YOLOv5-Based Object Detection for Emergency Response in Aerial Imagery

This paper presents a robust approach for object detection in aerial imagery using the YOLOv5 model. We focus on identifying critical objects such as ambulances, car crashes, police vehicles, tow trucks, fire engines, overturned cars, and vehicles on fire. By leveraging a custom dataset, we outline...

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Veröffentlicht in:	arXiv.org 2024-12
Hauptverfasser:	Boddu, Sindhu, Mukherjee, Arindam, Seal, Arindrajit
Format:	Artikel
Sprache:	eng
Schlagworte:	Aerial photography Annotations Automatic vehicle identification systems Automobiles Crashes Emergency response Object recognition Real time Traffic accidents
Online-Zugang:	Volltext
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