YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection

In the era of 5G communication, removing interference sources that affect communication is a resource-intensive task. The rapid development of computer vision has enabled unmanned aerial vehicles (UAVs) to perform various high-altitude detection tasks. Because the field of object detection for anten...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on instrumentation and measurement 2024, Vol.73, p.1-18
Hauptverfasser:	Tang, Xiaoyu, Chen, Xingming, Cheng, Jintao, Wu, Jin, Fan, Rui, Zhang, Chengxi, Zhou, Zebo
Format:	Artikel
Sprache:	eng
Schlagworte:	Antenna design Antenna interference source detection Antennas Artificial neural networks Computational modeling Computer vision convolutional neural network (CNN)–transformer fusion Datasets Feature extraction High altitude Interference Lightweight Modules Object detection Object recognition small object detection Task analysis Transformers Unmanned aerial vehicles YOLO-Ant
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!