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 first place in the 8th WOSDETC Drone-vs-Bird Detection Grand Challenge, held at the 2025 International Joint Conference on Neural Networks (IJCNN), showcasing its capability to detect drones in complex environments effectively.
@article{arxiv.2504.19347,
title = {Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation},
author = {Rayson Laroca and Marcelo dos Santos and David Menotti},
journal= {arXiv preprint arXiv:2504.19347},
year = {2025}
}
Comments
Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 2025