FLAWLESS (Detección rápida de aeronaves no tripuladas con recursos limitados) is an applied research project led by the Signal Processing and Communications Group (GAPSC) at the University of Sevilla. The project focuses on applying digital signal processing and artificial intelligence to design low-cost, portable, passive drone detection systems.

Co-funded by the European Union and the Junta de Andalucía, FLAWLESS addresses the growing need for early threat detection against unauthorized or negligent drone flights in civilian areas, critical infrastructure, public events, and urban environments.

Key Objectives & Innovation

Unlike conventional counter-UAS systems—which are often expensive, fixed, or rely on active radar emissions that face strict urban regulatory limits—FLAWLESS develops passive, low-complexity detection technologies that operate without emitting radio waves.

The project focuses on four key research pillars:

  • Dataset Curation & Expansion: Collecting and refining open RF and visual datasets using ground-based sensors and drone-mounted platforms (SDRs, RFSoCs, and low-cost cameras) to capture real-world flight emissions.
  • Radio Frequency (RF) Signal Analysis: Designing low-complexity feature extraction algorithms and Machine Learning (ML) / Deep Learning (DL) architectures to rapidly detect drone-to-controller communication signals in congested spectrum environments.
  • Computer Vision & Image Processing: Implementing optimized object detection models (such as light YOLO, SSD, and Faster/Mask R-CNN variants) tailored for resource-constrained embedded systems.
  • Multi-Sensor Fusion: Combining RF detection confidence scores with visual tracking through decision/feature fusion algorithms to achieve robust, high-accuracy early warning alarms[.

Project Details

  • Lead Institution: University of Seville (US)
  • Principal Investigator (US): Prof. Rafael Boloix Tortosa
  • Co-Principal Investigator (US): Prof. Juan José Murillo Fuentes
  • Research Team Members: Prof. Jesús Iván Maza Alcañiz, Prof. Eva Arias de Reyna Domínguez, Prof. Juan Antonio Becerra González
  • Project Duration: 36 Months
  • Funding Program: Junta de Andalucía / European Regional Development Fund (ERDF / Unión Europea)