In this letter, we construct MaRine ShiP (MRSP-13), a novel dataset containing 37,161 ship target images belonging to 13 classes with bounding box annotation, and among them there are 3051 images labeled with pixel-level annotation. This dataset equips us with the capability to conduct baseline experiments on maritime target classification, detection and segmentation. We propose a cross-layer multi-task CNN model for maritime target detection, which can simultaneously solve ship target detection, classification, and segmentation. Experimental results have demonstrated the efficiency of the MRSP-13 dataset to be used for maritime target analysis. In addition, the results validate the fact that by adopting the strategies of feature sharing, joint learning, and cross-layer connections, the proposed model achieves superior performance with less annotations. We believe that our MRSP-13 dataset and corresponding baseline experiments will lay down the foundation for further research in maritime target processing.

中文翻译：

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用于海上目标检测的多任务CNN

在这封信中，我们构建了MaRine ShiP（MRSP-13），这是一个新颖的数据集，其中包含37,161个属于目标类别的13个舰船目标图像，并带有边界框注释，其中有3051个图像被像素级注释。该数据集使我们具备进行海上目标分类，探测和分割的基线实验的能力。我们提出了一种用于海上目标检测的跨层多任务CNN模型，该模型可以同时解决舰船目标的检测，分类和分割。实验结果证明了用于海上目标分析的MRSP-13数据集的效率。此外，结果证实了以下事实：通过采用功能共享，联合学习和跨层连接的策略，所提出的模型以较少的注释实现了卓越的性能。我们相信，我们的MRSP-13数据集和相应的基线实验将为海事目标处理的进一步研究奠定基础。