Vehicle Detection in Uav Orthophotos Using Yolo V7-M Deep Learning Algorithm

Automatic vehicle detection in orthophoto images obtained by unmanned aerial vehicles (UAVs) can significantly accelerate information extraction for urban planning, transport and agriculture.This paper presents a case study of vehicle detection implemented in the open-source QGIS environment using the Deep neural remote sensing (Deepness) plugin.A UAV equipped with an RGB camera captured the area of interest (AOI), and Detection was performed using a You Only Look Once (YOLO) v7-m model.The model successfully detected 279 passenger vehicles.High detection accuracy was achieved, as demonstrated by the precision, recall, and F1-score parameters.The results show that the Deepness plugin provides a practical and efficient solution for fast vehicle detection in UAV orthophotos.