UAS industry growth: Forecasting impact on regional infrastructure, environment, and economy

Wargo, Chris A.; Snipes, Corey; Roy, Aloke; Kerczewski, Robert J.

doi:10.1109/dasc.2016.7778048

Cited by 14 publications

(3 citation statements)

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“…Another complementary technology that is rapidly proliferating and becoming easier to access is commercially available off-the-shelf drone platforms 12 , 13 . Increasingly, Uncrewed Aerial Systems (UASs) are being outfitted with not only RGB sensors to collect overhead imagery, but also with an array of other infrared-related sensors, such as LWIR (7.5 -13.5 µm), to collect valuable multispectral data 14 .…”

Section: Introductionmentioning

confidence: 99%

Assessing thermal imagery integration into object detection methods on air-based collection platforms

Gallagher¹,

Oughton²

2023

Preprint

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<p>Object detection models commonly focus on utilizing the visible spectrum via Red-Green-Blue (RGB) imagery. Due to various limitations with this approach in low visibility settings, there is growing interest in fusing RGB with thermal long wave infrared (LWIR) (7.5 - 13.5 µm) images to increase object detection performance. However, we still lack baseline performance metrics evaluating RGB, LWIR and LWIR-RGB fused object detection machine learning models, especially from air-based platforms. This study undertakes such an evaluation finding that a blended RGB-LWIR model generally exhibits superior performance compared to traditional RGB or LWIR approaches. For example, an RGB-LWIR blend only performed 1-5% behind the RGB approach in predictive power across various altitudes and periods of clear visibility. Yet, RGB fusion with a thermal signature overlayed provides edge redundancy and edge emphasis, both which are vital in supporting edge detection machine learning algorithms. This approach has the ability to improve object detection performance for a range of use cases in industrial, consumer, government, and military applications. Finally, this research additionally contributes a novel open labeled training dataset of 6,300 images for RGB, LWIR, and RGB-LWIR fused imagery, collected from air-based platforms, enabling further multispectral machine-driven object detection research. </p>

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Section: Introductionmentioning

confidence: 99%

Assessing thermal imagery integration into object detection methods on air-based collection platforms

Gallagher¹,

Oughton²

2023

Preprint

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“…In recent years, there has been growing interest in UAVs that are increasingly used in many scenarios [63]. As high-performance, low-cost, and intelligent UAVs have become more affordable and attainable, UAVs have experienced a quick transition from the military to the civilian domain.…”

Section: Introductionmentioning

confidence: 99%

DeepSIM: GPS Spoofing Detection on UAVs using Satellite Imagery Matching

Xue

Niu

Hong

et al. 2020

Annual Computer Security Applications Conference

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“…The use of unmanned aerial vehicles (UAVs) has been increasing exponentially in recent years, and thus they are having a strong economic impact on the global market [1]. Indeed, they can be used for a large variety of military and civilian applications [2], such as border patrol [3], search and rescue [4], infrastructure monitoring [5], package delivery [6] and precision agriculture [7].…”

Section: Introductionmentioning

confidence: 99%

Magnetometer Calibration for Small Unmanned Aerial Vehicles Using Cooperative Flight Data

Opromolla

2020

Sensors

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This paper presents a new method to improve the accuracy in the heading angle estimate provided by low-cost magnetometers on board of small Unmanned Aerial Vehicles (UAVs). This task can be achieved by estimating the systematic error produced by the magnetic fields generated by onboard electric equipment. To this aim, calibration data must be collected in flight when, for instance, the level of thrust provided by the electric engines (and, consequently, the associated magnetic disturbance) is the same as the one occurring during nominal flight operations. The UAV whose magnetometers need to be calibrated (chief) must be able to detect and track a cooperative vehicle (deputy) using a visual camera, while flying under nominal GNSS coverage to enable relative positioning. The magnetic biases’ determination problem can be formulated as a system of non-linear equations by exploiting the acquired visual and GNSS data. The calibration can be carried out either off-line, using the data collected in flight (as done in this paper), or directly on board, i.e., in real time. Clearly, in the latter case, the two UAVs should rely on a communication link to exchange navigation data. Performance assessment is carried out by conducting multiple experimental flight tests.

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UAS industry growth: Forecasting impact on regional infrastructure, environment, and economy

Cited by 14 publications

References 1 publication

Assessing thermal imagery integration into object detection methods on air-based collection platforms

Assessing thermal imagery integration into object detection methods on air-based collection platforms

DeepSIM: GPS Spoofing Detection on UAVs using Satellite Imagery Matching

Magnetometer Calibration for Small Unmanned Aerial Vehicles Using Cooperative Flight Data

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