SAR Ship Target Detection Using SAR Images

Object identification is one of the fundamental challenges in computer vision. Dealing with tiny, fuzzy, and widely dispersed objects is challenging despite tremendous improvements. This article introduces the detector in our model as a tool for detecting tiny objects in SAR (Synthetic Aperture Radar) photos. To overcome the aforementioned issues, the suggested model makes use of shallow layer information, spatial attention, and contextual information. To avoid missing the tiny object features, a shallow semantic information extraction model that incorporates low-level semantic data into the backbone has been designed. The original Neck has been replaced by an MSCFP (Multi-scale Context Feature Pyramid) in order to enhance the exploitation of lower levels of information and provide context information. The recognition of attention locations of various sizes is made possible by the introduction of a spatial attention system. Tests using open-source SSDD (SAR Ship Detection Dataset) datasets show that our model has effective identifying capabilities.