Improved Object Detection Method Utilizing YOLOv7-Tiny for Unmanned Aerial Vehicle Photographic Imagery

In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to impr...

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Veröffentlicht in:	Algorithms Jg. 16; H. 11; S. 520
Hauptverfasser:	Zhang, Linhua, Xiong, Ning, Pan, Xinghao, Yue, Xiaodong, Wu, Peng, Guo, Caiping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.11.2023
Schlagworte:	Accuracy Aerial photography Aerial vehicle Algorithms Antennas Boxes Classification Datasets decoupled head Deep learning Drone aircraft Effectiveness Image enhancement Loss functions Methods Neural networks Object detection Object detection method Object recognition Objects detection Photographic imagery Small object detection Small objects Telematics Unmanned aerial vehicles Unmanned aerial vehicles (UAV) Weather WIoU Wise intersection over union YOLOv7-tiny model
ISSN:	1999-4893, 1999-4893
Online-Zugang:	Volltext
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