MFSANet: Zero-Shot Side-Scan Sonar Image Recognition Based on Style Transfer
Hongli Xu, Zhongyu Bai, Xiangyue Zhang, Qichuan Ding
Abstract
Side-scan sonar (SSS) is attracting increasing attention in ocean exploration for its utility and stability on autonomous underwater vehicles (AUVs). Existing SSS image recognition methods mainly employ deep neural networks (DNNs) for various tasks. However, the effectiveness of DNN-based approaches is limited in situations with zero samples. In this letter, a multifeature fusion self-attention network (MFSANet) is proposed to generate SSS images of novel categories, transforming this problem into a conventional supervised learning problem. Specifically, optical-acoustic image pairs are used as inputs to the network to synthesize pseudo-SSS images. First, the shallow and deep features of the input images are extracted by different layers of the encoder. Then, the long-range dependencies of the acoustic images are efficiently modeled with the proposed simplified self-attention module (SSAM). Finally, the acoustic features are appropriately aggregated to each position of the optical features to efficiently generate pseudo-SSS samples for training the classification network. In addition, a novel contrastive loss is proposed to optimize the cross-modal feature space distribution. Experimental results demonstrate that our method can efficiently generate high-quality pseudo-SSS samples, which improves the accuracy of zero-shot SSS image recognition.