A synthetic aperture radar small ship detector based on transformers and multi-dimensional parallel feature extraction

Deep learning-based target detection methods have been widely used for Synthetic aperture radar (SAR) ship detection in recent years. However, the large amount of clutter noise and land interference information in SAR images poses challenges to small ship target detection. Existing methods are still inadequate for solving the problem of noise-induced feature loss of small targets, and feature extraction of small targets is inadequate, leading to poor detection accuracy. In response to these problems, we propose a new small ship detector-you only look once (SSD-YOLO). First, to solve the problem that noise interference leads to the loss of ship body features, a small target feature enhancement module (STFEM) based on multi-dimensional parallel feature extraction is introduced. STFEM uses attention mechanisms to capture the main features of small ships from multiple dimensions, effectively enhancing the boundary texture information of the ship body. In addition, a transformer-based skip connection path aggregation network (Tr-PANet) is introduced to adequately extract the contextual information of small targets. Tr-PANet uses self-attention to model the non-local contextual features of small targets in bottom-up feature fusion and uses skip connection to maximally maintain the global structural information of small targets. Since SSD-YOLO is based on a lightweight model, the number of parameters and the computational amount are lower than most of the comparative models. Moreover, SSD-YOLO shows the best detection performance on three real datasets.