In this paper, we introduce a challenging global large-scale ship database (called GLSD), designed specifically for ship detection tasks. The designed GLSD database includes a total of 140,616 annotated instances from 100,729 images. Based on the collected images, we propose 13 categories that widely exists in international routes. These categories include sailing boat, fishing boat, passenger ship, war ship, general cargo ship, container ship, bulk cargo carrier, barge, ore carrier, speed boat, canoe, oil carrier, and tug. The motivations of developing GLSD include the following: 1) providing a refined ship detection database; 2) providing the worldwide researchers of ship detection and exhaustive label information (bounding box and ship class label) in one uniform global database; and 3) providing a large-scale ship database with geographic information (port and country information) that benefits multi-modal analysis. In addition, we discuss the evaluation protocols given image characteristics in GLSD and analyze the performance of selected state-of-the-art object detection algorithms on GSLD, providing baselines for future studies. More information regarding the designed GLSD can be found at https://github.com/jiaming-wang/GLSD.
翻译:在本文中,我们引入了一个具有挑战性的全球大型船舶数据库(称为GLSD),专门为船舶探测任务设计了一个具有挑战性的全球大型船舶数据库(称为GLSD),设计GLSD数据库中共有来自100 729个图像的140 616个附加说明的实例,根据所收集的图像,我们提出了国际航线上广泛存在的13个类别,其中包括帆船、渔船、客船、战船、一般货轮、集装箱船、散装货承运人、驳船、矿石承运人、快船、独木舟、石油承运人和拖船。开发GLSD的动机包括:1) 提供一个经过改进的船舶探测数据库;2) 在一个统一的全球数据库中提供全世界船舶探测和详尽标签信息(装箱和船级标签标签)的研究人员;3) 提供一个具有地理信息(港口和国家信息)的大型船舶数据库,有利于多模式分析。此外,我们讨论了GLSD的图像特征评价协议,并分析了GLSD选定的最新物体探测算法的性,为未来研究提供基线。关于GLSDDD设计GSD的更多资料可在http://gith/aming.