Abstract Vessel re-identification (re-ID) is a crucial task in maritime supervision, enhancing maritime safety and improving the maritime situational awareness system. However, distinct from land-based scenarios involving vehicles or pedestrians, vessels, as enormous rigid bodies situated in the dynamic marine environment, face unique challenges such as significant variations in the scale of discriminative features and unpredictable sway. Furthermore, there is a limited number of publicly available datasets for vessel re-ID in complex backgrounds. In this paper, to overcome these challenges, a novel Hierarchical Perceptual Aggregation Network with Inclination-Aware Attention (HPAN-IAA) is proposed. HPAN-IAA comprises two main modules: the Hierarchical Perceptual Aggregation Block (HPAB) and the Inclination-Aware Attention Block (IAAB). Specifically, in HPAB, a hierarchical perceptual function is introduced to decompose visual information of vessels into discriminative features at multiple levels. These feature maps with different levels of detail from diverse network layers are then fused together by concatenation, resulting in a comprehensive feature representation that effectively integrates information across various scales. Conversely, to address the irregular variations and random omissions in discriminative feature distribution caused by unpredictable vessel sway, in IAAB, the Channel Collaborative Attention Module and the Pyramidal Spatial Attention Module are designed to adaptively extract potential discriminative features within each channel and spatial dimension, enhancing model’s ability in effectively extracting and utilizing irregularly changing discriminative features. Moreover, we propose a novel vessel re-ID dataset—VesselReID-2258. Extensive experiments conducted on VesselReID-2258 and the publicly available dataset VesselReID demonstrate that HPAN-IAA outperforms the current state-of-the-art methods,achieving superior performance with mean Average Precision scores of 0.861 and 0.823.

The key to address long-term person reidentification (Re-ID) in videos is to extract invariant spatio-temporal features (ISTF), which can be broadly categorized into two forms: 1) clothes-irrelevant appearance such as facial characteristics and body shape; 2) identity-distinctive motion such as posture and gait. However, existing studies mainly focus on mining either appearance- or motion-based features in the sequences without sufficient utilization of the ISTF. In this article, we propose an Appearance and gait-based tango network (AGT-Net) to comprehensively mine ISTF from appearance details and gait motions. Specifically, on the appearance detail branch, we introduce an invariance-aware video Swin transformer (IA-VST) to extract clothes-irrelevant appearance from the original RGB sequences. On the gait motion branch, we propose a motion-sensitive gait feature extractor (MS-GFE) to learn identity-distinctive motion from the pre-processed gait sequences. In addition, a score-level fusion strategy is introduced to integrate information from the two streams for prediction. Besides, since there is a lack of publicly available datasets, we propose a style-transferring synthetic long-term Video Re-ID (STYLE-VID) dataset, particularly for long-term Re-ID. Extensive experiments demonstrate that AGT-Net not only outperforms the state-of-the-art methods by up to 1.5% mAP on STYLE-VID, but also achieves comparable performance to other models on the traditional short-term Re-ID dataset MARS.

Modern vessels integrate a massive digital infrastructure and navigation-dependent operating systems, allowing for ship-to-shore and ship-to-ship collaborative communication. However, the heightened interconnection of various maritime infrastructures inevitably amplifies the risk of vessel navigation and communication. Existing intrusion detection techniques were usually built on individual network events, failing to account for the multi-event long-term dependency problem caused by the high latency and low bandwidth of ship communication networks, therefore cannot tackle sophisticated cyber-ship attacks, resulting in lower accuracy in intrusion detection. In this paper, we propose a dependable Intrusion Detection System(IDS) based on Segment-wise CoTransformer(SegCoT) to detect cyber-ship intrusion events, which primarily contains a two-stage Network Pattern Extraction Component (NPEC) and an Intrusion Event Identification Component (IEIC). The NPEC automates the extraction of long-term dependency of massive intrusion events employing a SegEvent-wise Attention (SEA). Furthermore, the extracted dependencies are leveraged by the IEIC for specific intrusion type detection from a spatio-temporal feature fusion perspective. Based on a cyber-ship dataset collected from real ocean-going vessels, the proposed model achieves 99% intrusion detection accuracy, outperforming the existing state-of-the-art approaches.

Synthetic Aperture Radar (SAR) is renowned for its all-weather capabilities, exceptional penetration, and high-resolution imaging, making SAR-based ship detection crucial for maritime surveillance and sea rescue operations. However, various challenges, such as blurred ship contours, complex backgrounds, and uneven scale distribution, can impede detection performance improvement. In this study, we propose FastPFM, a novel ship detection model developed to address these challenges. Firstly, we utilize FasterNet as the backbone network to reduce computational redundancy, enhancing feature extraction efficiency and overall computational performance. Additionally, we employ the Feature Bi-level Routing Transformation model (FBM) to obtain global feature information and enhance focus on target regions. Secondly, the PFM module is engineered to collect multi-scale target information effectively by establishing connections across stages, thereby improving fusion of target features. Thirdly, an extra target feature fusion layer is introduced to enhance small ship detection precision and accommodate multi-scale targets. Finally, comprehensive tests on SSDD and HRSID datasets validate FastPFM's efficacy. Compared to the baseline model YOLOX, FastPFM achieves a 5.5% and 4.4% improvement in detection accuracy, respectively. Furthermore, FastPFM demonstrates comparable or superior performance to other detection algorithms, achieving 92.1% and 83.1% accuracy on AP50, respectively.