Consumer-grade Unmanned Aerial Vehicle Research Articles

Estimation of corn crop damage caused by wildlife in UAV images

PurposeThis paper proposes a low-cost and low-effort solution for determining the area of corn crops damaged by the wildlife facility utilising field images collected by an unmanned aerial vehicle (UAV). The proposed solution allows for the determination of the percentage of the damaged crops and their location.MethodsThe method utilises image segmentation models based on deep convolutional neural networks (e.g., UNet family) and transformers (SegFormer) trained on over 300 hectares of diverse corn fields in western Poland. A range of neural network architectures was tested to select the most accurate final solution.ResultsThe tests show that despite using only easily accessible RGB data available from inexpensive, consumer-grade UAVs, the method achieves sufficient accuracy to be applied in practical solutions for agriculture-related tasks, as the IoU (Intersection over Union) metric for segmentation of healthy and damaged crop reaches 0.88.ConclusionThe proposed method allows for easy calculation of the total percentage and visualisation of the corn crop damages. The processing code and trained model are shared publicly.

Mangrove species detection using YOLOv5 with RGB imagery from consumer unmanned aerial vehicles (UAVs)

Despite comprising only one per cent of global forests, mangroves provide vital ecological and economic benefits to their ecosystems. Due to its decreasing extent over the past decade, there is a rise in research innovations supporting mangrove conservation. Specifically, consumer-grade Unmanned Aerial Vehicles (UAV) were proven effective as potential remote sensing alternatives to support mangrove research and monitoring in recent studies. As most studies use custom UAV-mounted sensors for mangrove species classification, similar studies using a UAV’s default red–green–blue (RGB) cameras were scarce. This study explores the potential of high-resolution RGB aerial images through state-of-the-art object detection algorithm, YOLOv5 to detect the dominant Rhizophora mangroves in Sarawak, Malaysia. A total of 400 RGB images were equally selected from two study areas and allocated into three datasets, two corresponding to each study area and one combining all images. The annotation process was performed using a previously proposed novel method, assisted by YOLOv5 for a semi-automated annotation process with expert verification. Systematic training experiments were conducted to select an optimal epoch size across models trained with each dataset. The final models produced an average true positive rate of 73.8% and 71.7% for each study site, while the combined dataset model produced an average true positive rate of 73.7%. Overall, this study demonstrated the potential of UAV-based RGB images and deep learning object detection architectures to identify specific mangrove objects, while also highlighting key considerations for similar future research.

Combining Low-Cost UAV Imagery with Machine Learning Classifiers for Accurate Land Use/Land Cover Mapping

Land use/land cover (LULC) is a fundamental concept of the Earth’s system intimately connected to many phases of the human and physical environment. LULC mappings has been recently revolutionized by the use of high-resolution imagery from unmanned aerial vehicles (UAVs). The present study proposes an innovative approach for obtaining LULC maps using consumer-grade UAV imagery combined with two machine learning classification techniques, namely RF and SVM. The methodology presented herein is tested at a Mediterranean agricultural site located in Greece. The emphasis has been placed on the use of a commercially available, low-cost RGB camera which is a typical consumer’s option available today almost worldwide. The results evidenced the capability of the SVM when combined with low-cost UAV data in obtaining LULC maps at very high spatial resolution. Such information can be of practical value to both farmers and decision-makers in reaching the most appropriate decisions in this regard.

MTL-PIE: A multi-task learning based drone pilot identification and operation evaluation scheme

As one of the most promising industries, consumer-grade Unmanned Aerial Vehicles (UAVs), also known as drones, have changed our lives. Although significant progress in drones has been made, adversary impersonation attacks still pose severe risks to flying drones. In addition, authorized pilot miss-operations also has become a critical factor leading to drone flight accidents. To validate the pilot's legal status and remind the authorized pilot about their miss-operations, we propose a multi-task learning-based drone pilot identification and operation evaluation scheme named MTL-PIE. Specifically, we first present qualitative and quantitative guidelines to evaluate pilot operation proficiency. Then, we design a pilot identification module and an operation evaluation module to resist pilot impersonation attacks and assess pilot operation proficiency, respectively. Finally, we propose a soft-parameter sharing mechanism to transfer knowledge between two modules and a dynamic weight-adjusting algorithm to prevent domain-dominant problems. Numerical results show that MTL-PIE can verify pilot legal status with an accuracy of 95.36% (outperforming our previous work with a margin of 2%-3%) and act as assessors to evaluate pilot operation proficiency with an accuracy of 94.47%. Note that MTL-PIE needs only 35 ms to verify pilot legal status and assess pilot operation proficiency; it has great potential to reduce drone flight accidents.

Advancing sun glint correction in high-resolution marine UAV RGB imagery for coral reef monitoring

Sun glint presents a significant challenge in marine ecological remote sensing by obscuring valuable features of benthic communities, thus hindering accurate monitoring of these communities. More specifically, sun glint leads to inaccurate coral reef identification when it comes to utilize the high-resolution marine Unmanned Aerial Vehicle (UAV) imagery. Despite the availability of many physical models that address this problem using ocean remote sensing satellite images, there is still a lack of effective sun glint correction solutions for high-resolution RGB imagery obtained by consumer-grade UAVs in marine ecological monitoring, particularly at the centimeter level. In this study, an innovative sun glint correction pipeline combining a Foreground Attention-based Semantic Segmentation Network (FANet) with optical flow-based pixel propagation has been proposed, aiming to achieve accurate sun glint detection and restoration of real texture of coral reefs. To further understand the inner mode of sun glint segmentation FANet model, we conduct explainable analysis to ensure that it learns the informative sun glint features and makes reliable decisions. According to the comprehensive experimental evaluation using three datasets, our FANet model exhibits accurate and effective sun glint detection, achieving an IoU range of 70.86%–83.96%. Moreover, the sun-glint free images processed by our sun glint correction method have been employed for large-scale image stitching and shallow-water coral reef habitat semantic mapping. The results demonstrate that our sun glint correction approach successfully restores the texture of coral reefs and prepares a reliable foundation for subsequent downstream applications. We believe that our method as an open-source sun glint correction solution in high-resolution marine UAV RGB images is able to enhance the accuracy and efficiency of various marine ecological remote sensing tasks at a low-cost. The relevant code can be found in https://github.com/jyqinnn/Sun-glint-correction.

Analysis of UAV Flight Patterns for Road Accident Site Investigation

Unmanned Aerial Vehicles (UAVs) offer a promising solution for road accident scene documentation. This study seeks to investigate the occurrence of systematic deformations, such as bowling and doming, in the 3D point cloud and orthomosaic generated from images captured by UAVs along an horizontal road segment, while exploring how adjustments in flight patterns can rectify these errors. Four consumer-grade UAVs were deployed, all flying at an altitude of 10 m while acquiring images along two different routes. Processing solely nadir images resulted in significant deformations in the outputs. However, when additional images from a circular flight around a designated Point of Interest (POI), captured with an oblique camera axis, were incorporated into the dataset, these errors were notably reduced. The resulting measurement errors remained within the 0–5 cm range, well below the customary error margins in accident reconstruction. Remarkably, the entire procedure was completed within 15 min, which is half the estimated minimum duration for scene investigation. This approach demonstrates the potential for UAVs to efficiently record road accident sites for official documentation, obviating the need for pre-established Ground Control Points (GCP) or the adoption of Real-Time Kinematic (RTK) drones or Post Processed Kinematic (PPK) technology.

Consumer-grade UAV imagery facilitates semantic segmentation of species-rich savanna tree layers

Conventional forest inventories are labour-intensive. This limits the spatial extent and temporal frequency at which woody vegetation is usually monitored. Remote sensing provides cost-effective solutions that enable extensive spatial coverage and high sampling frequency. Recent studies indicate that convolutional neural networks (CNNs) can classify woody forests, plantations, and urban vegetation at the species level using consumer-grade unmanned aerial vehicle (UAV) imagery. However, whether such an approach is feasible in species-rich savanna ecosystems remains unclear. Here, we tested whether small data sets of high-resolution RGB orthomosaics suffice to train U-Net, FC-DenseNet, and DeepLabv3 + in semantic segmentation of savanna tree species. We trained these models on an 18-ha training area and explored whether models could be transferred across space and time. These models could recognise trees in adjacent (mean F1-Score = 0.68) and distant areas (mean F1-Score = 0.61) alike. Over time, a change in plant morphology resulted in a decrease of model accuracy. Our results show that CNN-based tree mapping using consumer-grade UAV imagery is possible in savanna ecosystems. Still, larger and more heterogeneous data sets can further improve model robustness to capture variation in plant morphology across time and space.

Growth and evolution of the lava dome and coulée during the 2020–21 eruption of La Soufrière, St Vincent

Abstract The 2020–21 eruption of La Soufrière, St Vincent consisted of two distinct phases. The initial, effusive phase was characterized by the extrusion of a small lava dome inside the 1.5 km wide Summit Crater, and was followed by a violent explosive phase. Growth and evolution of the lava dome was monitored by visual observations and photogrammetry using imagery acquired from fixed-wing aircraft, helicopters and consumer-grade unmanned aerial vehicles. Additional monitoring was provided by satellite multispectral and radar imagery. Following the emergence of lava at the surface on 27 December 2020, volume and growth rate calculations showed the extrusion occurred at a steady rate of c . 1.8 m 3 s −1 and c . 16–19 × 10 6 m 3 of lava was extruded. The initial near-perfect hemispherical dome transitioned into a dome with a more elliptical footprint, eventually, evolving into a coulée-like feature with two lobes. The dome was destroyed by the onset of explosive activity on 9 April 2021.

Faba bean above-ground biomass and bean yield estimation based on consumer-grade unmanned aerial vehicle RGB images and ensemble learning

Faba bean above-ground biomass and bean yield estimation based on consumer-grade unmanned aerial vehicle RGB images and ensemble learning

Prospects of Consumer-Grade UAVs for Overpass Bridges Pier Pads Alignment

The use of Unmanned Aerial Vehicles (UAVs) for surveying is at the forefront of their use in the Architectural Engineering and Construction (AEC) industry. UAVs make accessing hard-to-reach construction regions simpler and more cost-effective because of their small size, ease of mobility, and the wealth of information given by their integrated sensors. Accordingly, their use is thriving in different AEC sectors such as the management and inspection of engineering facilities such as concrete bridges. Overpass bridge engineering inspections are still applied using high accuracy surveying instruments in situ to ensure meeting the quality standards of construction. One important application is to measure the bridge pier caps centerline fitting using total stations, which is costly in terms of time and labor. Therefore, in this article, a new approach based on consumer-grade UAV imaging is proposed for replacing the traditional surveying techniques which are expected to improve automation and reduce time and cost. The proposed method utilized a sequence of processes on the UAV point clouds of the bridge concrete pier caps to finally extract the pier pads center and check their alignment. In two experiments, point clouds are created using DJI Phantom 3 images taken over bridge pier projects under construction, and concrete pad centers are then estimated and compared to the reference total station measurements. The results of both tests reveal the ability of the proposed method to attain the required accuracy for the pads’ alignment, as the root mean square error (RMSE) is one centimeter and two centimeters for the first and second tests, respectively. In addition, the new approach can reduce implementation time and the project budget.

Bayesian-optimized deep learning model to segment deterioration patterns underneath bridge decks photographed by unmanned aerial vehicle

In recent years, bridge collapses and fractures have occurred in various countries mostly following a lack of inspection and maintenance. External inspection processes can be very time-consuming and pose labor safety hazards. Terrain obstacles may also prevent the thorough inspection of some structures. The use of artificial intelligence instead of visual inspection by bridge inspectors is state-of-the-art. This study develops a Bayesian-optimized deep learning model for use on an unmanned aerial vehicle (UAV) to identify the deterioration patterns and segment areas of composite decks under bridges by computer vision-based techniques. The proposed module alters traditional labor-intensive methods of visual bridge inspections, reduces labor safety hazards, and increases inspection accuracy. It can be embedded in an artificial intelligence chip, which is then installed in a consumer-grade UAV, making it a dedicated drone for the external inspection of composite bridges.

Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery.

Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson's correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R2, root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R2, RMSE, and MAE of 0.65, 10.53kg yield/kg N, and 8.90kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R2, RMSE, and MAE of 0.61, 7.48kg yield/kg N, and 6.05kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.

Classifying Sparse Vegetation in a Proglacial Valley Using UAV Imagery and Random Forest Algorithm

Extreme hydro-meteorological events become an increasing risk in high mountain environments, resulting in erosion events that endanger human infrastructure and life. Vegetation is known to be an important stabilizing factor; however, little is known about the spatial patterns of species composition in glacial forelands. This investigation aims to differentiate sparse vegetation in a steep alpine environment in the Austrian part of the Central Eastern Alps using low-cost multispectral cameras on an unmanned aerial vehicle (UAV). Highly resolved imagery from a consumer-grade UAV proved an appropriate basis for the SfM-based modeling of the research area as well as for vegetation mapping. Consideration must be paid to changing light conditions during data acquisition, especially with multispectral sensors. Different approaches were tested, and the best results were obtained using the Random Forest (RF) algorithm with the target class discrimination based on the RGB orthomosaic and the DEM as supplementary dataset. Our work contributes to the field of biogeomorphic research in proglacial areas as well as to the field of small-scale remote sensing and vegetation measuring. Our findings show that the occurrence of vegetation patches differs in terms of density and diversity within this relatively recent deglaciated environment.

M-O SiamRPN with Weight Adaptive Joint MIoU for UAV Visual Localization

Vision-based unmanned aerial vehicle (UAV) localization is capable of providing real-time coordinates independently during GNSS interruption, which is important in security, agriculture, industrial mapping, and other fields. owever, there are problems with shadows, the tiny size of targets, interfering objects, and motion blurred edges in aerial images captured by UAVs. Therefore, a multi-order Siamese region proposal network (M-O SiamRPN) with weight adaptive joint multiple intersection over union (MIoU) loss function is proposed to overcome the above limitations. The normalized covariance of 2-O information based on1-O features is introduced in the Siamese convolutional neural network to improve the representation and sensitivity of the network to edges. We innovatively propose a spatial continuity criterion to select 1-O features with richer local details for the calculation of 2-O information, to ensure the effectiveness of M-O features. To reduce the effect of unavoidable positive and negative sample imbalance in target detection, weight adaptive coefficients were designed to automatically modify the penalty factor of cross-entropy loss. Moreover, the MIoU was constructed to constrain the anchor box regression from multiple perspectives. In addition, we proposed an improved Wallis shadow automatic compensation method to pre-process aerial images, providing the basis for subsequent image matching procedures. We also built a consumer-grade UAV acquisition platform to construct an aerial image dataset for experimental validation. The results show that our framework achieved excellent performance for each quantitative and qualitative metric, with the highest precision being 0.979 and a success rate of 0.732.

Towards Improved Unmanned Aerial Vehicle Edge Intelligence: A Road Infrastructure Monitoring Case Study

Consumer-grade Unmanned Aerial Vehicles (UAVs) are poorly suited to monitor complex scenes where multiple analysis tasks need to be carried out in real-time and in parallel to fulfil time-critical requirements. Therefore, we developed an innovative UAV agnostic system that is able to carry out multiple road infrastructure monitoring tasks simultaneously and in real-time. The aim of the paper is to discuss the system design considerations and the performance of the processing pipeline in terms of computational strain and latency. The system was deployed on a unique typology of UAV and instantiated with realistic placeholder modules that are of importance for infrastructure inspection tasks, such as vehicle detection for traffic monitoring, scene segmentation for qualitative semantic reasoning, and 3D scene reconstruction for large-scale damage detection. The system was validated by carrying out a trial on a highway in Guadalajara, Spain. By utilizing edge computation and remote processing, the end-to-end pipeline, from image capture to information dissemination to drone operators on the ground, takes on average 2.9 s, which is sufficiently quick for road monitoring purposes. The system is dynamic and, therefore, can be extended with additional modules, while continuously accommodating developments in technologies, such as IoT or 5G.

Monitoring detailed mangrove hurricane damage and early recovery using multisource remote sensing data

Due to their location in tropical latitudes, mangrove forests are susceptible to the impact of hurricanes and can be vastly damaged by their high-speed winds. Given the logistic difficulties regarding field surveys in mangroves, remote sensing approaches have been considered a reliable alternative. We quantified trends in damage and early signs of canopy recovery in a fringe Rhizophora mangle area of Marismas Nacionales, Mexico, following the landfall of Hurricane Willa in October 2018. We monitored (2016–2021) broad canopy defoliation using 21 vegetation indices (VI) from the Google Earth Engine tool (GEE). We also mapped a detailed canopy fragmentation and developed digital surface models (DSM) during five study periods (2018–2021) with a consumer-grade unmanned aerial vehicle (UAV) over an area of 100 ha. Based on optical data from the GEE time series, results indicated an abrupt decline in the overall mangrove canopy. The VARI index was the most reliable VI for the mangrove canopy classification from a standard RGB sensor. The impact of the hurricane caused an overall canopy defoliation of 79%. The series of UAV orthomosaics indicate a gradual recovery in the mangrove canopy, while the linear model predicts at least 8.5 years to reach pre-impact mangrove cover conditions. However, the sequence of DSM estimates that the vertical canopy configuration will require a longer time to achieve its original structure.

The Potential of Widespread UAV Cameras in the Identification of Conifers and the Delineation of Their Crowns

With the ever-improving advances in computer vision and Earth observation capabilities, Unmanned Aerial Vehicles (UAVs) allow extensive forest inventory and the description of stand structure indirectly. We performed several flights with different UAVs and popular sensors over two sites with coniferous forests of various ages and flight levels using the custom settings preset by solution suppliers. The data were processed using image-matching techniques, yielding digital surface models, which were further analyzed using the lidR package in R. Consumer-grade RGB cameras were consistently more successful in the identification of individual trees at all of the flight levels (84–77% for Phantom 4), compared to the success of multispectral cameras, which decreased with higher flight levels and smaller crowns (77–54% for RedEdge-M). Regarding the accuracy of the measured crown diameters, RGB cameras yielded satisfactory results (Mean Absolute Error—MAE of 0.79–0.99 m and 0.88–1.16 m for Phantom 4 and Zenmuse X5S, respectively); multispectral cameras overestimated the height, especially in the full-grown forests (MAE = 1.26–1.77 m). We conclude that widely used low-cost RGB cameras yield very satisfactory results for the description of the structural forest information at a 150 m flight altitude. When (multi)spectral information is needed, we recommend reducing the flight level to 100 m in order to acquire sufficient structural forest information. The study contributes to the current knowledge by directly comparing widely used consumer-grade UAV cameras and providing a clear elementary workflow for inexperienced users, thus helping entry-level users with the initial steps and supporting the usability of such data in practice.

UAV and Structure-From-Motion Photogrammetry Enhance River Restoration Monitoring: A Dam Removal Study

Dam removal is a river restoration technique that has complex landscape-level ecological impacts. Unmanned aerial vehicles (UAVs) are emerging as tools that enable relatively affordable, repeatable, and objective ecological assessment approaches that provide a holistic perspective of restoration impacts and can inform future restoration efforts. In this work, we use a consumer-grade UAV, structure-from-motion (SfM) photogrammetry, and machine learning (ML) to evaluate geomorphic and vegetation changes pre-/post-dam removal, and discuss how the technology enhanced our monitoring of the restoration project. We compared UAV evaluation methods to conventional boots-on-ground methods throughout the Bellamy River Reservoir (Dover, NH, USA) pre-/post-dam removal. We used a UAV-based vegetation classification approach that used a support vector machine algorithm and a featureset composed of SfM-derived elevation and visible vegetation index values to map other, herbaceous, shrub, and tree cover throughout the reservoir (overall accuracies from 83% to 100%), mapping vegetation succession as well as colonization of exposed sediments that occurred post-dam removal. We used SfM-derived topography and the vegetation classifications to map erosion and deposition throughout the reservoir, despite its heavily vegetated condition, and estimate volume changes post-removal. Despite some limitations, such as influences of refraction and vegetation on the SfM topography models, UAV provided information on post-dam removal changes that would have gone unacknowledged by the conventional ecological assessment approaches, demonstrating how UAV technology can provide perspective in restoration evaluation even in less-than-ideal site conditions for SfM. For example, the UAV provided perspective of the magnitude and extent of channel shape changes throughout the reservoir while the boots-on-ground topographic transects were not as reliable for detecting change due to difficulties in navigating the terrain. In addition, UAV provided information on vegetation changes throughout the reservoir that would have been missed by conventional vegetation plots due to their limited spatial coverage. Lastly, the COVID-19 pandemic prevented us from meeting to collect post-dam removal vegetation plot data. UAV enabled data collection that we would have foregone if we relied solely on conventional methods, demonstrating the importance of flexible and adaptive methods for successful restoration monitoring such as those enabled via UAV.

A Bird's-Eye View: Novel Use of Drone Images to Quantify Differences in Altitudinal Reflections in Bird-window Collision Studies

Bird-window collisions (BWCs) constitute a significant source of mortality for both resident and migratory birds. Because windows reflect surrounding landscape components, such as vegetation or sky, birds do not always perceive glass as a barrier. Here we demonstrate a novel technique to classify and quantify reflections in windows on the Radford University campus in southwest Virginia, U.S.A. We deployed a consumer-grade Unmanned Aerial Vehicle, i.e., drone, to photograph 14 contiguous or near-contiguous window columns across five campus buildings in August 2020. For each study aspect, the drone (equipped with an RGB camera) captured images at ca. 5 m altitudinal increments from the ground floor to the roof of each building (three to six images/vertical column). We then manually classified each image in ImageJ to calculate approximate proportions of reflected: (1) vegetation, (2) sky, (3) and artificial structures or impervious surfaces, plus (4) nonreflective glass. We used a generalized linear model to determine how proportional reflections of vegetation, sky, buildings, and nonreflective glass varied across vertical increments. We found the proportion of sky significantly decreased with increasing photo heights, whereas proportion of nonreflective glass significantly increased with increasing heights. This supports previous findings that because birds are drawn to sky reflections, they may collide at relatively lower positions on buildings. Inconsistency in landscape design and building positioning on the campus precluded trends in vegetative or building reflections by height. Our pilot study demonstrates the applicability of a consumer-grade drone for investigating visual characteristics of reflections that influence BWCs from variable observation angles. We suggest the expanded use of drone images as a straightforward technique to measure changes in reflection characteristics from varying degrees of observation. They are a novel method in developing a BWC risk assessment as well as potential mitigation strategies in a suburban or campus environment with buildings of intermediate heights.

Implications of Non-Uniform Deadline Scaling to Quality of Service Under Single Errors

Fault-tolerant real-time systems for emerging critical applications like wearable electronic healthcare monitors, consumer-grade unmanned aerial vehicles, or environmental monitoring have to tolerate errors during operation. If they fail, the consequences are dire. But often their budgets for error mitigation capabilities are low, which requires to limit mitigation capabilities to the most critical functions of a system. Still, less critical functions of a system should operate as long as possible, but current state-of-the-art scheduling approaches either ignore them or suffer from low acceptance rates. Our fully static and verification friendly mixed-criticality approach guarantees mentioned systems that the most critical system functions are always available, and maximizes the time where less critical system functions are operational: We prove that our approach is feasible, and extends the system operation time while providing full service by a factor of 1.93 with a probability of 0.92. With 1.56 higher acceptance rates compared to similar state-of-the-art approaches, the integration of functionalities of different criticality in one fault-tolerant system succeeds more and more often, which especially benefits emerging critical applications with limited budgets for error mitigation.