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A New Passive 3-D Automatic Target Recognition Architecture for Aerial Platforms
The 3-D automatic target recognition (ATR) has many advantages over its 2-D counterpart, but there are several constraints in the context of small low-cost unmanned aerial vehicles (UAVs). These limitations include the requirement for active rather than passive monitoring, high equipment costs, sensor packaging size, and processing burden. We, therefore, propose a new structure from motion (SfM) 3-D ATR architecture that exploits the UAV's onboard sensors, i.e., the visual band camera, gyroscope, and accelerometer, and meets the requirements of a small UAV system. We tested the proposed 3-D SfM ATR using simulated UAV reconnaissance scenarios and found that the performance was better than classic 3-D light detection and ranging (LIDAR) ATR, combining the advantages of 3-D LIDAR ATR and passive 2-D ATR. The main advantages of the proposed architecture include the rapid processing, target pose invariance, small template size, passive scene sensing, and inexpensive equipment. We implemented the SfM module under two keypoint detection, description and matching schemes, with the 3-D ATR module exploiting several current techniques. By comparing SfM 3-D ATR, 3-D LIDAR ATR, and 2-D ATR, we confirmed the superior performance of our new architecture
ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information
Object detection in wide area motion imagery (WAMI) has drawn the attention
of the computer vision research community for a number of years. WAMI proposes
a number of unique challenges including extremely small object sizes, both
sparse and densely-packed objects, and extremely large search spaces (large
video frames). Nearly all state-of-the-art methods in WAMI object detection
report that appearance-based classifiers fail in this challenging data and
instead rely almost entirely on motion information in the form of background
subtraction or frame-differencing. In this work, we experimentally verify the
failure of appearance-based classifiers in WAMI, such as Faster R-CNN and a
heatmap-based fully convolutional neural network (CNN), and propose a novel
two-stage spatio-temporal CNN which effectively and efficiently combines both
appearance and motion information to significantly surpass the state-of-the-art
in WAMI object detection. To reduce the large search space, the first stage
(ClusterNet) takes in a set of extremely large video frames, combines the
motion and appearance information within the convolutional architecture, and
proposes regions of objects of interest (ROOBI). These ROOBI can contain from
one to clusters of several hundred objects due to the large video frame size
and varying object density in WAMI. The second stage (FoveaNet) then estimates
the centroid location of all objects in that given ROOBI simultaneously via
heatmap estimation. The proposed method exceeds state-of-the-art results on the
WPAFB 2009 dataset by 5-16% for moving objects and nearly 50% for stopped
objects, as well as being the first proposed method in wide area motion imagery
to detect completely stationary objects.Comment: Main paper is 8 pages. Supplemental section contains a walk-through
of our method (using a qualitative example) and qualitative results for WPAFB
2009 datase
Automatic vehicle tracking and recognition from aerial image sequences
This paper addresses the problem of automated vehicle tracking and
recognition from aerial image sequences. Motivated by its successes in the
existing literature focus on the use of linear appearance subspaces to describe
multi-view object appearance and highlight the challenges involved in their
application as a part of a practical system. A working solution which includes
steps for data extraction and normalization is described. In experiments on
real-world data the proposed methodology achieved promising results with a high
correct recognition rate and few, meaningful errors (type II errors whereby
genuinely similar targets are sometimes being confused with one another).
Directions for future research and possible improvements of the proposed method
are discussed
Stereo Vision: A Comparison of Synthetic Imagery vs. Real World Imagery for the Automated Aerial Refueling Problem
Missions using unmanned aerial vehicles have increased in the past decade. Currently, there is no way to refuel these aircraft. Accomplishing automated aerial refueling can be made possible using the stereo vision system on a tanker. Real world experiments for the automated aerial refueling problem are expensive and time consuming. Currently, simulations performed in a virtual world have shown promising results using computer vision. It is possible to use the virtual world as a substitute environment for the real world. This research compares the performance of stereo vision algorithms on synthetic and real world imagery
UAV-Enabled Surface and Subsurface Characterization for Post-Earthquake Geotechnical Reconnaissance
Major earthquakes continue to cause significant damage to infrastructure systems and the loss of life (e.g. 2016 Kaikoura, New Zealand; 2016 Muisne, Ecuador; 2015 Gorkha, Nepal). Following an earthquake, costly human-led reconnaissance studies are conducted to document structural or geotechnical damage and to collect perishable field data. Such efforts are faced with many daunting challenges including safety, resource limitations, and inaccessibility of sites. Unmanned Aerial Vehicles (UAV) represent a transformative tool for mitigating the effects of these challenges and generating spatially distributed and overall higher quality data compared to current manual approaches. UAVs enable multi-sensor data collection and offer a computational decision-making platform that could significantly influence post-earthquake reconnaissance approaches. As demonstrated in this research, UAVs can be used to document earthquake-affected geosystems by creating 3D geometric models of target sites, generate 2D and 3D imagery outputs to perform geomechanical assessments of exposed rock masses, and characterize subsurface field conditions using techniques such as in situ seismic surface wave testing. UAV-camera systems were used to collect images of geotechnical sites to model their 3D geometry using Structure-from-Motion (SfM). Key examples of lessons learned from applying UAV-based SfM to reconnaissance of earthquake-affected sites are presented. The results of 3D modeling and the input imagery were used to assess the mechanical properties of landslides and rock masses. An automatic and semi-automatic 2D fracture detection method was developed and integrated with a 3D, SfM, imaging framework. A UAV was then integrated with seismic surface wave testing to estimate the shear wave velocity of the subsurface materials, which is a critical input parameter in seismic response of geosystems. The UAV was outfitted with a payload release system to autonomously deliver an impulsive seismic source to the ground surface for multichannel analysis of surface waves (MASW) tests. The UAV was found to offer a mobile but higher-energy source than conventional seismic surface wave techniques and is the foundational component for developing the framework for fully-autonomous in situ shear wave velocity profiling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145793/1/wwgreen_1.pd
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
Improved LANDSAT to give better view of earth resources
The launch data of LANDSAT 3 is announced. The improved capability of the spacecrafts' remote sensors (the return beam vidicon and the multispectral scanner) and application of LANDSAT data to the study of energy supplies, food production, and global large-scale environmental monitoring are discussed along with the piggyback amateur radio communication satellite-OSCAR-D, the plasma Interaction Experiment, and the data collection system onboard LANDSAT 3. An assessment of the utility of LANDSAT multispectral data is given based on the research results to data from studies of LANDSAT 1 and 2 data. Areas studied include agriculture, rangelands, forestry, water resources, environmental and marine resources, environmental and marine resources, cartography, land use, demography, and geological surveys and mineral/petroleum exploration
Virtual Testbed for Monocular Visual Navigation of Small Unmanned Aircraft Systems
Monocular visual navigation methods have seen significant advances in the
last decade, recently producing several real-time solutions for autonomously
navigating small unmanned aircraft systems without relying on GPS. This is
critical for military operations which may involve environments where GPS
signals are degraded or denied. However, testing and comparing visual
navigation algorithms remains a challenge since visual data is expensive to
gather. Conducting flight tests in a virtual environment is an attractive
solution prior to committing to outdoor testing.
This work presents a virtual testbed for conducting simulated flight tests
over real-world terrain and analyzing the real-time performance of visual
navigation algorithms at 31 Hz. This tool was created to ultimately find a
visual odometry algorithm appropriate for further GPS-denied navigation
research on fixed-wing aircraft, even though all of the algorithms were
designed for other modalities. This testbed was used to evaluate three current
state-of-the-art, open-source monocular visual odometry algorithms on a
fixed-wing platform: Direct Sparse Odometry, Semi-Direct Visual Odometry, and
ORB-SLAM2 (with loop closures disabled)
Image Exploitation-A Forefront Area for UAV Application
Image exploitation, an innovative image utilisation program uses high revisit multisensor, multiresolution imagery from unmanned air vehicle or other reconnaissance platform for intelligent information gathering. This paper describes the imagc exploitation system developed at the Aeronautical Dcvclopment Establishment, Bangalore, for the remotely piloted vehicle (RPV) Nishonr and highlights two major areas (i) In-flight imagc exploitation, and (ii) post-flight imagc cxploitatlon. In-flight imagc study includes real-timeenhancement of images frames during RPV flight. target acquisition. calculation of geo-location of targets, distance and area computation, and image-to-map correspondence. Post-flight image exploitation study includes image restoration, classtfication of terrain, 3-D depth computation using stereo vision and shape from shading techniques. The paper shows results obtained in each of these areas from actual flight trials
The Design Fabrication and Flight Testing of an Academic Research Platform for High Resolution Terrain Imaging
This thesis addresses the design, construction, and flight testing of an Unmanned Aircraft System (UAS) created to serve as a testbed for Intelligence, Surveillance, and Reconnaissance (ISR) research topics that require the rapid acquisition and processing of high resolution aerial imagery and are to be performed by academic research institutions. An analysis of the requirements of various ISR research applications and the practical limitations of academic research yields a consolidated set of requirements by which the UAS is designed. An iterative design process is used to transition from these requirements to cycles of component selection, systems integration, flight tests, diagnostics, and subsystem redesign. The resulting UAS is designed as an academic research platform to support a variety of ISR research applications ranging from human machine interaction with UAS technology to orthorectified mosaic imaging. The lessons learned are provided to enable future researchers to create similar systems
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