Unmanned Surface Vehicles (USVs) have emerged as vital tools in marine monitoring and management due to their high efficiency, low cost, and flexible deployment capabilities. This paper presents a systematic review focusing on four core areas of USV applications: communication networking, navigation, control, and data-driven operations. First, the characteristics and challenges of acoustic, electromagnetic, and optical communication methods for USV networking are analyzed, with an emphasis on the future trend toward multimodal communication integration. Second, a comprehensive review of global navigation, local navigation, cooperative navigation, and autonomous navigation technologies is provided, highlighting their applications and limitations in complex environments. Third, the evolution of USV control systems is examined, covering group control, distributed control, and adaptive control, with particular attention given to fault tolerance, delay compensation, and energy optimization. Finally, the application of USVs in data-driven marine tasks is summarized, including multi-sensor fusion, real-time perception, and autonomous decision-making mechanisms. This study aims to reveal the interaction and coordination mechanisms among communication, navigation, control, and data-driven operations from a system integration perspective, providing insights and guidance for the intelligent operations and comprehensive applications of USVs in marine environments.

Real-time processing of high-resolution sonar images is of great significance for the autonomy and intelligence of autonomous underwater vehicle (AUV) in complex marine environments. In this paper, we propose a real-time semantic segmentation network termed RT-Seg for Side-Scan Sonar (SSS) images. The proposed architecture is based on a novel encoder-decoder structure, in which the encoder blocks utilized Depth-Wise Separable Convolution and a 2-way branch for improving performance, and a corresponding decoder network is implemented to restore the details of the targets, followed by a pixel-wise classification layer. Moreover, we use patch-wise strategy for splitting the high-resolution image into local patches and applying them to network training. The well-trained model is used for testing high-resolution SSS images produced by sonar sensor in an onboard Graphic Processing Unit (GPU). The experimental results show that RT-Seg can greatly reduce the number of parameters and floating point operations compared to other networks. It runs at 25.67 frames per second on an NVIDIA Jetson AGX Xavier on 500*500 inputs with excellent segmentation result. Further insights on the speed and accuracy trade-off are discussed in this paper.

The recognition of multiple Autonomous Underwater Vehicles (AUVs) equipped with Side-Scan Sonar (SSS) is the key to marine surveys, and autonomously and efficiently recognizing marine targets is an urgent open challenge. This paper proposes a lightweight Recurrent Transfer-Adaptive Learning (RTAL) to improve the recognition accuracy of SSS images. In this work, (1) we analyze the target features collected by multiple AUVs, calculate the similarity between features using Mahalanobis distance, and establish a threshold. (2) When the threshold is higher than the similarity, AUVs adopt improved Recurrent Transfer Learning (RTL) to reduce computational consumption and ensure the real-time performance of the algorithm. (3) When the similarity is lower than the threshold, AUVs adopt RTAL to reconstruct the data of the target information with inconspicuous features caused by the complex background, extract features efficiently, and reduce the interference of environmental factors. We compare several classical neural networks and validate them on dataset collected from AUVs sea trials. The experimental results show that our proposed method has high recognition speed and recognition accuracy.