论文标题
SEADRONESIM：模拟航空图像以检测水上物体
SeaDroneSim: Simulation of Aerial Images for Detection of Objects Above Water
论文作者
论文摘要
无人驾驶汽车（UAV）以其快速且通用的适用性而闻名。随着无人机在可用性和应用的增长，它们现在对于在海洋环境中作为搜索和救援（SAR）操作的技术支持至关重要。高分辨率摄像机和GPU可以在无人机上配备，以提供有效而有效的援助，以为紧急救援行动提供帮助。使用现代计算机视觉算法，我们可以检测到针对此类救援任务的对象。但是，这些现代的计算机视觉算法取决于无人机的大量培训数据，这在海上环境中耗时且劳动力密集。为此，我们提出了一个新的基准套件SeadRonesim，可用于创建带有地面真相的照片真实的空中图像数据集，用于任何给定对象的分割掩码。仅利用由Seadronesim生成的合成数据，我们获得了71个真实航空图像的地图，用于检测Bluerov作为可行性研究。新的仿真西装的结果也是检测Bluerov的基线。
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.
