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Airborne Unmanned Sensor System for UAVs
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Airborne Unmanned Sensor System for UAVs

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (15 September 2024) | Viewed by 2406

Special Issue Editors

Prof. Dr. Hyo-Sang Shin

E-Mail Website
Guest Editor
Autonomous and Intelligent Systems Group, Centre for Autonomous and Cyberphysical Systems, Cranfield University, Cranfield MK43 0AL, UK
Interests: unmanned aerial vehicles; decision making on multi-agent systems; distributed sensing and estimation; data-centric guidance and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shaoming He

E-Mail Website
Guest Editor
College of Aerospace, Beijing Institute of Technology, Beijing 100081, China
Interests: guidance of aerial vehicles; decision making; application of artificial intelligence in aerospace
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The proliferation of low-cost, lightweight, and power-efficient sensors, in combination with advances in networked systems, has enabled the use of multiple sensors in UAVs to accomplish different missions, including environmental monitoring, habitat monitoring, airborne target tracking, situation awareness, etc. These advances have permitted the use of multiple UAVs to cooperatively perform large-scale sensing tasks which would otherwise be difficult to accomplish by individually operating these sensing devices. The challenge of operating low-cost sensors, e.g., visual camera, infrared/laser range finder, acoustic sensor, etc., is that they are likely to contain some degree of uncertainties and usually have limited spatial coverage, communication and computation capabilities. Modern technologies such as model-/data-driven estimation, heterogeneous data fusion, optimization, and artificial intelligence can improve the performance of using low-cost onboard sensors. This Special Issue aims to identify recent theoretical and technical advances in airborne unmanned sensor systems for UAVs. Related topics include, but are not limited to:

  • Airborne target tracking in cluttered environments;
  • Airborne target tracking in GPS-denied environments;
  • Integrated tracking and searching in unknown environments;
  • Distributed multi-sensor fusion;
  • Sensor management and UAV trajectory optimization;
  • Sensor bias calibration;
  • Integrated target tracking and calibration;
  • Scalable target(s) tracking algorithm;
  • Applied artificial intelligence in target tracking.

Prof. Dr. Hyo-sang Shin
Prof. Dr. Shaoming He
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (2 papers)

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Research

14 pages, 1818 KiB  
Article
An Experimental Methodology for Automated Detection of Surface Turbulence Features in Tidal Stream Environments
by James Slingsby, Beth E. Scott, Louise Kregting, Jason McIlvenny, Jared Wilson, Fanny Helleux and Benjamin J. Williamson
Sensors 2024, 24(19), 6170; https://doi.org/10.3390/s24196170 - 24 Sep 2024
Viewed by 555
Abstract
Tidal stream environments are important areas of marine habitat for the development of marine renewable energy (MRE) sources and as foraging hotspots for megafaunal species (seabirds and marine mammals). Hydrodynamic features can promote prey availability and foraging efficiency that influences megafaunal foraging success [...] Read more.
Tidal stream environments are important areas of marine habitat for the development of marine renewable energy (MRE) sources and as foraging hotspots for megafaunal species (seabirds and marine mammals). Hydrodynamic features can promote prey availability and foraging efficiency that influences megafaunal foraging success and behaviour, with the potential for animal interactions with MRE devices. Uncrewed aerial vehicles (UAVs) offer a novel tool for the fine-scale data collection of surface turbulence features and animals, which is not possible through other techniques, to provide information on the potential environmental impacts of anthropogenic developments. However, large imagery datasets are time-consuming to manually review and analyse. This study demonstrates an experimental methodology for the automated detection of turbulence features within UAV imagery. A deep learning architecture, specifically a Faster R-CNN model, was used to autonomously detect kolk-boils within UAV imagery of a tidal stream environment. The model was trained on pre-existing, labelled images of kolk-boils that were pre-treated using a suite of image enhancement techniques based on the environmental conditions present within each image. A 75-epoch model variant provided the highest average recall and precision values; however, it appeared to be limited by sub-optimal detections of false positive values. Although further development is required, including the creation of standardised image data pools, increased model benchmarking and the advancement of tailored pre-processing techniques, this work demonstrates the viability of utilising deep learning to automate the detection of surface turbulence features within a tidal stream environment. Full article
(This article belongs to the Special Issue Airborne Unmanned Sensor System for UAVs)
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14 pages, 1964 KiB  
Article
Utility of Spectral Filtering to Improve the Reliability of Marine Fauna Detections from Drone-Based Monitoring
by Andrew P. Colefax, Andrew J. Walsh, Cormac R. Purcell and Paul Butcher
Sensors 2023, 23(22), 9193; https://doi.org/10.3390/s23229193 - 15 Nov 2023
Cited by 2 | Viewed by 1263
Abstract
Monitoring marine fauna is essential for mitigating the effects of disturbances in the marine environment, as well as reducing the risk of negative interactions between humans and marine life. Drone-based aerial surveys have become popular for detecting and estimating the abundance of large [...] Read more.
Monitoring marine fauna is essential for mitigating the effects of disturbances in the marine environment, as well as reducing the risk of negative interactions between humans and marine life. Drone-based aerial surveys have become popular for detecting and estimating the abundance of large marine fauna. However, sightability errors, which affect detection reliability, are still apparent. This study tested the utility of spectral filtering for improving the reliability of marine fauna detections from drone-based monitoring. A series of drone-based survey flights were conducted using three identical RGB (red-green-blue channel) cameras with treatments: (i) control (RGB), (ii) spectrally filtered with a narrow ‘green’ bandpass filter (transmission between 525 and 550 nm), and, (iii) spectrally filtered with a polarising filter. Video data from nine flights comprising dolphin groups were analysed using a machine learning approach, whereby ground-truth detections were manually created and compared to AI-generated detections. The results showed that spectral filtering decreased the reliability of detecting submerged fauna compared to standard unfiltered RGB cameras. Although the majority of visible contrast between a submerged marine animal and surrounding seawater (in our study, sites along coastal beaches in eastern Australia) is known to occur between 515–554 nm, isolating the colour input to an RGB sensor does not improve detection reliability due to a decrease in the signal to noise ratio, which affects the reliability of detections. Full article
(This article belongs to the Special Issue Airborne Unmanned Sensor System for UAVs)
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