A Deep Detection Network Based on Interaction of Instance Segmentation and Object Detection for SAR Images

Abstract

Ship detection is a challenging task for synthetic aperture radar (SAR) images. Ships have arbitrary directionality and multiple scales in SAR images. Furthermore, there is a lot of clutter near the ships. Traditional detection algorithms are not robust to these situations and easily cause redundancy in the detection area. With the continuous improvement in resolution, the traditional algorithms cannot achieve high-precision ship detection in SAR images. An increasing number of deep learning algorithms have been applied to SAR ship detection. In this study, a new ship detection network, known as the instance segmentation assisted ship detection network (ISASDNet), is presented. ISASDNet is a two-stage detection network with two branches. A branch is called an object branch and can extract object-level information to obtain positioning bounding boxes and classification results. Another branch called the pixel branch can be utilized for instance segmentation. In the pixel branch, the designed global relational inference layer maps the features to interaction space to learn the relationship between ship and background. The global reasoning module (GRM) based on global relational inference layers can better extract the instance segmentation results of ships. A mask assisted ship detection module (MASDM) is behind the two branches. The MASDM can improve detection results by interacting with the outputs of the two branches. In addition, a strategy is designed to extract the mask of SAR ships, which enables ISASDNet to perform object detection training and instance segmentation training at the same time. Experiments carried out two different datasets demonstrated the superiority of ISASDNet over other networks.

SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis

“…Obviously, SAR ship detection using PSeg is the most ideal because PSeg can almost completely suppress background clutter. Up to now, several scholars [52,54,98,99] have drawn support from it to achieve SAR ship detection. PSeg ground truths are labeled by themselves, but they are not publicly available, too.…”

“…Obviously, SAR ship detection using PSeg is the most ideal because PSeg can almost thoroughly suppress the background clutter. So far, there are only four papers adopting these kinds of labels, including Su et al [52], Mao et al [54], Sun et al [98], and Wu et al [99]. It should be noted that only one of the four realizes the ship segmentation authentically, i.e., Su et al [52].…”

“…In this network, PSeg labels were used to suppress cruciform sidelobes. Wu et al [99] established the interaction between instance segmentation and object detection. Finally, the BBox detection accuracy on inshore scenes is improved greatly.…”

“…Later, some scholars also adopted other ratios for training, validation, and testing, e.g., 7:2:1 in the work of Chen et al [62], about 5:1 in the work of Yu et al [97], 7:3 in the work of Wu et al [99], 8:2 in the work of Chen et al [90], and so on. Obviously, these diverse dataset division mechanisms will lead to unfair methodological comparison, which is not conducive to academic exchanges.…”

“…One can use a segmentation mask to assist ship detection, e.g., the reports of Mao et al [54], Hou et al [84], and Wu et al [99].…”