Efficient CNN-based low-resolution facial detection from UAVs

Julio Diez-Tomillo, Ignacio Martinez-Alpiste, Gelayol Golcarenarenji, Qi Wang, Jose Maria Alcaraz-Calero

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Face detection in UAV imagery requires high accuracy and low execution time for real-time mission-critical operations in public safety, emergency management, disaster relief and other applications. This study presents UWS-YOLO, a new Convolutional Neural Network (CNN) based machine learning algorithm designed to address these demanding requirements. UWS-YOLO’s key strengths lie in its exceptional speed, remarkable accuracy, and ability to handle complex UAV operations. This algorithm presents a balanced and portable solution for real-time face detection in UAV applications. Evaluation and comparison with the state-of-the-art algorithms using standard and UAV-specific datasets demonstrate UWS-YOLO’s superiority. It achieves 59.29% of accuracy compared with 27.43% in a state-of-the-art solution RetinaFace and 46.59% with YOLOv7. Additionally, UWS-YOLO operates at 11 milliseconds which is 345% faster than RetinaFace and 373% than YOLOv7.
Original languageEnglish
Number of pages14
JournalNeural Computing & Applications
Early online date13 Jan 2024
Publication statusEarly online - 13 Jan 2024


  • facial detection
  • UAV
  • deep learning
  • YOLO
  • RetinaFace

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