SeaDroneSim: Simulation of Aerial Images for Detection of Objects Above Water

Abstract

Unmanned Aerial Vehicles (UAVs) are known for their speed and versatility in collecting aerial images and remote sensing for land use surveys and precision agriculture. With UAVs' growth in availability and accessibility, they are now of vital importance as technological support in marine-based applications such as vessel monitoring and search-and-rescue (SAR) operations. High-resolution cameras and Graphic processing units (GPUs) can be equipped on the UAVs to effectively and efficiently aid in locating objects of interest, lending themselves to emergency rescue operations or, in our case, precision aquaculture applications. Modern computer vision algorithms allow us to detect objects of interest in a dynamic environment; however, these algorithms are dependent on large training datasets collected from UAVs, which are currently time-consuming and labor-intensive to collect for maritime environments. To this end, we present a new benchmark suite, SeaD-roneSim, that can be used to create photo-realistic aerial image datasets with ground truth for segmentation masks of any given object. Utilizing only the synthetic data gen-erated from SeaDroneSim, we obtained 71 a mean Average Precision (mAP) on real aerial images for detecting our ob-ject of interest, a popular, open source, remotely operated underwater vehicle (BlueROV) in this feasibility study. The results of this new simulation suit serve as a baseline for the detection of the BlueROV, which can be used in underwater surveys of oyster reefs and other marine applications.