Smart Drone for Critical Missions: Development of a VTOL UAV with Real-Time Visual Intelligence

Abstract

This study presents the design

construction

and evaluation of a fixed-wing Vertical Take-Off and Landing (VTOL) unmanned aerial vehicle (UAV) equipped with an onboard real-time visual-intelligence system optimized for critical missions such as search and rescue

surveillance

and precision agriculture. The UAV was built using lightweight

cost-effective materials and 3D-printed components

which considerably reduced development costs and improved accessibility for both academic research and field applications. A central contribution of this work is the integration of VTOL functionality with real-time deep-learning inference on embedded hardware within a fully open-source architecture. Unlike most existing UAVs that depend on bulky or expensive hardware

the proposed system performs efficient object detection (YOLOv5s) directly on a Raspberry Pi 4B

enabling onboard processing without external computation. Three detection models-YOLOv5s

Tiny-YOLOv4

and MobileNet-SSD—were trained on a custom aerial dataset and evaluated for real-time performance. YOLOv5s achieved the highest accuracy

with a mean Average Precision (mAP@0.5) of 82.4 % at 4.2 FPS. Owing to its modular and scalable design

the proposed UAV platform offers a practical and affordable solution for implementing intelligent aerial systems in real-world critical-mission environments.