scholarly journals Field tests of photogrammetric evaluation technique of snow-glacial surface albedo

2019 ◽  
pp. 18-28
Author(s):  
D. M. Zhuravskiy ◽  
U. V. Prokhorova ◽  
B. V. Ivanov ◽  
A. S. Yanjura ◽  
N. M. Kuprikov ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Surface Albedo ◽  
Field Tests ◽  
Extreme Conditions ◽  
Evaluation Technique ◽  
Recording Equipment ◽  
Original Technique ◽  
Aerial Vehicle

The article discusses the results of applying in Antarctica an original technique for estimating albedo from photogrammetric data and exposure parameters by an unmanned aerial vehicle (UAV). The complexities of the photogrammetric observations under extreme conditions are considered. Conclusions are drawn on ways to improve the recording equipment and the direction of improving the technique for calculating albedo values based on photogrammetric materials and metadata.

scholarly journals Airborne System for Pipeline Surveillance Using an Unmanned Aerial Vehicle

2020 ◽  
Vol 5 (2) ◽  
pp. 178-182
Author(s):  
Mbadiwe Samuel Benyeogor ◽  
Adeboye Olatunbosun ◽  
Sushant Kumar
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Oil And Gas ◽  
Field Tests ◽  
Oil Industry ◽  
Leakage Detection ◽  
Liquefied Petroleum Gas ◽  
Gas Leakage ◽  
Remote Sensors ◽  
Aerial Vehicle ◽  
Asset Monitoring

Our work involves the development of a quadcopter Unmanned Aerial Vehicle (UAV) system with remote sensors onboard for monitoring oil and gas pipelines. Two Liquefied Petroleum Gas (LPG) sensors were used for LPG gas leakage detection. The Multiwii software is used to control, track and simulate the 3D motion of the UAV in flight. Using this device, experimental data from field tests were analyzed with MATLAB. Results reveal that the developed system has performed as expected. Thus, our device can be used to enhance asset monitoring and operational safety in the oil industry.

Subsurface targets detection using Faster R-CNN for Unmanned Aerial Vehicle Magnetic Survey

2020 ◽  
Author(s):  
Yaoxin Zheng ◽  
Xiaojuan Zhang ◽  
Yaxin Mu ◽  
Wupeng Xie
Keyword(s):  
Feature Extraction ◽  
Unmanned Aerial Vehicle ◽  
Data Augmentation ◽  
Field Tests ◽  
Magnetic Survey ◽  
Positional Error ◽  
Buried Objects ◽  
Object Proposal ◽  
Proposal A ◽  
Aerial Vehicle

<p>Unmanned Aerial Vehicle (UAV) has become a viable platform for magnetic surveys, but the interference generated during flight and lack of the interpretation method for survey data limits its application. In this paper, we present a structure of a half-fixed boom for the UAV-magnetometer system. Compared to suspend the magnetometer on a long rope or cable, our new structure reduces interference and positional error meanwhile increases flight stability. The interference field was removed through compensation based on leveling, with root mean square error significantly reduced from 2.7889 nT to 0.2809 nT. The Faster R-CNN network was adapted for the detection of subsurface buried objects (i.e. Unexploded Ordnance) in UAV magnetic surveys, our Faster R-CNN object detection network is composed of a feature extraction network followed by two subnetworks, the feature extraction network we use is a pre-trained CNN called ResNet-50, the first subnetwork is a region proposal network (RPN) and the second subnetwork is trained to predict the actual class of each object proposal. A labeled dataset that contains 740 images was used for training and each image contains one or more labeled instances of mag anomaly, data augmentation is used by randomly flipping the image and associated box labels horizontally to improve network accuracy, the trained object detector was evaluated on both simulated and field test images. All implementations in this work were accomplished through MATLAB Deep Learning Toolbox using a PC with a GPU compute capability 7.5. Preliminary results reveal that the proposed technique can automatically confirm the number of subsurface targets, in the meantime results from different field tests show its robustness. Significant improvements have made compared to traditional computer vision methods and hence become quite promising to be applied in the field of UAV magnetic survey.</p>

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

Results of Spatial Structure of Atmosphere Radiation in a Spectral Range (1.5–2) µm Research

2018 ◽  
pp. 7-13
Author(s):  
Anton M. Mishchenko ◽  
Sergei S. Rachkovsky ◽  
Vladimir A. Smolin ◽  
Igor V . Yakimenko
Keyword(s):  
Spatial Structure ◽  
Unmanned Aerial Vehicle ◽  
Wavelength Range ◽  
Spectral Range ◽  
Near Ir ◽  
Optoelectronic Systems ◽  
Aerial Vehicle

Results of experimental studying radiation spatial structure of atmosphere background nonuniformities and of an unmanned aerial vehicle being the detection object are presented. The question on a possibility of its detection using optoelectronic systems against the background of a cloudy field in the near IR wavelength range is also considered.

Positioning the Angle of Attack Sensor on an Unmanned Aerial Vehicle Wing

2016 ◽  
Vol 3 (1) ◽  
pp. 25-33
Author(s):  
Amir Birjandi ◽  
◽  
Valentin Guerry ◽  
Eric Bibeau ◽  
Hamidreza Bolandhemmat ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Angle Of Attack ◽  
Aerial Vehicle
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