Abstract
A vision-based drone-to-drone detection system is crucial for various applications like collision avoidance, countering hostile drones, and search-and-rescue operations. However, detecting drones presents unique challenges, including small object sizes, distortion, occlusion, and real-time processing requirements. Current methods integrating multi-scale feature fusion and temporal information have limitations in handling extreme blur and minuscule objects. To address this, we propose a novel coarse-to-fine detection strategy based on vision transformers. We evaluate our approach on three challenging drone-to-drone detection datasets, achieving F1 score enhancements of 7%, 3%, and 1% on the FL-Drones, AOT, and NPS-Drones datasets, respectively. Additionally, we demonstrate real-time processing capabilities by deploying our model on an edge-computing device. Our code will be made publicly available.
Abstract (translated)
基于视觉的无人机对无人机检测系统对于各种应用，如避障、应对敌对无人机和搜索与救援任务至关重要。然而，检测无人机存在独特的挑战，包括小物体尺寸、畸变、遮挡和实时处理需求。目前将多尺度特征融合和时间信息相结合的方法在处理极端模糊和微小物体方面存在局限。为了应对这一挑战，我们提出了一个基于视觉变压器的全新粗-到细检测策略。我们在FL-Drones、AOT和NPS-Drones等三个具有挑战性的无人机对无人机检测数据集上进行了评估，分别实现了FL-Drones数据集的F1得分提高7%、AOT数据集的F1得分提高3%和NPS-Drones数据集的F1得分提高1%。此外，通过将我们的模型部署在边缘计算设备上，我们还展示了实时处理能力。我们的代码将公开发布。
URL
https://arxiv.org/abs/2404.19276
