In the remote sensing field, synthetic aperture radar (SAR) is a type of active microwave imaging sensor working in all-weather and all-day conditions, providing high-resolution SAR images of objects such as marine ships. Detection and instance segmentation of marine ships in SAR images has become an important question in remote sensing, but current deep learning models cannot accurately quantify marine ships because of the multi-scale property of marine ships in SAR images. In this paper, we propose a multi-scale feature pyramid network (MS-FPN) to achieve the simultaneous detection and instance segmentation of marine ships in SAR images. The proposed MS-FPN model uses a pyramid structure, and it is mainly composed of two proposed modules, namely the atrous convolutional pyramid (ACP) module and the multi-scale attention mechanism (MSAM) module. The ACP module is designed to extract both the shallow and deep feature maps, and these multi-scale feature maps are crucial for the description of multi-scale marine ships, especially the small ones. The MSAM module is designed to adaptively learn and select important feature maps obtained from different scales, leading to improved detection and segmentation accuracy. Quantitative comparison of the proposed MS-FPN model with several classical and recently developed deep learning models, using the high-resolution SAR images dataset (HRSID) that contains multi-scale marine ship SAR images, demonstrated the superior performance of MS-FPN over other models.

Marine ships are the transport vehicle in the ocean and instance segmentation of marine ships is an accurate and efficient analysis approach to achieve a quantitative understanding of marine ships, for example, their relative locations to other ships or obstacles. This relative spatial information is crucial for developing unmanned ships to avoid crashing. Visible light imaging, e.g. using our smartphones, is an efficient way to obtain images of marine ships, however, so far there is a lack of suitable open-source visible light datasets of marine ships, which could potentially slow down the development of unmanned ships. To address the problem of insufficient datasets, here we built two instance segmentation visible light datasets of marine ships, MariBoats and MariBoatsSubclass, which could facilitate the current research on instance segmentation of marine ships. Moreover, we applied several existing instance segmentation algorithms based on neural networks to analyze our datasets, but their performances were not satisfactory. To improve the segmentation performance of the existing models on our datasets, we proposed a global and local attention mechanism for neural network models to retain both the global location and semantic information of marine ships, resulting in an average segmentation improvement by 4.3% in terms of mean average precision. Therefore, the presented new datasets and the new attention mechanism will greatly advance the marine ship relevant research and applications.