Aerial-view human detection has a large demand for large-scale data to
capture more diverse human appearances compared to ground-view human detection.
Therefore, synthetic data can be a good resource to expand data, but the domain
gap with real-world data is the biggest obstacle to its use in training. As a
common solution to deal with the domain gap, the sim2real transformation is
used, and its quality is affected by three factors: i) the real data serving as
a reference when calculating the domain gap, ii) the synthetic data chosen to
avoid the transformation quality degradation, and iii) the synthetic data pool
from which the synthetic data is selected. In this paper, we investigate the
impact of these factors on maximizing the effectiveness of synthetic data in
training in terms of improving learning performance and acquiring domain
generalization ability--two main benefits expected of using synthetic data. As
an evaluation metric for the second benefit, we introduce a method for
measuring the distribution gap between two datasets, which is derived as the
normalized sum of the Mahalanobis distances of all test data. As a result, we
have discovered several important findings that have never been investigated or
have been used previously without accurate understanding. We expect that these
findings can break the current trend of either naively using or being hesitant
to use synthetic data in machine learning due to the lack of understanding,
leading to more appropriate use in future research.

本文研究了航拍人体检测中利用合成数据来训练的方法，通过探讨实际数据、合成数据和数据池选择对合成数据的质量影响以及利用合成数据提高学习性能和域泛化能力的效果，发现了几个重要的发现，为未来研究提供了更合理的使用合成数据的方向。