Inshore Dense Ship Detection in SAR Images Based on Edge Semantic Decoupling and Transformer

Abstract

Synthetic aperture radar ship detection has recently received significant attention from scholars. However, accurately distinguishing between ships is challenging due to the significant overlap between inshore ship labels. In addition, some labeled boxes contain interference information, such as land areas, which can cause false alarms and confusion in ship feature learning. To address these challenges, this article creates an edge semantic decoupling (ESD) module, adds semantic segmentation branches, and introduces the edge semantic information of ships into the training process. As a result, the model can accurately distinguish between ship targets even when significant overlap exists between inshore labeled boxes. In addition, considering that transformer has the benefit of capturing global and contextual information, this article introduces it into the detection layer to construct a transformer detection layer (TDL) to limit the interference of land and other regions within the labeled box. Experimental results from the public SAR ship detection dataset show that the proposed ESD module and TDL detection layer effectively distinguish different ship targets in the inshore dense ship area, which is less affected by interference areas, such as land in the labeled box. The average precision improves to 96.72%, and both false alarms and miss detections inshore are reduced.