To detect unmanned aerial vehicles (UAVs) in real-time, computer vision and deep learning approaches are evolving research areas. Interest in this problem has grown due to concerns regarding the possible hazards and misuse of employing UAVs in many applications. These include potential privacy violations. To address the concerns, vision-based object detection methods have been developed for UAV detection. However, UAV detection in images with complex backgrounds and weather artifacts like rain has yet to be reasonably studied. Hence, for this purpose, we prepared two training datasets. The first dataset has the sky as its background and is called the Sky Background Dataset (SBD). The second training dataset has more complex scenes (with diverse backgrounds) and is named the Complex Background Dataset (CBD). Additionally, two test sets were prepared: one containing clear images and the other with images with three rain artifacts, named the Rainy Test Set (RTS). This work also focuses on benchmarking state-of-the-art object detection models, and to the best of our knowledge, it is the first to investigate the performance of recent and popular vision-based object detection methods for UAV detection under challenging conditions such as complex backgrounds, varying UAV sizes, and low-to-heavy rainy conditions. The findings presented in the paper shall help provide insights concerning the performance of the selected models for UAV detection under challenging conditions and pave the way to develop more robust UAV detection methods. The codes and datasets are available at: https://github.com/AdnanMunir294/UAVD-CBRA.

翻译：为实时检测无人机，计算机视觉与深度学习方法正成为新兴研究领域。由于无人机在诸多应用中可能引发安全隐患与滥用问题（包括潜在隐私侵犯），该问题的研究兴趣日益增长。针对这些关切，基于视觉的目标检测方法已被开发用于无人机检测。然而，在复杂背景及雨雾等天气伪影影响下的无人机图像检测尚未得到充分研究。为此，我们制备了两个训练数据集：首个数据集以天空为背景，称为天空背景数据集（SBD）；第二个训练数据集包含更复杂的场景（多样化背景），命名为复杂背景数据集（CBD）。此外，还准备了两个测试集：一个包含清晰图像，另一个包含三种雨雾伪影的图像，称为雨天测试集（RTS）。本研究还聚焦于对现有最优目标检测模型进行基准测试，据我们所知，这是首次在复杂背景、无人机尺寸变化及轻至重度降雨等挑战条件下，系统评估近期主流基于视觉的无人机检测方法性能。本文结论将揭示所选模型在挑战条件下进行无人机检测的表现规律，为开发更鲁棒的无人机检测方法奠定基础。代码与数据集获取地址：https://github.com/AdnanMunir294/UAVD-CBRA。