Recent advances in deep learning technology have triggered radical progress in the autonomy of ground vehicles. Marine coastal Autonomous Surface Vehicles (ASVs) that are regularly used for surveillance, monitoring and other routine tasks can benefit from this autonomy. Long haul deep sea transportation activities are additional opportunities. These two use cases present very different terrains -- the first being coastal waters -- with many obstacles, structures and human presence while the latter is mostly devoid of such obstacles. Variations in environmental conditions are common to both terrains. Robust labeled datasets mapping such terrains are crucial in improving the situational awareness that can drive autonomy. However, there are only limited such maritime datasets available and these primarily consist of optical images. Although, Long Wave Infrared (LWIR) is a strong complement to the optical spectrum that helps in extreme light conditions, a labeled public dataset with LWIR images does not currently exist. In this paper, we fill this gap by presenting a labeled dataset of over 2,900 LWIR segmented images captured in coastal maritime environment under diverse conditions. The images are labeled using instance segmentation and classified in seven categories -- sky, water, obstacle, living obstacle, bridge, self and background. We also evaluate this dataset across three deep learning architectures (UNet, PSPNet, DeepLabv3) and provide detailed analysis of its efficacy. While the dataset focuses on the coastal terrain it can equally help deep sea use cases. Such terrain would have less traffic, and the classifier trained on cluttered environment would be able to handle sparse scenes effectively. We share this dataset with the research community with the hope that it spurs new scene understanding capabilities in the maritime environment.
翻译:深层学习技术的最近进步导致地面车辆的自主性取得了巨大进展; 沿海沿海自控水面车辆(ASV)定期用于监视、监测和其他日常任务的海洋自控车辆(ASV)能够从这种自主性中受益; 长期的深海运输活动是额外的机遇。 这两个使用案例呈现出非常不同的地形 -- -- 首先是沿海水域 -- -- 有许多障碍、结构和人的存在,而后者大多不存在这些障碍; 环境条件的变化是两种地形的共同特点; 牢固的标记数据集测绘这些地形对于提高可驱动自主性的情况意识至关重要; 然而,只有有限的海洋自控数据集,而且主要包括光学图像。虽然长波红外线(LWIR)是光谱化(LWIR)的强大补充光谱,在极端的环境下,目前还不存在许多带有LWIR图像的贴标签的公共数据集; 在各种地形中绘制有2,900多个LWIR分层图像的标签数据集,在各种条件下收集的海岸地形图像。 图像使用实例分割和分类,主要包括光学图像的七个类别 -- -- 长波星、水深层地面、水深层数据屏障碍、我们学习的地形分析。