A Multi-Task CNN for Maritime Target Detection
Zhaoying Liu, Muhammad Waqas, Yang Jia, Ahmar Rashid, Zhu Han
Abstract
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.