Haojie Pan

With the development of artificial intelligence neural networks, semantic segmentation has received more and more attention in the field of ocean engineering, especially in the fields of unmanned vessels and drones. However, challenges such as limited open ocean datasets, insufficient feature extraction for segmentation networks, pixel pairing problem, and frequency-domain obfuscation still exist. To address these issues, we propose USVS-Net, a high-performance segmentation network for segmenting USV feasible domains and surface obstacles. To overcome the pixel pairing confusion problem, a Global Channel-Spatial Attention module (GCSA) is designed in this paper, which enhances feature interactions, suppresses redundant features, and improves pixel matching accuracy through channel shuffling strategy and large kernel spatial attention. In addition, a median-enhanced channel-spatial attention (MECS) module is proposed to enhance edge details and suppress noise by fusing the median, mean, and maximum values to facilitate cross-scale feature interactions. For evaluation, a dataset USV-DATA containing images of marine obstacles is constructed. Experiments show that USVS-Net outperforms SOTA with mIoU reaching 81.71% and mPA reaching 90.18%, which is a significant improvement over the previous methods. These findings indicate that USVS-Net has high accuracy and robustness and can provide valuable support for autonomous navigation of unmanned vessels.

Directly measuring the mooring cable load while a ship is moored at a wharf poses significant practical challenges. This paper proposes an indirect load measurement method to identify mooring cable static loads based on the Influence Coefficient Matrix (ICM) method. First, a finite element analysis of the bollard is conducted to obtain the full-field strains under each unit load. A solution procedure based on the genetic algorithm (GA) is then implemented to determine the optimal placement and orientation of strain gauges, aiming to improve load identification accuracy. An optimal load coefficient matrix is derived to establish the correlation between cable loads and bollard strains. Subsequently, following the established measured point placement scheme, strain gauges are installed on the bollard surface to capture the strains, enabling inverse identification of mooring cable loads through the measured strains and the pre-established load–strain relationship. A numerical case study validated the feasibility of this method, demonstrating high identification accuracy. Furthermore, experimental verification was conducted to assess its reliability under different conditions. Results confirmed the effectiveness of this indirect approach for mooring cable static loads measurement. The research findings provide a technical framework for real-time monitoring of mooring cable loads.