Šiaulys Andrius, Vaičiukynas Evaldas, Medelytė Saulė, Olenin Sergej, Šaškov Aleksej, Buškus Kazimieras, Verikas Antanas
Marine Research Institute, Klaipeda University, Klaipeda, Lithuania.
Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania.
Data Brief. 2021 Jan 30;35:106823. doi: 10.1016/j.dib.2021.106823. eCollection 2021 Apr.
Underwater imagery is widely used for a variety of applications in marine biology and environmental sciences, such as classification and mapping of seabed habitats, marine environment monitoring and impact assessment, biogeographic reconstructions in the context of climate change, etc. This approach is relatively simple and cost-effective, allowing the rapid collection of large amounts of data. However, due to the laborious and time-consuming manual analysis procedure, only a small part of the information stored in the archives of underwater images is retrieved. Emerging novel deep learning methods open up the opportunity for more effective, accurate and rapid analysis of seabed images than ever before. We present annotated images of the bottom macrofauna obtained from underwater video recorded in Spitsbergen island's European Arctic waters, Svalbard Archipelago. Our videos were filmed in both the photic and aphotic zones of polar waters, often influenced by melting glaciers. We used artificial lighting and shot close to the seabed (<1 m) to preserve natural colours and avoid the distorting effect of muddy water. The underwater video footage was captured using a remotely operated vehicle (ROV) and a drop-down camera. The footage was converted to 2D mosaic images of the seabed. 2D mosaics were manually annotated by several experts using the Labelbox tool and co-annotations were refined using the SurveyJS platform. A set of carefully annotated underwater images associated with the original videos can be used by marine biologists as a biological atlas, as well as practitioners in the fields of machine vision, pattern recognition, and deep learning as training materials for the development of various tools for automatic analysis of underwater imagery.
水下图像在海洋生物学和环境科学的各种应用中被广泛使用，例如海底栖息地的分类和绘图、海洋环境监测与影响评估、气候变化背景下的生物地理重建等。这种方法相对简单且具有成本效益，能够快速收集大量数据。然而，由于人工分析过程繁琐且耗时，水下图像档案中存储的信息只有一小部分被检索出来。新兴的深度学习方法为比以往更有效、准确和快速地分析海底图像提供了机会。我们展示了从斯瓦尔巴群岛欧洲北极水域斯匹次卑尔根岛水下视频中获取的底栖大型动物的带注释图像。我们的视频是在极地水域的光合带和无光带拍摄的，这些区域经常受到冰川融化的影响。我们使用人工照明并在靠近海底（<1米）的位置拍摄，以保留自然颜色并避免浑水的扭曲效果。水下视频片段是使用遥控潜水器（ROV）和下拉式相机拍摄的。这些片段被转换为海底的二维镶嵌图像。几位专家使用Labelbox工具对二维镶嵌图像进行了人工注释，并使用SurveyJS平台对共同注释进行了完善。一组与原始视频相关的经过精心注释的水下图像可供海洋生物学家用作生物图谱，也可供机器视觉、模式识别和深度学习领域的从业者用作开发各种水下图像自动分析工具的训练材料。
