facilities. The current paper examines the levels of vulnerability of the Namibian airports to possible intrusion and attack from unmanned aerial vehicles, a situation that could pose a serious threat to aviation and national security. Adopting a qualitative research approach, the study made use of a questionnaire and semi-structured interview guide to collect primary data from the participants. Microsoft Excel was used to analyse the data. The study establishes that Namibian airports are prone to attacks from unmanned aerial vehicles as there are no anti-unmanned aerial vehicle detection systems installed at all airports in the country. Thus, there is clear evidence that the Namibia Civil Aviation Authority and the Namibian Airport Company’s regulations and policies on aviation safety and security did not prioritise the installation of anti-unmanned aerial vehicle detection systems at all airports in Namibia. The paper suggests that, in order to enhance aviation safety and security, a joint civil/military Information Technology Unit, responsible for spoofing, detection, and the monitoring of illicit unmanned aerial vehicle operations should be set up and operations activated at all airports and other public infrastructures in Namibia.

UAV-based modelling of vegetation recovery under extreme habitat stresses in the water level fluctuation zone of the Three Gorges Reservoir, China

Impoundment of the Three Gorges Reservoir on the upper Yangtze River has remarkably altered hydrological regime within the dammed reaches, triggering structural and functional changes of the riparian ecosystem. Up to date, how vegetation recovers in response to compound habitat stresses in the water level fluctuation zone remains inexplicitly understood. In this study, plant above-ground biomass (AGB) in a selected water level fluctuation zone was quantified to depict its spatial and temporal pattern using unmanned aerial vehicle (UAV)-derived multispectral images and screened empirical models. The contributions of multiple habitat stressors in governing vegetation recovery dynamics along the environmental gradient were further explored. Screened random forest models indicated relatively higher accuracy in AGB estimation, with R2 being 0.68, 0.79 and 0.62 during the sprouting, growth, and mature periods, respectively. AGB displayed a significant linear increasing trend along the elevational gradient during the sprouting and early growth period, while it showed an inverted U-shaped pattern during late growth and mature period. Flooding duration, magnitude and timing were found to exert greater negative effects on plant sprouting and biomass accumulation and acted as decisive factors in governing the elevation-dependent pattern of AGB. Localized spatial variations in AGB were modulated by other stressors such as sediment burial, soil erosion, soil moisture and nutrient content. Occurrence of episodic summer floods and vegetation distribution were responsible for an inverted U-shaped pattern of AGB during the late growth and mature period. Generally, AGB reached its peak in August, thereafter an obvious decline by an unprecedent dry-hot climatic event. The water level fluctuations with cumulative flooding effects exerted substantial control on AGB temporal dynamics, while climatic condition played a secondary role. Herein, further restorative efforts need to be directed to screening suitable species, maintaining favorable soil condition, and improving vegetation pattern to balance the many trade-offs.

Farmland quality assessment using deep learning and UAVs

The transformation of agricultural landscapes due to the intensification of farming practices has placed a major threat on habitat and species diversity. Stopping the loss of biodiversity is addressed in various international treaties and adequate strategies are required to assess the initial status of biodiversity and to measure the success of policies. While traditional methods of field-based biodiversity monitoring are time consuming, costly, and highly surveyor-dependent, recent advances in remote sensing and machine learning enable a systematic, more general and cost-effective monitoring. Accordingly, this study investigated whether large-scale biodiversity monitoring campaigns such as the European Monitoring of Biodiversity in Agricultural Landscapes (EMBAL) could be supported by the use of Unmanned Aerial Vehicle (UAV) based remote sensing and Convolutional Neural Networks (CNNs). For this purpose, the Structural Nature Value (SNV) indicator was proposed, which allows an intuitive estimation of the species richness of landscape sections. Subsequently, a CNN was trained to directly predict the SNV. For the accuracy assessment, a survey was conducted in which participants were asked to assess the SNV for various landscape patches, both to obtain an independent test set, and to evaluate the intuitiveness of the concept. The CNN achieved a mean weighted F1-score of 0.81 with an overall accuracy of 81 %. It was shown that the CNN can provide promising results to not only aid large-scale biodiversity monitorings, but to enhance the quality of their results through the automated analysis of fine-resolution UAV imagery.

Stower-13: A Multi-View Inspection Image Dataset for the Automatic Classification and Naming of Tension Towers

In high-voltage transmission lines, the tension tower needs to withstand the tension load of the overhead power line for a long time, which is prone to damage, and it is an important part of the inspection in the circuit inspection. In the modern circuit inspection process, operation and maintenance personnel mostly use unmanned aerial vehicles (UAVs) to photograph various parts of the tension tower, obtain inspection images, and manually classify and name the massive inspection images, which is low in accuracy and efficiency. Based on the above problems, this paper collects a large number of real-life UAV inspection images of various parts of a tension tower, and proposes the Stower-13 inspection image dataset, which is used to train the classification model to achieve automatic classification and naming of inspection images. Based on this dataset, this paper also proposes an improved MobileViT model, in which the Scale-Correlated Pyramid Convolution Block Attention Block (SCPCbam) module is introduced, which adds the Convolution Block Attention Module (CBAM) to the four branches of the original Scale-Correlated Pyramid Convolution (SCPC) module, so as to strengthen the ability of image multi-scale information extraction and improve the classification accuracy. This paper discusses a number of experiments on the model, and the experimental results show that the dataset proposed in this paper helps the model to understand the feature information. At the same time, the improved MobileViT model has a strong ability to extract image spatial feature information, the classification accuracy is higher than that of other models of the same type, so it is able to cope with a wide range of problems that arise in the course of practice, and it meets the practical needs of automatically naming transmission line inspection images.

Age-of-Information based Multi-UAV Trajectories Using Deep Reinforcement Learning

The use of unmanned aerial vehicles (UAVs) as a unified platform for sensing and communication is especially relevant in environments with inadequate infrastructure. In this paper, a multi-UAV system is constructed for dynamic data collection in a resource-constrained environment. The devised approach involves the implementation of an access platform referred to as the Access UAV (A_UAV). This A_UAV orchestrates the data collection process from Inspection-UAVs (I_UAVs), each equipped with a visual sensor, facilitating the relay of collected data to the cloud. Our approach jointly considers the trajectory scheduling of A_UAV and I_UAV to collect data from specific points in a decentralized manner. Specifically, a Deep Reinforcement Learning framework utilizing an actor-critic network is formulated for A_UAV, aiming to generate an equitable access schedule for I_UAVs. Moreover, trajectory scheduling of A_UAV ensures dynamic data collection while minimizing total system energy and the Age of Information (AoI) of data arriving from I_UAVs. The simulation results validate the performance of our proposed approach against several baselines under different parameter settings.

A method for detecting photovoltaic panel faults using a drone equipped with a multispectral camera

Abstract. Photovoltaic power stations utilizing solar energy, have grown in scale, resulting in an increase in operational maintenance requirements. Efficient inspection of components within these stations is crucial. However, the large area of photovoltaic power generation, coupled with a substantial number of photovoltaic panels and complex geographical environments, renders manual inspection methods highly inefficient and inadequate for modern photovoltaic power stations. To address this issue, this paper proposes a method and system for hot spot detection on photovoltaic panels using unmanned aerial vehicles (UAVs) equipped with multispectral cameras. The UAVs capture visible and infrared images of the photovoltaic power plant, which are then processed for photogrammetry to determine imaging position and attitude. The infrared images are stitched together using this information, forming a geographically referenced overall image. Hot spot detection is performed on the infrared images, enabling the identification of faulty photovoltaic panels and facilitating efficient inspection and maintenance. Experimental trials were conducted at a photovoltaic power station in Qingyuan, Guangdong Province China. The results demonstrate the effectiveness of the proposed method in accurately detecting panels with hot spot faults.

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