Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation
Journal:
arXiv
Published Date:
Apr 27, 2025
Abstract
Detecting small drones, often indistinguishable from birds, is crucial for
modern surveillance. This work introduces a drone detection methodology built
upon the medium-sized YOLOv11 object detection model. To enhance its
performance on small targets, we implemented a multi-scale approach in which
the input image is processed both as a whole and in segmented parts, with
subsequent prediction aggregation. We also utilized a copy-paste data
augmentation technique to enrich the training dataset with diverse drone and
bird examples. Finally, we implemented a post-processing technique that
leverages frame-to-frame consistency to mitigate missed detections. The
proposed approach attained a top-3 ranking in the 8th WOSDETC Drone-vsBird
Detection Grand Challenge, held at the 2025 International Joint Conference on
Neural Networks (IJCNN), showcasing its capability to detect drones in complex
environments effectively.
