通过多尺度处理与数据增强，提升小无人机在复杂环境中的检测效果。
Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation
- 输入图像整体与分块并行处理，融合多尺度预测结果。
- 使用copy-paste增强数据集，显著提升对小型无人机和鸟类的区分能力。
- 基于帧间一致性后处理，有效减少漏检，适合实战部署场景。
小无人机检测在现代监控中至关重要，因其常难以与鸟类区分。本文基于中等规模的YOLOv11模型，提出一种改进检测方法：将输入图像整体与分块分别处理，通过多尺度特征融合提升小目标检测性能；采用copy-paste数据增强技术，在训练集中加入多样化的无人机与鸟类样本；最后引入基于帧间一致性的后处理策略，减少漏检。该方法在2025年国际神经网络联合会议（IJCNN）举办的第八届WOSDETC无人机-鸟类检测挑战赛中夺得第一名，验证了其在复杂环境下高效检测无人机的能力。
原文摘要 · Abstract (English)
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.
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