Complex Encoding Transformer for 3D Sonar Target Detection

With the ongoing advancement of 3D sonar detection technology, research on underwater 3D target detection has gained increasing significance. Currently, there is substantial research on optical point clouds, while research on 3D sonar point clouds remains limited. Underwater 3D sonar recognition differs from optical recognition, facing challenges such as high sparsity, strong noise intensity, and inter-object coupling. However, traditional optical-based methods struggle with recognizing coupled targets like frogmen and bubbles. This paper proposed a detection method based on a dynamic complex encoding transformer. By combining the principles of sparse array 3D sonar imaging and complex decoupling based on prior knowledge, noise and sidelobe interference are effectively reduced. Addressing the challenges of detecting concealed targets, this paper proposed a novel 3D backbone based on complex-encoding, which effectively enhances additional information around targets, achieving efficient recognition of 3D sonar targets. Finally, our model achieved satisfactory performance through both qualitative and quantitative experiments.