Maritime object detection remains challenging because of complex sea-surface backgrounds, adverse illumination conditions, and severe class imbalance, especially when safety-critical targets such as search-and-rescue vessels are sparsely represented. To address these challenges, we propose a cascaded hybrid DINOv2-YOLOv8n detection framework for maritime scenes. Rather than relying only on supervised learning from raw RGB images, the proposed method introduces semantic priors from a frozen DINOv2 encoder and projects them into a compact representation for a YOLOv8n-based detector. To improve robustness under diverse maritime conditions, the framework uses a sea-state-aware online augmentation strategy and is trained with the standard YOLO detection objective. Experiments on the Maritime Target Data Sharing Project (MTDSP) dataset show that the proposed framework achieves strong detection performance. Specifically, it obtains an overall mAP@0.5 of 89.4% and a precision of 100% on the test set. For sparse search-and-rescue vessels and rigid offshore structures, it achieves mAP@0.5 scores of 99.5% and 96.3%, respectively. These results indicate that combining foundation-model semantic priors with a lightweight detector can improve the reliability of maritime object detection under complex sea-surface conditions.
Zijia Huang, Erkang Zhu, Guo Ye et al.· Electronics· 0 citations
In complex network analysis, the identification of influential nodes is a fundamental issue, which is closely related to the structural robustness of the network and the dynamics of propagation processes. Current research primarily focuses on mesoscale features based on the smallest cycles or local features derived from star-shaped structures. However, the role of neighboring nodes that are connected to a given node but do not participate in its smallest cycles remains underexplored in network analysis. To address this issue, this paper proposes a hybrid centrality measure that integrates information from both smallest-cycle structures and non-smallest-cycle structures associated with each target node. The smallest-cycle structures considered in this method are identified only within the imposed local search range and do not necessarily correspond to the true smallest cycles in the full graph. Specifically, the extent of a node’s involvement in mesoscale structures is characterized by the number of the smallest cycles it participates in, while its local structural heterogeneity is represented by the number of neighboring nodes connected to it that do not belong to any smallest cycles. These two aspects are then unified into a single node importance metric through a weighted integration strategy. This paper evaluates node importance from multiple perspectives, including propagation capability analysis based on the SI model, network robustness testing through node attack simulations, and ranking accuracy assessment using Kendall correlation coefficient. The experimental results demonstrate that the proposed method achieves competitive or superior performance compared with the selected baseline methods under the experimental settings considered in this work. The findings indicate that integrating smallest-cycle and non-smallest-cycle features provides a more comprehensive characterization of a node’s role in complex networks. This study offers a novel perspective on the integration of multi-scale structural information in complex networks and presents an effective new approach for the identification of important nodes.
Fu-Rui Tan, Xiao-long Chen, Ruijie Wang et al.· Entropy· 0 citations
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