  Unmanned Aerial Vehicles (UAVs) are known for their fast and versatile
applicability. With UAVs' growth in availability and applications, they are now
of vital importance in serving as technological support in
search-and-rescue(SAR) operations in marine environments. High-resolution
cameras and GPUs can be equipped on the UAVs to provide effective and efficient
aid to emergency rescue operations. With modern computer vision algorithms, we
can detect objects for aiming such rescue missions. However, these modern
computer vision algorithms are dependent on numerous amounts of training data
from UAVs, which is time-consuming and labor-intensive for maritime
environments. To this end, we present a new benchmark suite, SeaDroneSim, that
can be used to create photo-realistic aerial image datasets with the ground
truth for segmentation masks of any given object. Utilizing only the synthetic
data generated from SeaDroneSim, we obtain 71 mAP on real aerial images for
detecting BlueROV as a feasibility study. This result from the new simulation
suit also serves as a baseline for the detection of BlueROV.
