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

TL;DR

SeaDroneSim is a new simulation tool that generates realistic aerial images for training and evaluating object detection algorithms in marine environments, reducing the need for labor-intensive real data collection.

Contribution

The paper introduces SeaDroneSim, a benchmark suite for creating synthetic aerial datasets with ground truth annotations for maritime object detection.

Findings

Achieved 71 mAP on real images using only synthetic data

Provided a baseline for BlueROV detection in aerial imagery

Demonstrated the effectiveness of synthetic data in marine object detection

Abstract

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.