We present an air-to-airmulti-sensor andmulti-viewfixed-wingUAVdataset, MMFW-UAV, in this work. MMFW-UAV contains a total of 147,417 fixed-wing UAVs images captured by multiple types of sensors (zoom, wide-angle, and thermal imaging sensors), displaying the flight status of fixed-wing ...
that provides attitude estimates for a fixed wing UAV. The key contribution is to develop a model of the non-inertial acceleration of the airframe that can be used to compensate the accelerometer output to obtain a zero bias estimate of ...
The main objective of this research contribution is to combine the Digital Elevation Model (DEMs) from quadcopter Unmanned Aerial Vehicles (UAVs), Fixed Wing UAV-based cameras, and iPhone datasets for the forest plots. The datasets from two ...
Our approach proposes a pipeline consisting of a Convolutional Neural Network (CNN)-based detection of UAV anchors which, in turn, drives the estimation of UAV pose. In order to ensure robust and precise anchor detection, we designed a Block-CNN architecture to mitigate the influenc...
UAV data setPurpose – The purpose of this paper is to present a localization and mapping data set acquired by a fixed-wing unmanned aerial vehicle (UAV). The data set was collected for educational and research purposes: to save time in dealing with hardware and to compare the results with...
Regarding this, this work evaluates products from digital photogrammetry from images acquired with a fixed-wing UAV (18Mpixel camera) in a 300-380m height flight over a Hydroelectric Power Plant (HPP) in Brazil. A dataset of 23 ground control points assessed with an RTK-GNSS (using natural ...
The quantification of the volume of the eroded-accumulated material (using the M3C2 distances and the two UAV datasets) resulted in significant differences as the fixed-wing underestimated the values calculated using the multirotor dataset. The 2.5D stra...
The quantification of the volume of the eroded-accumulated material (using the M3C2 distances and the two UAV datasets) resulted in significant differences as the fixed-wing underestimated the values calculated using the multirotor dataset. The 2.5D strategies used to quantify the volume of change ...
The areas were flown with a Fixed-Wing UAV and three sensors (RGB, multispectral, thermal). A multisensor dataset consisting of the sensor data, plant height and five spectral indices was classified with a random forest algorithm. The classification accuracies were 87.1 and 89.0% for 10 and ...
Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, ...