Visual detection of Micro Air Vehicles (MAVs) has attracted increasing
attention in recent years due to its important application in various tasks.
The existing methods for MAV detection assume that the training set and testing
set have the same distribution. As a result, when deployed in new domains, the
detectors would have a significant performance degradation due to domain
discrepancy. In this paper, we study the problem of cross-domain MAV detection.
The contributions of this paper are threefold. 1) We propose a
Multi-MAV-Multi-Domain (M3D) dataset consisting of both simulation and
realistic images. Compared to other existing datasets, the proposed one is more
comprehensive in the sense that it covers rich scenes, diverse MAV types, and
various viewing angles. A new benchmark for cross-domain MAV detection is
proposed based on the proposed dataset. 2) We propose a Noise Suppression
Network (NSN) based on the framework of pseudo-labeling and a large-to-small
training procedure. To reduce the challenging pseudo-label noises, two novel
modules are designed in this network. The first is a prior-based curriculum
learning module for allocating adaptive thresholds for pseudo labels with
different difficulties. The second is a masked copy-paste augmentation module
for pasting truly-labeled MAVs on unlabeled target images and thus decreasing
pseudo-label noises. 3) Extensive experimental results verify the superior
performance of the proposed method compared to the state-of-the-art ones. In
particular, it achieves mAP of 46.9%(+5.8%), 50.5%(+3.7%), and 61.5%(+11.3%) on
the tasks of simulation-to-real adaptation, cross-scene adaptation, and
cross-camera adaptation, respectively.

通过提供包括模拟图像和真实图像的多种场景、多种微型飞行器类型和多种视角的多微型飞行器多领域 (M3D) 数据集，在众多领域上提出了一个新的基准，通过基于伪标签的框架和一个由大到小的训练过程提出了一个噪声抑制网络 (NSN)。通过大量的实验结果验证了所提出方法在各项任务中的优越性能评估。