SMFF-YOLO: A Scale-Adaptive YOLO Algorithm with Multi-Level Feature Fusion for Object Detection in UAV Scenes

Object detection in images captured by unmanned aerial vehicles (UAVs) holds great potential in various domains, including civilian applications, urban planning, and disaster response. However, it faces several challenges, such as multi-scale variations, dense scenes, complex backgrounds, and tiny-s...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 15; H. 18; S. 4580
Hauptverfasser:	Wang, Yuming, Zou, Hua, Yin, Ming, Zhang, Xining
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.09.2023
Schlagworte:	Accuracy Adaptability Adaptive algorithms algorithms Comparative analysis Competitiveness complex scenarios data collection Datasets Deep learning Disaster management Drone aircraft head Image segmentation Localization Machine learning Machine vision Methods Modules multi-level feature information fusion Neural networks object detection Object recognition Performance evaluation Photography, Aerial prediction Remote sensing Technology application Telematics tiny objects Unmanned aerial vehicles Urban planning China
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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