scholarly journals Evaluation of an unmanned aircraft system for detecting surrogate caribou targets in Labrador

2016 ◽  
Vol 4 (1) ◽  
pp. 53-69 ◽  
Author(s):  
Charla Patterson ◽  
William Koski ◽  
Paul Pace ◽  
Brian McLuckie ◽  
David M. Bird
Keyword(s):  
Target Detection ◽  
Rangifer Tarandus ◽  
Survey Methods ◽  
Habitat Type ◽  
Flight Time ◽  
Unmanned Aircraft ◽  
Aerial Images ◽  
Line Of Sight ◽  
Unmanned Aerial Systems ◽  
Aerial Surveys

Regular, standardized population inventories have been suggested as an important component to the recovery of declining populations of boreal caribou (Rangifer tarandus caribou). Current survey methods typically employ manned aircraft, which can be noisy, expensive to operate, and dangerous for the people conducting the surveys. Small unmanned aerial systems (UAS) have garnered attention as a promising alterative to conducting aerial surveys in manned aircraft. Our research investigates the feasibility of using an UAS to conduct aerial surveys and determine which factors affect the detection of surrogate caribou targets, and hence may affect detection of real caribou. In the fall of 2013, we tested the capabilities of the Brican TD100E, a small, electric-powered fixed-wing UAS, to fly beyond visual line of sight near Goose Bay, Labrador. Seven surveys were done using different flight paths to collect aerial images of 26 surrogate caribou targets placed in six different habitats. Mixed effects logistic regression models were used to evaluate how habitat type, distance of the target from the image centerline, photo analysts’ experience level, flight time, and the target contrast against the landscape influenced the detection of surrogate caribou targets. We found that habitat type, target contrast, and the flight time affected target detection. Overall, 77.5% of the targets were detected; the odds of a photo analyst detecting a target in open habitat were roughly 10.5 times higher than in burned habitat and 42 times higher than in heavy forest. Target detection was influenced by the contrast of the target against the landscape, where a higher corrected integrated density was associated with greater target detection. The detection of targets was 87% during evening flights and 75% for morning flights. This study was the first of its kind to successfully fly a UAS beyond line of sight over land for non-military applications in North America and the findings of our research will provide an evaluation for using UAS to survey caribou in the future.

scholarly journals Surveying Wild Animals from Satellites, Manned Aircraft and Unmanned Aerial Systems (UASs): A Review

2019 ◽  
Vol 11 (11) ◽  
pp. 1308 ◽  
Author(s):  
Dongliang Wang ◽  
Quanqin Shao ◽  
Huanyin Yue
Keyword(s):  
High Resolution ◽  
Satellite Image ◽  
Wild Animal ◽  
Unmanned Aircraft ◽  
Image Resolution ◽  
Detection Methods ◽  
Unmanned Aerial Systems ◽  
Wild Animals ◽  
Small Areas ◽  
Aerial Surveys

This article reviews studies regarding wild animal surveys based on multiple platforms, including satellites, manned aircraft, and unmanned aircraft systems (UASs), and focuses on the data used, animal detection methods, and their accuracies. We also discuss the advantages and limitations of each type of remote sensing data and highlight some new research opportunities and challenges. Submeter very-high-resolution (VHR) spaceborne imagery has potential in modeling the population dynamics of large (>0.6 m) wild animals at large spatial and temporal scales, but has difficulty discerning small (<0.6 m) animals at the species level, although high-resolution commercial satellites, such as WorldView-3 and -4, have been able to collect images with a ground resolution of up to 0.31 m in panchromatic mode. This situation will not change unless the satellite image resolution is greatly improved in the future. Manned aerial surveys have long been employed to capture the centimeter-scale images required for animal censuses over large areas. However, such aerial surveys are costly to implement in small areas and can cause significant disturbances to wild animals because of their noise. In contrast, UAS surveys are seen as a safe, convenient and less expensive alternative to ground-based and conventional manned aerial surveys, but most UASs can cover only small areas. The proposed use of UAS imagery in combination with VHR satellite imagery would produce critical population data for large wild animal species and colonies over large areas. The development of software systems for automatically producing image mosaics and recognizing wild animals will further improve survey efficiency.

Development of Rotary Wing Mini UAS for Civilian Applications

2013 ◽  
Vol 01 (02) ◽  
pp. 247-258 ◽  
Author(s):  
K. Senthil Kumar ◽  
A. Mohamed Rasheed
Keyword(s):  
Tamil Nadu ◽  
Field Testing ◽  
Unmanned Aircraft ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Test Bed ◽  
Institute Of Technology ◽  
Aircraft System ◽  
Religious Festival ◽  
Rotary Wing

This research paper is about team Dhaksha's accomplishment in designing, developing and testing a slew of Rotary Wing Mini Unmanned Aerial Systems for entry into various international aerial robotics/unmanned aerial vehicle (UAV) competitions and civilian applications. Dhaksha, the Unmanned Aircraft System (UAS), developed by the team at Madras Institute of Technology (MIT) campus of Anna University, Chennai, Tamil Nadu, India, with its stable design presented stiff competition to other contestants during the May 2012 technology demonstration called UAVForge organized by Defense Advanced Research Project Agency (DARPA), Department of Defense, USA. Team Dhaksha on behalf of national defense research agency deployed their system as a test bed for Acoustic field testing and analysis. The team also deployed their UAS, for state police, in a religious festival, over a crowd of 20 lakh pilgrims during November 2012, to avail instant aerial images. UAS Dhaksha was deployed for investigation of structural strength of the India's tallest structure, a 300 m high Reinforced Cement Concrete (RCC) tower. Recently during the flash floods in the Himalayan river Mandakini at an altitude of 4200 m above mean sea level, Dhaksha assisted the forces in the relief and rescue operations by providing instant thermal/video images of the scene of disaster.

scholarly journals Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2834
Author(s):  
Billur Kazaz ◽  
Subhadipto Poddar ◽  
Saeed Arabi ◽  
Michael A. Perez ◽  
Anuj Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Pollution Prevention ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Construction Sites ◽  
State Regulations ◽  
Novel Approach ◽  
Model Training ◽  
Aerial Systems

Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices.

scholarly journals Method to Characterize Potential UAS Encounters Using Open Source Data

Aerospace ◽  
2020 ◽  
Vol 7 (11) ◽  
pp. 158
Author(s):  
Andrew Weinert
Keyword(s):  
Open Source ◽  
Unmanned Aircraft ◽  
Unmanned Aerial Systems ◽  
Horizontal Distance ◽  
Geospatial Information ◽  
Open Source Data ◽  
Source Data ◽  
Aircraft Trajectories ◽  
Processing Techniques ◽  
Aerial Systems

As unmanned aerial systems (UASs) increasingly integrate into the US national airspace system, there is an increasing need to characterize how commercial and recreational UASs may encounter each other. To inform the development and evaluation of safety critical technologies, we demonstrate a methodology to analytically calculate all potential relative geometries between different UAS operations performing inspection missions. This method is based on a previously demonstrated technique that leverages open source geospatial information to generate representative unmanned aircraft trajectories. Using open source data and parallel processing techniques, we performed trillions of calculations to estimate the relative horizontal distance between geospatial points across sixteen locations.

scholarly journals Experimental Comparison of Direct and Active Throttle Control of a 7 kW Turboelectric Power System for Unmanned Aircraft

2021 ◽  
Vol 11 (22) ◽  
pp. 10608
Author(s):  
Johnathan Burgess ◽  
Timothy Runnels ◽  
Joshua Johnsen ◽  
Joshua Drake ◽  
Kurt Rouser
Keyword(s):  
Transient Behavior ◽  
Unmanned Aircraft ◽  
Experimental Comparison ◽  
Small Scale ◽  
Unmanned Aerial Systems ◽  
Operational Parameters ◽  
Turbine Engine ◽  
Control Scheme ◽  
Aerial Systems ◽  
Electric Loads

This article compares direct turbine throttle control and active turbine throttle control for a turboelectric system; the featured turboprop is rated for 7 kW of shaft output power. The powerplant is intended for applications in unmanned aerial systems and requires a control system to produce different amounts of power for varying mission legs. The most straightforward control scheme explored is direct turbine control, which is characterized by the pilot controlling the throttle of the turbine engine. In contrast, active control is characterized by the turbine reacting to the power demanded by the electric motors or battery recharge cycle. The transient response to electric loads of a small-scale turboelectric system is essential in identifying and characterizing such a system’s safe operational parameters. This paper directly compares the turbogenerator’s transient behavior to varying electric loads and categorizes its dynamic response. A proportional, integral, and derivative (PID) control algorithm was utilized as an active throttle controller through a microcontroller with battery power augmentation for the turboelectric system. This controller manages the turbine’s throttle reactions in response to any electric load when applied or altered. By comparing the system’s response with and without the controller, the authors provide a method to safely minimize the response time of the active throttle controller for use in the real-world environment of unmanned aircraft.

Development and Flight Validation of Proposed Unmanned Aerial System Handling Qualities Requirements

2021 ◽  
Author(s):  
Christina M. Ivler ◽  
Kevin Truong ◽  
Declan Kerwin ◽  
Joel Otomize ◽  
Danielle Parmer ◽  
...  
Keyword(s):  
Large Scale ◽  
Performance Standards ◽  
Unmanned Aircraft ◽  
Performance Standard ◽  
Great Promise ◽  
Unmanned Aerial Systems ◽  
Dynamic Scaling ◽  
Scoring Method ◽  
Handling Qualities ◽  
Aerial Systems

Unmanned aerial systems, commonly known as drones, present new opportunities to perform autonomous tasks. Handling qualities requirements for manned vertical lift aircraft have been well defined and documented. The need to define handling qualities requirements for vertical take-off and landing (VTOL) unmanned aerial systems (UAS) to meet mission demands is of paramount importance for all potential operators and procurement agencies. One way to relate handling qualities specifications of large-scale manned and subscale unmanned aircraft is through Froude dynamic scaling. Froude dynamic scaling based on hub-to-hub distance has shown great promise in relating the natural frequencies of scaled multicopters. There have been recent efforts to develop a VTOL-UAS handling qualities standard by scaling mission task elements and rating their performance through a Trajectory, Tracking, and Aggression (TTA) score. This paper proposes a new performance standard adapted from the TTA scoring method, along with a modified Cooper–Harper scale as a VTOL-UAS handling qualities framework that is consistent with the spirit of Aeronautical Design Standard 33 (ADS-33). These newly proposed performance standards were then validated through simulation and flight testing on a small hexacopter UAS, flown at the University of Portland. A key outcome of this work is the flight verification of a key dynamic response metric, the disturbance rejection bandwidth, and associated validation of Froude scaling for predicted handling qualities metrics.

Situating Hobby Drone Practices

2017 ◽  
Vol 3 (2) ◽  
pp. 207-218 ◽  
Author(s):  
Julia M. Hildebrand
Keyword(s):  
Social Relations ◽  
Social Networking Sites ◽  
Physical Space ◽  
Weather Conditions ◽  
Unmanned Aircraft ◽  
Aerial Images ◽  
Ethnographic Research ◽  
Location Awareness ◽  
Common Reference ◽  
Low Profile

Abstract Consumer drones are entering everyday spaces with increasing frequency and impact as more and more hobbyists use the aerial tool for recreational photography and videography. In this article, I seek to expand the common reference to drones as “unmanned aircraft systems” by conceptualising the hobby drone practice more broadly as a heterogeneous, mobile assemblage of virtual and physical practices and human and non-human actors. Drawing on initial ethnographic fieldwork and interviews with drone hobbyists as well as ongoing cyber-ethnographic research on social networking sites, this article gives an overview of how the mobile drone practice needs to be situated alongside people, things, and data in physical and virtual spheres. As drone hobbyists set out to fly their devices at a given time and place, a number of relations reaching across atmospheric (e. g. weather conditions, daylight hours, GPS availability), geographic (e. g. volumetric obstacles), mobile (e. g. flight restrictions, ground traffic), and social (e. g. bystanders) dimensions demand attention. Furthermore, when drone operators share their aerial images online, visual (e. g. live stream) and cyber-social relations (e. g. comments, scrutiny) come into play, which may similarly impact the drone practice in terms of the pilot’s performance. While drone hobbysists appear to be interested in keeping a “low profile” in the physical space, many pilots manage a comparatively “high profile” in the virtual sphere with respect to the sharing of their images. Since the recreational trend brings together elements of convergence, location-awareness, and real-time feedback, I suggest approaching consumer drones as, what Scott McQuire (2016) terms, “geomedia.” Moreover, consumer drones open up different “cybermobilities” (Adey/Bevan 2006) understood as connected movement that flows through and shapes both physical and virtual spaces simultaneously. The way that many drone hobbyists appear to navigate these different environments, sometimes at the same time, has methodological implications for ethnographic research on consumer drones. Ultimately, the assemblage-perspective brings together aviation-related and socio-cultural concerns relevant in the context of consumer drones as digital communication technology and visual production tool.

scholarly journals Guidelines for the Use of Unmanned Aerial Systems in Flood Emergency Response

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 521 ◽  
Author(s):  
Gloria Salmoral ◽  
Monica Rivas Casado ◽  
Manoranjan Muthusamy ◽  
David Butler ◽  
Prathyush Menon ◽  
...  
Keyword(s):  
Risk Management ◽  
Emergency Response ◽  
Flood Risk ◽  
Online Survey ◽  
Practical Experience ◽  
Flood Risk Management ◽  
Unmanned Aircraft ◽  
Unmanned Aerial Systems ◽  
Effective Response ◽  
Deployment Analysis

There is increasing interest in using Unmanned Aircraft Systems (UAS) in flood risk management activities including in response to flood events. However, there is little evidence that they are used in a structured and strategic manner to best effect. An effective response to flooding is essential if lives are to be saved and suffering alleviated. This study evaluates how UAS can be used in the preparation for and response to flood emergencies and develops guidelines for their deployment before, during and after a flood event. A comprehensive literature review and interviews, with people with practical experience of flood risk management, compared the current organizational and operational structures for flood emergency response in both England and India, and developed a deployment analysis matrix of existing UAS applications. An online survey was carried out in England to assess how the technology could be further developed to meet flood emergency response needs. The deployment analysis matrix has the potential to be translated into an Indian context and other countries. Those organizations responsible for overseeing flood risk management activities including the response to flooding events will have to keep abreast of the rapid technological advances in UAS if they are to be used to best effect.

scholarly journals Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 583 ◽  
Author(s):  
Khang Nguyen ◽  
Nhut T. Huynh ◽  
Phat C. Nguyen ◽  
Khanh-Duy Nguyen ◽  
Nguyen D. Vo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Unmanned Aircraft ◽  
Aerial Images ◽  
Great Success ◽  
Single Shot ◽  
Convolutional Networks ◽  
Image Pyramids ◽  
Fully Convolutional Networks ◽  
Wide Range

Unmanned aircraft systems or drones enable us to record or capture many scenes from the bird’s-eye view and they have been fast deployed to a wide range of practical domains, i.e., agriculture, aerial photography, fast delivery and surveillance. Object detection task is one of the core steps in understanding videos collected from the drones. However, this task is very challenging due to the unconstrained viewpoints and low resolution of captured videos. While deep-learning modern object detectors have recently achieved great success in general benchmarks, i.e., PASCAL-VOC and MS-COCO, the robustness of these detectors on aerial images captured by drones is not well studied. In this paper, we present an evaluation of state-of-the-art deep-learning detectors including Faster R-CNN (Faster Regional CNN), RFCN (Region-based Fully Convolutional Networks), SNIPER (Scale Normalization for Image Pyramids with Efficient Resampling), Single-Shot Detector (SSD), YOLO (You Only Look Once), RetinaNet, and CenterNet for the object detection in videos captured by drones. We conduct experiments on VisDrone2019 dataset which contains 96 videos with 39,988 annotated frames and provide insights into efficient object detectors for aerial images.

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