YOLOMG: Vision-based Drone-to-Drone Detection with Appearance and Pixel-Level Motion Fusion

• URL: http://arxiv.org/abs/2503.07115v1
• Date: Mon, 10 Mar 2025 09:44:21 GMT
• Title: YOLOMG: Vision-based Drone-to-Drone Detection with Appearance and Pixel-Level Motion Fusion
• Authors: Hanqing Guo, Xiuxiu Lin, Shiyu Zhao,
• Abstract summary: This paper proposes a novel end-to-end framework that accurately identifies small drones in complex environments.<n>It starts by creating a motion difference map to capture the motion characteristics of tiny drones.<n>Next, this motion difference map is combined with an RGB image using a bimodal fusion module, allowing for adaptive feature learning of the drone.
• Score: 9.810747004677474
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Vision-based drone-to-drone detection has attracted increasing attention due to its importance in numerous tasks such as vision-based swarming, aerial see-and-avoid, and malicious drone detection. However, existing methods often encounter failures when the background is complex or the target is tiny. This paper proposes a novel end-to-end framework that accurately identifies small drones in complex environments using motion guidance. It starts by creating a motion difference map to capture the motion characteristics of tiny drones. Next, this motion difference map is combined with an RGB image using a bimodal fusion module, allowing for adaptive feature learning of the drone. Finally, the fused feature map is processed through an enhanced backbone and detection head based on the YOLOv5 framework to achieve accurate detection results. To validate our method, we propose a new dataset, named ARD100, which comprises 100 videos (202,467 frames) covering various challenging conditions and has the smallest average object size compared with the existing drone detection datasets. Extensive experiments on the ARD100 and NPS-Drones datasets show that our proposed detector performs exceptionally well under challenging conditions and surpasses state-of-the-art algorithms across various metrics. We publicly release the codes and ARD100 dataset at https://github.com/Irisky123/YOLOMG.

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