  Visual detection of micro aerial vehicles (MAVs) has received increasing
research attention in recent years due to its importance in many applications.
However, the existing approaches based on either appearance or motion features
of MAVs still face challenges when the background is complex, the MAV target is
small, or the computation resource is limited. In this paper, we propose a
global-local MAV detector that can fuse both motion and appearance features for
MAV detection under challenging conditions. This detector first searches MAV
target using a global detector and then switches to a local detector which
works in an adaptive search region to enhance accuracy and efficiency.
Additionally, a detector switcher is applied to coordinate the global and local
detectors. A new dataset is created to train and verify the effectiveness of
the proposed detector. This dataset contains more challenging scenarios that
can occur in practice. Extensive experiments on three challenging datasets show
that the proposed detector outperforms the state-of-the-art ones in terms of
detection accuracy and computational efficiency. In particular, this detector
can run with near real-time frame rate on NVIDIA Jetson NX Xavier, which
demonstrates the usefulness of our approach for real-world applications. The
dataset is available at https://github.com/WestlakeIntelligentRobotics/GLAD. In
addition, A video summarizing this work is available at
https://youtu.be/Tv473mAzHbU.
