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

本文研究了计算机视觉和深度学习技术在无人机检测方面的表现，探究了在复杂背景、不同大小、复杂天气等挑战条件下使用一阶段探测器和二阶段探测器进行UAV检测的性能，并对两个数据集进行了评估，研究结果为发展更为强大的UAV检测方法提供了借鉴。

基于视觉的复杂背景和雨天下的无人机检测