Rapid Radiological Pollution Detection and Identification Using a UAV-Based AI System

This research introduces an innovative UAV-based system designed to accelerate environmental risk assessment during radiological emergencies. The framework combines unmanned aerial vehicles (UAVs) equipped with a typical camera and gamma-ray detectors to identify, precisely geolocate, and quantify radioactive contaminants. A machine learning-driven detection pipeline leverages onboard AI-powered computer vision to recognize contaminated objects in cluttered or dynamic environments, while a sensor fusion architecture synthesizes visual data and radiation data to isolate targets with anomalous emissions in real time. Field tests demonstrate the system’s capability to map contamination faster than conventional ground surveys while maintaining sub-meter geolocation accuracy, enabling emergency teams to prioritize hazards and deploy countermeasures within critical early-response windows. We detail the system’s architecture, field validation results, and operational advantages over existing methods. The discussion highlights how this technology addresses persistent gaps in rapid radiological triage, offering a scalable solution that enhances situational awareness while minimizing responder exposure. The findings underscore the transformative potential of integrated UAV-AI systems for redefining radiological emergency protocols and environmental monitoring frameworks globally.