To detect UAVs in real-time, computer vision and deep learning approaches are developing areas of research. There have been concerns raised regarding the possible hazards and misuse of employing unmanned aerial vehicles (UAVs) in many applications. These include potential privacy violations, safety-related issues, and security threats. Vision-based detection systems often comprise a combination of hardware components such as cameras and software components. In this work, the performance of recent and popular vision-based object detection techniques is investigated for the task of UAV detection under challenging conditions such as complex backgrounds, varying UAV sizes, complex background scenarios, and low-to-heavy rainy conditions. To study the performance of selected methods under these conditions, two datasets were curated: one with a sky background and one with complex background. In this paper, one-stage detectors and two-stage detectors are studied and evaluated. The findings presented in the paper shall help provide insights concerning the performance of the selected models for the task of UAV detection under challenging conditions and pave the way to develop more robust UAV detection methods

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研究热点。人们对无人机在众多应用中的潜在风险与滥用问题日益担忧，包括可能的隐私侵犯、安全问题及安保威胁。基于视觉的检测系统通常由摄像头等硬件组件与软件组件共同构成。本研究针对复杂背景、无人机尺寸多变、复杂背景场景及轻到重度降雨等挑战性条件，探究了当前主流基于视觉的目标检测技术在无人机检测任务中的性能表现。为评估所选方法在上述条件下的性能，研究构建了两个数据集：其一为天空背景数据集，其二为复杂背景数据集。本文对单阶段检测器与两阶段检测器进行了研究与评估。研究结果将有助于深入了解所选模型在挑战性条件下执行无人机检测任务时的性能表现，并为开发更鲁棒的无人机检测方法奠定基础。