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Person Detection in Drone Imagery

机译：无人机影像中的人检测

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The use of drones in search and rescue operations has become standard almost everywhere in the world. A special challenge in the search and rescue operation is the automatic detection of persons in different terrains, in different situations and body positions, in different weather conditions, and from different shooting heights during a drone flight. This paper investigates the accuracy of people detection in drone images on existing VisDrone, Okutama - Action datasets, and on a custom SARD image dataset built to simulate search and rescue scenes. A Faster R-CNN with FPN as the backbone, pre-trained on the COCO data set, was used as a detector. The person detector is additionally trained on the SARD data set containing 1,981 images and on the subset of the VisDrone set. After transfer learning, a significant improvement in the detection results of persons in the images taken by the drone was achieved concerning mAP and precision and recall.
机译：在搜索和救援行动中使用无人机已成为世界几乎所有地方的标准。搜救行动中的一个特殊挑战是在无人机飞行过程中自动检测处于不同地形，处于不同状况和身体位置，处于不同天气条件以及来自不同射击高度的人员。本文研究了现有的VisDrone，Okutama-Action数据集以及为模拟搜索和救援场景而构建的自定义SARD图像数据集上的无人机图像中人员检测的准确性。在COCO数据集上预先训练的以FPN为骨干的Faster R-CNN被用作检测器。在包含1,981张图像的SARD数据集和VisDrone集的子集上对人员检测器进行了额外的培训。在进行转移学习之后，在无人机和mAP精度和召回率方面，无人机检测图像中的人员检测结果得到了显着改善。

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