Forward-looking sonar (FLS) is widely applied in underwater operations, among which the search of underwater crash objects and victims is an incredibly challenging task. An efficient detection method based on deep learning can intelligently detect objects in FLS images, which makes it a reliable tool to replace manual recognition. To achieve this aim, we propose a novel Swin Transformer based anchor-free network (STAFNet), which contains a strong backbone Swin Transformer and a lite head with deformable convolution network (DCN). We employ a ROV equipped with a FLS to acquire dataset including victim, boat and plane model objects. A series of experiments are carried out on this dataset to train and verify the performance of STAFNet. Compared with other state-of-the-art methods, STAFNet significantly overcomes complex noise interference, and achieves the best balance between detection accuracy and inference speed.