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Design and Implementation of a Camera-Based Tracking System for MAV Using Deep Learning Algorithms

  • In recent years, the advancement of micro-aerial vehicles has been rapid, leading to their widespread utilization across various domains due to their adaptability and efficiency. This research paper focuses on the development of a camera-based tracking system specifically designed for low-cost drones. The primary objective of this study is to build up a system capable of detecting objects andIn recent years, the advancement of micro-aerial vehicles has been rapid, leading to their widespread utilization across various domains due to their adaptability and efficiency. This research paper focuses on the development of a camera-based tracking system specifically designed for low-cost drones. The primary objective of this study is to build up a system capable of detecting objects and locating them on a map in real time. Detection and positioning are achieved solely through the utilization of the drone’s camera and sensors. To accomplish this goal, several deep learning algorithms are assessed and adopted because of their suitability with the system. Object detection is based upon a single-shot detector architecture chosen for maximum computation speed, and the tracking is based upon the combination of deep neural-network-based features combined with an efficient sorting strategy. Subsequently, the developed system is evaluated using diverse metrics to determine its performance for detection and tracking. To further validate the approach, the system is employed in the real world to show its possible deployment. For this, two distinct scenarios were chosen to adjust the algorithms and system setup: a search and rescue scenario with user interaction and precise geolocalization of missing objects, and a livestock control scenario, showing the capability of surveying individual members and keeping track of number and area. The results demonstrate that the system is capable of operating in real time, and the evaluation verifies that the implemented system enables precise and reliable determination of detected object positions. The ablation studies prove that object identification through small variations in phenotypes is feasible with our approach.show moreshow less

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Metadaten
Document Type:Article (reviewed)
Zitierlink: https://opus.hs-offenburg.de/8283
Bibliografische Angaben
Title (English):Design and Implementation of a Camera-Based Tracking System for MAV Using Deep Learning Algorithms
Author:Stefan HenselStaff MemberORCiDGND, Marin B. Marinov, Raphael Panter
Year of Publication:2023
Place of publication:Basel
Publisher:MDPI
First Page:1
Last Page:26
Article Number:244
Parent Title (English):Computation
Volume:11
Issue:12
ISSN:2079-3197
DOI:https://doi.org/10.3390/computation11120244
URN:https://urn:nbn:de:bsz:ofb1-opus4-82838
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Forschung / IMLA - Institute for Machine Learning and Analytics
Collections of the Offenburg University:Bibliografie
Tag:Deep Learning
Formale Angaben
Relevance for "Jahresbericht über Forschungsleistungen":Wiss. Zeitschriftenartikel reviewed: Listung in Master Journal List
Open Access: Open Access 
 Gold 
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International