DBF-YOLO: a fall detection algorithm for complex scenes

In complex environments, factors such as varying light intensities, occluded body shapes, crowding, and significant changes in target scale result in low accuracy, reduced detection efficiency, and frequent missed detections in human fall detection. This paper proposes an optimized version of YOLOv8...

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Veröffentlicht in:	Signal, image and video processing Jg. 19; H. 7; S. 532
Hauptverfasser:	Wang, Xu, Xiang, Xiaodong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.07.2025 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Computer Imaging Computer Science Computer vision Deep learning Design Efficiency Fall detection Feature extraction Formability Image Processing and Computer Vision Luminous intensity Methods Modules Multimedia Information Systems Object recognition Original Paper Pattern Recognition and Graphics Posture Real time Sensors Signal,Image and Speech Processing Vision Wearable computers Complex scenes Focal EIoU Fall detection Deformable convolution BiFPN
ISSN:	1863-1703, 1863-1711
Online-Zugang:	Volltext
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