Autonomous surface vehicles (ASVs) are widely employed in various marine industrial tasks. Regardless of the specific operation, one common process is docking, particularly the ASV's return to port. Therefore, the docking process is crucial to ASV operations in industrial applications. Docking space perception (DSP) provides essential environmental information, such as the docking space, driving space, and the shore. However, only a few studies have utilized marks for cooperative docking space perception, and no research has explored noncooperative perception. This article proposes a novel docking space bird's eye view (DSBEV) network to segment docking space on the bird's eye view (BEV) plane for the DSP task. DSBEV does not require any marks and can operate directly in port environments. DSBEV network includes an undistortion-splat module and an attention-water module, designed to enhance the DSP performance. A new watershore loss is introduced to generate the docking space utilizing the shape information. Due to the lack of the docking perception dataset, we construct a water surface platform to create a docking space dataset. The experimental results demonstrate that our DSBEV network achieved an average IoU of 76.8%, attaining state-of-the-art performance. Module analyses and ablation studies are conducted to validate the effectiveness of our designed modules.

Forward-looking sonar (FLS) image segmentation plays a significant role in ocean engineering. However, the existing image segmentation algorithms present difficulties in extracting features from FLS images with weak semantic information, complex backgrounds and strong environmental noises. Convolutional neural networks (CNNs) have demonstrated remarkable capabilities in semantic segmentation tasks, but the locality of convolution limits the ability to extract global context and long-range semantic information. The effective extraction of global contextual information is indispensable for achieving accurate segmentation results in sonar image processing. In this paper, we propose a novel semantic segmentation architecture for forward-looking sonar images called SonarNet. SonarNet is based on a hybrid CNN-Transformer-HOG framework and comprises four modules. 1) The Global-Local Encoder can extract both global and detailed feature information of the underwater target; 2) the Network Decoder converts the high-semantic feature map into a pixel-level classification; 3) as a bridge between dual encoders, the Global-Local Fusion Module ensures semantic consistency between different encoders; 4) the HOG Feature Encoder and Fusion can extract traditional manual features and perform feature alignment. We conducted comprehensive ablation experiments to validate the efficacy of the designed modules. Finally, experimentation revealed that SonarNet significantly outperforms other CNN-based and CNN-Transformer FLS image segmentation methods.

While the intricate underwater environment leads to blurry and faint features of sonar targets, SonarNet has depicted great success due to its high model capabilities and multifeature fusion mechanism. However, their remarkable performance is accompanied by heavier backbones and larger model sizes to achieve benefits at the cost of increased complexity. The research on fast segmenters deployed on edge devices is urgently inquired. In this article, we analyze the current best-performing sonar image segmentation network named SonarNet. Based on the analysis, we propose a lightweight local feature grafted vision transformer (ViT) embedding network for forward-looking sonar (FLS) images called EsonarNet, which promotes a priority balance between efficiency and accuracy. EsonarNet is based on a hybrid local-global feature grafting architecture and comprises four modules. First, expand from the traditional convolutional neural network (CNN) and histogram of oriented gradients (HOG), a lightweight sonar semantic segmentation model based on hybrid CNN-transformer-HOG fusion encoding and decoding, while preserving high efficiency applied to hardware resources. Second, lightweight encoder units are employed in our EsonarNet, including a designed spatial mobile inverted bottleneck convolution (SMBConv) and efficient vision transformer (ViT) module. Third, serving as a transitional liaison between the CNN encoder and the transformer encoder, the local-global features interaction (LGFI) module focuses on dispersing local semantic information to facilitate long-distance computations, and the global-local aggregation unit (GLAU) module computes correlations through dot products to restore inductive bias. Fourth, the HOG features are introduced into EsonarNet through the lightweight HOG-deep learning graft mechanism (LHDGM) module to ensure the coherence and compatibility of the acquired traditional and abstract information with different semantics. Ultimately, experimental results demonstrate that EsonarNet outperforms other methods for FLS image segmentation in efficiency.

Mobile Underwater Acoustic Networks (UANs) leverage Autonomous Underwater Vehicles (AUVs) to enhance flexibility and mobility, playing an essential role in ocean research. Similar to static UANs, the Medium Access Control (MAC) protocol is still critical for mobile UANs to achieve efficient communication. However, mobile UANs are delaysensitive and suffer from low Signal-to-Noise Ratio (SNR), which presents significant challenges for the design of MAC protocols. As a hybrid technology combining spread spectrum and multicarrier modulation, Multi-Carrier Code Division Multiple Access (MC-CDMA) offers simple multi-path channel equalization and flexible multi-user access, aiding the MAC protocol in achieving robust communication in mobile UANs. Along this line, we propose an MC-CDMA-based MAC (MC-MAC) protocol, which considers both characteristics of mobile UANs and MC-CDMA to achieve efficient concurrent communication. Specifically, to adequately utilize the limited underwater communication resources, we design an adaptive node clustering algorithm, classifying nodes based on propagation distance, relative mobile velocity, data size, and data grade. Meanwhile, the algorithm determines non-random initial center nodes and adaptively decides the optimal number of clusters to decrease the computational complexity. Based on the clustering results, we present a manyobjective optimization algorithm, which jointly allocates specific spreading code length, spreading code number, subcarrier range, and transmission power to optimize throughput, delay, and energy consumption in mobile UANs. Extensive simulation results demonstrate that MC-MAC fully leverages the advantages of MC-CDMA, providing efficient concurrent communication with lower energy consumption for mobile UANs compared to stateof-the-art protocols.