YOLO series algorithms in object detection of unmanned aerial vehicles: a survey

YOLO series algorithms are widely used in unmanned aerial vehicles (UAV) object detection scenarios due to their fast and lightweight properties. This article summarizes the key concepts in YOLO series algorithms, such as the anchor mechanism, feature fusion strategy, bounding box regression loss an...

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Veröffentlicht in:Service oriented computing and applications Jg. 18; H. 3; S. 269 - 298
Hauptverfasser: Jiao, Li, Abdullah, Muhammad Irsyad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.09.2024
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Boxes
Computer Appl. in Administrative Data Processing
Computer Science
Computer Systems Organization and Communication Networks
e-Commerce/e-business
IT in Business
Management of Computing and Information Systems
Object recognition
Real time
Software Engineering/Programming and Operating Systems
Special Issue Paper
Surveillance
Telematics
Unmanned aerial vehicles
YOLO
Unmanned aerial vehicles
UAV
Drone
Object detection
ISSN:1863-2386, 1863-2394
Online-Zugang:Volltext
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Zusammenfassung:YOLO series algorithms are widely used in unmanned aerial vehicles (UAV) object detection scenarios due to their fast and lightweight properties. This article summarizes the key concepts in YOLO series algorithms, such as the anchor mechanism, feature fusion strategy, bounding box regression loss and so on and points out the advantages and improvement space of the YOLO series algorithms. Discussing the relevant technologies of the YOLOv1 to YOLOv7 series algorithms in detail in three parts: basic structure, strengths and weaknesses, and compares the algorithm performance. On this basis, combined with the challenges of object detection technology in UAV applications, various solutions for improving the YOLO series algorithms and applying them to UAV object detection scenarios are demonstrated. The improvement strategies, application scenarios, academic contributions and limitations of the algorithms are summarized. Finally, the future development directions and challenges of applying YOLO series algorithms to UAV object recognition are prospected.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1863-2386
1863-2394
DOI:10.1007/s11761-024-00388-w