A Deep Learning Framework for Emergency Drone Landing Zone Detection
Objective
The primary objective of this project is to develop an intelligent, real-time system for Emergency Drone Landing Zone Detection using deep learning. The proposed framework utilizes a fine-tuned YOLOv8 model to accurately detect critical objects such as Vehicles, Persons, Unmanned Aerial Platforms (UAP), and Unmanned Aerial Instruments (UAI) in aerial imagery. The system computes a weighted Landing Safety Score and classifies potential landing zones as SAFE, RISKY, or UNSAFE. A comprehensive relevance verification mechanism ensures only contextually valid emergency scenes are processed. The framework is implemented as a secure, user-friendly web application using the Flask framework, enabling authenticated users to upload images and receive instant analysis with visual explanations including annotated images and risk heatmaps.
Paper Publishing
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