Search Results (121)

Hyperspectral imaging systems frequently rely on spectral rather than spatial resolving power for identifying objects within a scene. A hyperspectral imaging system’s response to point targets under flight conditions provides a novel technique for extracting system-level radiometric performance that is comparable to spatially unresolved objects.The system-level analysis not only provides a method for verifying radiometric calibration during flight but also allows for the exploration of the impacts on small target radiometry, post orthorectification. Standard Lambertian panels do not provide similar insight due to the insensitivity of orthorectification over a uniform area. In this paper, we utilize a fixed mounted hyperspectral imaging system (radiometrically calibrated) to assess eight individual point targets over 18 drone flight overpasses. Of the 144 total observations, only 18.1% or 26 instances are estimated to be within the uncertainty of the predicted entrance aperture-reaching radiance signal. For completeness, the repeatability of Lambertian and point targets are compared over the 18 overpasses, where the effects of orthorectification drastically impact the radiometric estimate of point targets. The unique characteristic that point targets offer, being both a known spatial and radiometric source, is that they are the only field-deployable method for understanding the small target radiometric performance of drone-based hyperspectral imaging systems. Full article

The invasive morning glory, Ipomoea purpurea (Convolvulaceae), poses a mounting challenge in vineyards by hindering grape harvest and as a secondary host of disease pathogens, necessitating advanced detection and control strategies. This study introduces a novel automated image analysis framework using aerial images obtained from a small fixed-wing unmanned aircraft system (UAS) and an RGB camera for the large-scale detection of I. purpurea flowers. This study aimed to assess the sampling fidelity of aerial detection in comparison with the actual infestation measured by ground validation surveys. The UAS was systematically operated over 16 vineyard plots infested with I. purpurea and another 16 plots without I. purpurea infestation. We used a semi-supervised segmentation model incorporating a Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to detect and count I. purpurea flowers. The flower detectability of the GMM was compared with that of conventional K-means methods. The results of this study showed that the GMM detected the presence of I. purpurea flowers in all 16 infested plots with 0% for both type I and type II errors, while the K-means method had 0% and 6.3% for type I and type II errors, respectively. The GMM and K-means methods detected 76% and 65% of the flowers, respectively. These results underscore the effectiveness of the GMM-based segmentation model in accurately detecting and quantifying I. purpurea flowers compared with a conventional approach. This study demonstrated the efficiency of a fixed-wing UAS coupled with automated image analysis for I. purpurea flower detection in vineyards, achieving success without relying on data-driven deep-learning models. Full article

A steady supply of mineral raw materials is vital for the transition to a low-carbon, circular economy. The number of active mines in Europe has severely declined over the last century and half, giving rise to many abandoned mining waste sites and corresponding geological heritage. Also, the rise in minerals demand for large-scale deployment of renewable energy requires the continued and steady availability of key minerals. The supply risk associated with unpredicted geopolitical events needs to be eliminated/mitigated. Historical mine waste sites are the answer but evaluating mine waste is a lengthy and costly exercise. The study, undertaken in the Lousal Mine, used small unmanned aerial systems (sUASs) to model and determine mine waste volumes by generating orthomosaic maps with quick, inexpensive, and reliable results. Calculated mine waste volumes between 308,478 m³ and 322,455 m³ were obtained. XRD and p-XRF techniques determined the mineralogy and chemistry of waste, which varied from mineralization and host rocks with hydrothermal alteration and numerous neogenic sulphates (mostly gypsum, rhomboclase, ferricopiapite, coquimbite, and jarosite) related with supergene processes and weathering. The study shows the viability of using these sUASs to successfully model historical mine waste sites in an initial phase and for future monitoring programs. Full article

Floral resources for native pollinators that live in wildland settings are diverse and vary across and within growing seasons. Understanding floral resource dynamics and management is becoming increasingly important as honeybee farms seek public land for summer pasture. Small Unmanned Aircraft Systems (sUASs) present a viable approach for accurate broad floristic surveys and present an additional solution to more traditional alternative methods of vegetation assessment. This methodology was designed as a simplified approach using tools frequently available to land managers. The images of three subalpine meadows were captured from a DJI Phantom 4 Pro drone platform three times over the growing season in 2019 in Sanpete County, Utah. The images were composited using Pix4D software 4.5.6 and classified using a simple supervised approach in ENVI 4.8 and ArcGIS Pro 2.4.3 These same meadows were assessed using two traditional ocular methods of vegetation cover–meter-squared quadrats and macroplot estimation. The areas assessed with these methods were compared side by side with their classified counterparts from drone imagery. Classified images were not only found to be highly accurate when detecting overall floral cover and floral color groups (76–100%), but they were also strongly correlated with quadrat estimations, suggesting that these methods used in tandem may be a conducive strategy toward increased accuracy and efficiency when determining floral cover at broad spatial scales. Full article

With the development of UAV automatic cruising along power transmission lines, intelligent defect detection in aerial images has become increasingly important. In the process of target detection for aerial photography of transmission lines, insulator defects often pose challenges due to complex backgrounds, resulting in noisy images and issues such as slow detection speed, leakage, and the misidentification of small-sized targets. To address these challenges, this paper proposes an insulator defect detection algorithm called DFCG_YOLOv5, which focuses on improving both the accuracy and speed by enhancing the network structure and optimizing the loss function. Firstly, the input part is optimized, and a High-Speed Adaptive Median Filtering (HSMF) algorithm is introduced to preprocess the images captured by the UAV system, effectively reducing the noise interference in target detection. Secondly, the original Ghost backbone structure is further optimized, and the DFC attention mechanism is incorporated to strike a balance between the target detection accuracy and speed. Additionally, the original CIOU loss function is replaced with the Poly Loss, which addresses the issue of imbalanced positive and negative samples for small targets. By adjusting the parameters for different datasets, this modification effectively suppresses background positive samples and enhances the detection accuracy. To align with real-world engineering applications, the dataset utilized in this study consists of unmanned aircraft system machine patrol images from the Yunnan Power Supply Bureau Company. The experimental results demonstrate a 9.2% improvement in the algorithm accuracy and a 26.2% increase in the inference speed compared to YOLOv5s. These findings hold significant implications for the practical implementation of target detection in engineering scenarios. Full article

The Tajos de Alhama de Granada, which since ancient times have inspired and surprised locals and strangers, especially foreign travelers, constituted a unique landscape, cultural and ethnological heritage of Spain, linked to water and its old flour mills. And, they are currently at serious risk of degradation. The aim of this research is to obtain a high-resolution 3D model capable of documenting this historical heritage environment with a high level of detail, using a methodology that includes small light weight LiDAR (Light Detection and Ranging) system for UAS (Unmanned Aircraft System). The model obtained should serve, on the one hand, as a valuable tool for knowledge and analysis of all the elements (river, lake, ditches, dams, mills, aqueducts, and paths) that made up this place, registered as a picturesque landscape for its extraordinary beauty and uniqueness, and on the other hand, as a basis for the development of rehabilitation and architectural restoration projects that would have to be undertaken to preserve this cultural and landscape legacy. Full article

This study examines the wing hinge oscillations in an aircraft concept that employs multiple wings, or small aircraft, chained at the wing tips through freely rotatable hinges with minimal structural damping and no mechanical position-locking system. This creates a single pseudo long-span aircraft that resembles a flying chain oriented perpendicular to the flight direction. Numerical calculations were conducted using the vortex lattice method and modified equations for a multi-link rigid body pendulum. The calculations demonstrated good agreement with small-scale wind tunnel experiments, where the motion of the chained wings was tracked through color tracking, and the forces were measured using six-axis force sensors. The total $C_L/C_D$ increased for the chained wings, even in the presence of hinge joint oscillations. Furthermore, numerical simulations assuming an unmanned airplane size corroborated the theoretical attainment of passive stability with high chained numbers ($\ge 9$ wings), without any structural damping and relying solely on aerodynamic forces. Guidelines for appropriate hinge axis angle δ and angle-of-attack regions for different chained wing numbers to maximize passive oscillation stability were obtained. The results showed that wing-tip-chained airplanes could successfully provide substantially large wing spans while retaining flexibility, light weight and $C_L/C_D$, without requiring active hinge rotation control. Full article

Advances in remote sensing and small unmanned aircraft systems (sUAS) have been applied to various precision agriculture applications. However, there has been limited research on the accuracy of real-time kinematic (RTK) sUAS photogrammetric elevation surveys, especially in preparation for precision agriculture practices that require precise topographic surfaces, such as increasing irrigation system efficiency. These practices include, but are not limited to, precision land grading, placement of levees, multiple inlet rice irrigation, and computerized hole size selection for furrow irrigation. All such practices rely, in some way, on the characterization of surface topography. While agro-terrestrial (ground-based) surveying is the dominant method of agricultural surveying, aerial surveying is emerging and attracting potential early adopters. This is the first study of its kind to assess the accuracy, precision, time, and cost efficiency of RTK sUAS surveying in comparison to traditional agro-terrestrial techniques. Our findings suggest sUAS are superior to ground survey methods in terms of relative elevation and produce much more precise raster surfaces than ground-based methods. We also showed that this emergent technology reduces costs and the time it takes to generate agricultural elevation surveys. Full article

Controlling non-native plant invasions that reduce the quality of preferred wetland habitats is a challenge for many wetland managers. Herbicides may be used to control invasions, but it may be difficult to find effective application methods depending on the terrain. Manned aircraft cover large patches, but aerial use is limited by high costs, weather conditions, and overspray concerns. Ground applications target smaller patches, but their effectiveness may be limited by accessibility, labor costs, and applicator health concerns. Considering these difficulties, unmanned aerial systems (UAS) have emerged as a viable alternative for more effectively treating plant invasions. We tested the use of a specialized UAS to control invasive perennial pepperweed (Lepidium latifolium) in Suisun Marsh in northern California, USA. This “spray drone” flew at an altitude of 2–3 m, a speed of 24 kmph, and applied herbicide with a swath width of 6 m. We applied herbicide with the spray drone to treat small patches before they expanded. To delineate invasive patch boundaries, we first flew a survey drone with a 4K resolution camera to detect emerging plants with color imagery and conduct an initial classification analysis. We subsequently visited areas with suspected invasive patches based on the classification, and observers manually confirmed the presence of the invasive species. We then flew the spray drone on transects to treat the patches and examined the results with post-treatment survey drone imagery and plots along ground transects. In total, we sprayed 14 ha of Lepidium across eight sites and found that 87% of the Lepidium was discernibly affected by our herbicide treatment. Furthermore, we measured the overspray, which was substantially less than that reported for other aerial application methods such as helicopter-spraying, and our estimated operational costs were lower. Our results indicated that applying remote-sensing imagery for the identification of invasive species patches and the use of a spray drone for treatment may be an effective means of controlling invasive plants with high precision at a reasonable cost. In the near future, a unified UAS system that both identifies invasive species and then treats them in a single pass should be a promising goal for early detection and rapid response in wetland management. Full article

Pushbroom hyperspectral imaging (HSI) systems intrinsically measure our surroundings by leveraging 1D spatial imaging, where each pixel contains a unique spectrum of the observed materials. Spatial misregistration is an important property of HSI systems because it defines the spectral integrity of spatial pixels and requires characterization. The IEEE P4001 Standards Association committee has defined laboratory-based methods to test the ultimate limit of HSI systems but negates any impacts from mounting and flying the instruments on airborne platforms such as unmanned aerial vehicles (UAV’s) or drones. Our study was designed to demonstrate a novel vicarious technique using convex mirrors to bridge the gap between laboratory and field-based HSI performance testing with a focus on extracting hyperspectral spatial misregistration. A fast and simple extraction technique is proposed for estimating the sampled Point Spread Function’s width, along with keystone, as a function of wavelength for understanding the key contributors to hyperspectral spatial misregistration. With the ease of deploying convex mirrors, off-axis spatial misregistration is assessed and compared with on-axis behavior, where the best performance is often observed. In addition, convex mirrors provide an easy methodology to exploit ortho-rectification errors related to fixed pushbroom HSI systems, which we will show. The techniques discussed in this study are not limited to drone-based systems but can be easily applied to other airborne or satellite-based systems. Full article

In recent years, there has been a considerable growth in the demand for low-orbit satellites, leading to a need for more flexible and cost-effective launch systems. This study presents a low-cost “carrier-launcher” configuration designed for space missions in low earth orbit. The carrier is a remote-controlled unmanned flying wing that can fulfil the role of the first stage of a multi-stage earth-to-orbit launcher rocket. Making the carrier a flying wing increases its effectiveness and efficiency compared to other state-of-the-art options. The flying wing architecture allows for a significantly lighter carrier compared to the traditional aircraft. The launcher is carried on the wing’s upper surface and is released during a high-altitude almost “zero g” parabolic manoeuvre. A state-of-the-art analysis has been conducted to initialize and develop the carrier’s conceptual configuration. The aerodynamics and flight mechanics of the flying wing carrier were studied using the potential aerodynamic code Athena Vortex Lattice. The high-altitude launcher’s release manoeuvre has been investigated to properly assess the required installed thrust. Finite element analyses were also performed using NASTRAN to preliminarily evaluate the aeroelastic behaviour of the proposed “carrier-launcher” configuration. The overall results show the conceptual feasibility of the flying wing carrier for launching small satellites in low earth orbit. This study provides valuable insights into the development of cost-effective launch systems for the growing demand in the low-orbit satellite sector. Our proposed design has a maximum take-off mass of 122,000 kg, uses 4 Rolls-Royce UltraFan model engines, has a wingspan of 54 m, and can carry a 10,000 kg launcher to put a 460 kg payload in LEO. As it is an initial conceptual study, this investigation establishes an initial benchmark for forthcoming inquiries, hence providing a starting point of a breakthrough concept to foster its future development. Full article

The Aerospace Plane Research Center at the Muroran Institute of Technology is currently conducting research to develop enabling technologies for high-speed aircraft traveling at high altitudes and constructing experimental, small-scale, unmanned supersonic aircraft called Oowashi as a testbed for flight. To confirm the control performance of the aircraft, an experiment using a one-third-scale model of the Oowashi aircraft has been planned. The flight of high-speed aircraft always presents the problem of having to land on an ordinary runway regardless of the aircraft’s high speed at the beginning of the landing process. This paper therefore proposes a new landing control design method that can shorten the landing distance for a high-speed aircraft without increasing the rate of descent. The design method utilizes the newly clarified relationship between an angle of attack and the time constant of flare control system, which is effective to raise glideslope angle during landing. The validity of the method is confirmed by computer simulation assuming the model aircraft equivalent to a one-third-scale model of the Oowashi aircraft. Full article

Heavy-fuel aviation piston engines (HF-APEs) are widely used in general aviation and unmanned aerial vehicle (UAV) due to their safety and fuel economy. This paper describes a numerical and experimental study of scavenging and combustion processes on a 2-Stroke Direct Injected HF-APEs for light aircraft, with its cylinder specifically designed as cross scavenging. A 3-Dimentional transient model of in-cylinder flow and combustion process is established by the Forte platform, and the engine test system is set up. By comparing the simulation results to the experimental results, it showed that multi-ports cross scavenging can generate unbalanced aerodynamic torque in the cylinder. In the compression process, the swirl ratio (SR) gradually increases, and the peak SR reaches 15. Moreover, approximately 25% of exhaust residual gas in the cylinder is conducive to the fuel atomization and evaporation process in a high-altitude environment. When the injection timing is between −8 °CA and −16 °CA, the engine has the optimal power and economy performance at different altitudes. Finally, when the injection advance angle moves forward by 4 °CA, the maximum pressure increases by 2 MPa, with the rising rate decreasing gradually. The results have important significance for the development of the combustion system of small 2-Stroke Direct Injected HF-APEs. Full article

The marsupial unmanned aircraft system (UAS) consists of a large parent unmanned aerial vehicle (UAV) and multiple small children UAVs that can be launched and recovered in the air. The employment of marsupial UAS can expand the mission range of small UAVs and enhance the collaborative capabilities of small UAVs. However, the serious aerodynamic interference between the parent UAV and the child UAV will affect the flight safety during the launch and recovery process. In this paper, the interference characteristics of marsupial UAS is investigated through ground tests and CFD simulation. Ground tests compared the lift and power of the child UAV with and without parent UAV interference in different areas, and the simulation extended the experimental scope. Three specific interference regions above the parent UAV are defined, including the area above the rotors, the area above body and the transition area. In the first two aeras, the variation of the disturbed lift is more than 30% of the child UAV weight. In the transition aera, the child UAV will be subjected to significant lift variations and asymmetric moments. According to the interference characteristics of different regions, the safe flight boundaries and the appropriate paths of children UAVs are proposed. Full article

In recent years, deep learning-based approaches have proliferated across a variety of ecological studies. Inspired by deep learning’s emerging prominence as the preferred tool for analyzing wildlife image datasets, this study employed You Only Look Once (YOLO), a single-shot, real-time object detection algorithm, to effectively detect cavity trees of Red-cockaded Woodpeckers or RCW (Dryobates borealis). In spring 2022, using an unmanned aircraft system (UAS), we conducted presence surveys for RCW cavity trees within a 1264-hectare area in the Sam Houston National Forest (SHNF). Additionally, known occurrences of RCW cavity trees outside the surveyed area were aerially photographed, manually annotated, and used as a training dataset. Both YOLOv4-tiny and YOLOv5n architectures were selected as target models for training and later used for inferencing separate aerial photos from the study area. A traditional survey using the pedestrian methods was also conducted concurrently and used as a baseline survey to compare our new methods. Our best-performing model generated an mAP (mean Average Precision) of 95% and an F1 score of 85% while maintaining an inference speed of 2.5 frames per second (fps). Additionally, five unique cavity trees were detected using our model and UAS approach, compared with one unique detection using traditional survey methods. Model development techniques, such as preprocessing images with tiling and Sliced Aided Hyper Inferencing (SAHI), proved to be critical components of improved detection performance. Our results demonstrated the two YOLO architectures with tiling and SAHI strategies were able to successfully detect RCW cavities in heavily forested, heterogenous environments using semi-automated review. Furthermore, this case study represents progress towards eventual real-time detection where wildlife managers are targeting small objects. These results have implications for more achievable conservation goals, less costly operations, a safer work environment for personnel, and potentially more accurate survey results in environments that are difficult using traditional methods. Full article