论文标题
无人机LAM：一个基于无人机的面部检测数据集，具有较大角度和许多情况
Drone LAMS: A Drone-based Face Detection Dataset with Large Angles and Many Scenarios
论文作者
论文摘要
这项工作提出了一个新的基于无人机的面部检测数据集无人机LAMS，以解决在诸如大角度之类的场景中基于无人机的面部检测低性能的问题，例如，当无人机飞得高时，这是主要的工作条件。提议的数据集从261个视频中捕获了超过43k注释和4.0k图像的图像，距离为-90°至90°的斜角或偏航角度。就检测性能而言，无人机LAMS比当前可用的基于无人机的面部检测数据集显示出显着改善，尤其是在较大的音高和偏航角度方面。还提供了对关键因素（例如重复率，注释方法等）的详细分析，还提供了影响数据集的性能，以促进面部检测进一步使用无人机。
This work presented a new drone-based face detection dataset Drone LAMS in order to solve issues of low performance of drone-based face detection in scenarios such as large angles which was a predominant working condition when a drone flies high. The proposed dataset captured images from 261 videos with over 43k annotations and 4.0k images with pitch or yaw angle in the range of -90° to 90°. Drone LAMS showed significant improvement over currently available drone-based face detection datasets in terms of detection performance, especially with large pitch and yaw angle. Detailed analysis of how key factors, such as duplication rate, annotation method, etc., impact dataset performance was also provided to facilitate further usage of a drone on face detection.
