Search Results (550)

Synthetic aperture radar (SAR) ship detection in complex inshore scenarios has long been constrained by the coupled effects of speckle noise and small–scale weak scattering targets. Although feature–level frequency–domain denoising methods partially alleviate noise interference, existing studies predominantly focus on spatial frequency modeling and implicitly assume consistent spectral responses and discriminative contributions across channels. This assumption may lead to over–suppression of weak ship targets under complex backgrounds. To address the incomplete dimensionality of current frequency–domain modeling, this paper proposes FSMD–Net, a joint spatial–channel spectral modeling framework for SAR ship detection. During multi–scale feature fusion, a coordinated modulation mechanism integrating multi–spectral channel attention with spatial frequency–domain denoising is introduced. This design enables channel discriminability and frequency–subspace denoising to act synergistically, enforcing structurally consistent spectral constraints throughout multi–scale feature propagation. Extensive experiments on SARDet–100K, HRSID, and AIR–SARShip–2.0 demonstrate that FSMD–Net achieves consistent performance improvements, particularly in small–target and strong–clutter scenarios, exhibiting enhanced detection accuracy and robustness. Full article

The coupling of non-stationary marine noise and complex ship-radiated signals makes high-fidelity signal recovery exceptionally difficult. Existing deep learning methods often prioritize objective metrics, such as the Scale-Invariant Signal-to-Noise Ratio (SI-SNR), but fail to maintain the integrity of narrow-band line spectral data. We propose FocuS-MN, an end-to-end framework that combines learnable resampling with Feedforward Sequential Memory Network (FSMN)-based temporal modeling for precise waveform reconstruction. The model is optimized using a two-stage training strategy to ensure stable magnitude estimation and waveform consistency. On the ShipsEar dataset, FocuS-MN shows strong generalization to unseen vessel types. At a −5 dB Signal-to-Noise Ratio (SNR), it achieves a Signal-to-Distortion Ratio (SDR) of 3.77 dB and a Segmental Signal-to-Noise Ratio (SSNR) of 3.83 dB. Power Spectral Density (PSD) analysis further confirms that FocuS-MN recovers fine-grained line spectral structures, proving its effectiveness in both noise suppression and signal fidelity. Full article

This article presents a literature review aimed at outlining the state of the art in the assessment of underwater noise and in the evaluation of its effects on fish behavior and health. We examine current methodologies for characterizing the underwater soundscape, emphasizing the importance of incorporating particle motion sensors alongside pressure sensors due to the nature of fish auditory systems. Guidelines for simulating underwater acoustic environments in laboratory settings are also summarized. To characterize anthropogenic noise sources, we consider ship propellers as the primary source of continuous underwater noise, whereas we consider the equipment used in marine seismic surveys as the primary source of impulsive underwater noise. Finally, we summarize documented effects of acoustic pollution on a commercially important species, European seabass (Dicentrarchus labrax), and describe experimental setups suitable for observing these effects. Full article

With the continuous expansion of maritime trade scale, ports have put forward increasingly higher requirements for transshipment efficiency. Container twist lock disassembly is a key link in the loading and unloading process, and its automation level has a significant impact on the ship’s berthing time at the port. Aiming at the demand of automated disassembly for high-precision 3D vision, this paper proposes a multi-view point cloud local registration method for twist lock recognition. First, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is used to extract the keyhole region with the highest overlap in multi-view point clouds, reducing the interference from non-overlapping structures. Then, a two-stage strategy of “coarse registration + fine registration” is adopted: initial alignment is achieved through Random Sample Consensus (RANSAC), and the Iterative Closest Point (ICP) algorithm is improved by combining adaptive distance threshold and normal consistency constraint to complete fine registration. Experimental results show that the proposed method outperforms the global registration scheme in both accuracy and efficiency: the Root Mean Square Error (RMSE) is reduced to 2.15 mm, the Relative Mean Distance (RMD) is reduced to 1.81 mm, and the registration time is approximately 2.41 s. Compared with global registration, the efficiency is improved by 44.2%, which can meet the real-time requirements of continuous operation at automated terminals for the perception link and the time constraints for subsequent manipulator control. The research results preliminarily verify the application potential of this method in the scenario of automated twist lock disassembly. Full article

Synthetic aperture radar (SAR) imagery plays a vital role in critical applications such as military reconnaissance and disaster monitoring. These applications require high detection accuracy. Therefore, rotated object detection has gained increasing attention. By predicting an object orientation angle, it offers advantages over horizontal bounding boxes, especially for elongated structures such as ships and bridges in SAR scenes. However, challenges such as speckle noise and complex backgrounds in SAR imagery still hinder high-precision detection. To address this, we propose WE-KAN, a novel rotated object detection framework based on wavelet features and Kolmogorov–Arnold network (KAN) prediction. First, we enhance the backbone by incorporating wavelet domain features from SAR grayscale images. The extracted wavelet domain features and image features are fused by a proposed attention module. Second, considering the sensitivity to angle prediction, we design a angle predictor based on KAN. This architecture provides a powerful and dedicated solution for accurate angle regression. Finally, for precise rotated bounding box regression, we employ a joint loss function combining a rotated intersection over union (RIoU) with a Gaussian distance loss function. These designs improve the model’s robustness to noise and its perception of fine object structures. When evaluated on the large-scale public RSAR dataset, our method achieves an AP50 of 70.1 and a mAP of 35.9 under the same training schedule and backbone network, significantly outperforming existing baselines. This demonstrates the effectiveness and robustness of our method for dense, small, and highly oriented objects in complex SAR scenes. Full article

Modeling ship maneuvering dynamics presents numerous challenges, including long-term multi-step recursive error accumulation, insufficient generalization under distributed control rates, and high-frequency disturbance amplification effects. Traditional analytical models heavily rely on vessel-specific trials to characterize strongly nonlinear coupling terms and perform parameter identification, making it difficult to balance efficiency and accuracy under complex operating conditions. This paper presents a ship maneuvering-oriented integration of an invertible Koopman representation and a NeuralODE-based continuous-time predictor. The IKN reconstructs strongly coupled state spaces while enhancing representational invertibility, whereas NeuralODE directly fits the control differential equations governing ship maneuvering dynamics and supports continuous-time prediction. Experiments validate multi-rate control performance under ideal and disturbed data conditions, assessing error accumulation and extrapolation stability through long-term multi-step propagation. Evaluations utilize the KVLCC2-type L7 ship model with a 0.25 s sampling interval and a 200 s prediction horizon, validated against a multi-rate control test set. The results indicate that, compared to the baseline neural ODEs model without IKN, the normalized root mean square error (NRMSE) of state quantities decreased by 12.68% on average. In typical operational scenarios such as constant-speed emergency turns and variable-speed sine sweep maneuvers, the average state NRMSE was 7.96% lower than the LSTM model and 53.85% lower than the IKN–Koopman operator network. Noise experiments demonstrated that when introducing simulated sensor noise at 5%, 10%, and 20% into the dataset, the average state NRMSE remained at 5.98%, 8.24%, and 10.06%, respectively. This confirms the method’s stable prediction performance under varying noise intensities. Full article

Deploying Large Language Models (LLMs) on Maritime Autonomous Surface Ships (MASS) entails a critical trade-off between reasoning depth, inference latency, and hardware constraints. To fill the existing gap, we introduce MARTIAN (Maritime Agent for Real-time Tactical Inference And Navigation), a 14B-parameter decision support agent engineered for edge deployment on standard vessel hardware (e.g., the NVIDIA Jetson AGX Orin). Central to our approach is the Cognitive Core architecture, which utilizes a verified dataset of 21,800 Chain-of-Thought (CoT) instruction–response pairs to align general linguistic capabilities with maritime procedural logic. Empirical evaluations demonstrate that MARTIAN achieves an overall accuracy of 73.23% (SFT only) and 81.16% (SFT + RAG) on the Bilingual Maritime Multiple-Choice Questionnaire (BM-MCQ), a standardized assessment dataset constructed based on Officer of the Watch (OOW) competencies. Notably, the SFT-only configuration attains 78.53% on pure-logic-intensive COLREG tasks—surpassing the 72B-parameter Qwen-2.5 foundation model in this domain—while maintaining a real-time inference latency of 22.4 ms/token. Crucially, our ablation studies support a nuanced Interference Hypothesis: while RAG significantly enhances factual recall in knowledge-intensive domains (boosting total accuracy from 73.23% to 81.16%), it concurrently introduces semantic noise that degrades performance in pure logic reasoning tasks (e.g., COLREG maneuvering accuracy decreases from 78.53% to 77.36%). On the basis of this finding, we identify and empirically motivate a decoupled cognitive design principle that separates procedural reflexes (via SFT) from declarative knowledge (via RAG). While the full implementation of an adaptive routing mechanism is deferred to future work, the ablation results presented herein offer a validated, cost-effective reference architecture for deploying transparent and regulation-compliant AI on resource-constrained merchant vessels. Full article

Detecting ships using Synthetic Aperture Radar (SAR) in coastal areas is still difficult due to the impact of coherent speckle noise from the ocean surface, complex land clutter and having multi-scale target representations in the radar imagery. Most of the existing ship detection algorithms lose important target features during downsampling and have difficulty recovering those features through upsampling, resulting in a high number of false detections and missed detections. In this work, we present a new ship detection algorithm called Synergistic Star-Attentive Network with Dynamic Feature Refinement (S²D-Net). First, we create a new backbone called Multi-scale PCCA-StarNet to generate robust feature representations. Within the backbone we implement a Progressive Channel-Coordinate Attention (PCCA) mechanism to create a synergy between global channel filtering and adaptive coordinate locking to decouple ship textures from granular speckle noise. Second, we create a Dynamic Feature Refinement Neck. We develop a content-aware dynamic upsampler called DySample to replace conventional interpolation to improve fidelity of the upsampled feature of small targets. Further, we design a Star-PCCA Feature Aggregation module which fuses features together. Using star-operations and the PCCA mechanism, this module refines semantic features and removes background clutter while aggregating features across multiple scales. Third, we develop a Lightweight Shared Convolutional Detection Head with Quality Estimation (LSCD-LQE). The LSCD-LQE decreases parameter redundancy by using shared convolutional layers and adds a localization quality estimation branch. Therefore, the LSCD-LQE effectively reduces false positive detections through alignment of classification scores with localization quality based on Intersection over Union (IoU) in difficult coastal environments. Our experimental results, using the SSDD and HRSID datasets, show that S²D-Net produces results comparable to representative ship detection algorithms. In particular, on the challenging HRSID inshore subset, our proposed method achieved a mean average precision (mAP) of 82.7%, which is 6.9% greater than the YOLOv11n baseline ship detection algorithm. These results demonstrate that S²D-Net is superior at detecting small coastal vessels and mitigating the detrimental effects of the nearshore complex environment on the performance ship detection using SAR. Full article

International shipping contributes approximately 3% of global carbon dioxide emissions while serving as the circulatory system of global commerce. The International Maritime Organization’s 2023 GHG Strategy mandates net-zero emissions by or around 2050, with indicative targets requiring a 20–30% reduction by 2030 and a 70–80% reduction by 2040. From a coastal and ocean conservation perspective, these targets represent more than climate mitigation—they offer an opportunity to reduce the maritime sector’s broader ecological footprint, including underwater noise pollution, chemical contamination from antifouling coatings, and the transfer of invasive species through biofouling. This article examines the role of artificial intelligence in supporting maritime decarbonization across multiple domains: voyage optimization, wind-assisted propulsion management, vessel automation, port coordination, predictive maintenance, ship design optimization, and hull maintenance robotics. Critically, the analysis also addresses AI’s own environmental footprint—the substantial energy demands of data centers that power these technologies—and emphasizes the importance of transparent accounting of AI-related emissions. The article proposes research directions that advance both climate objectives and marine ecosystem protection. Full article

Synthetic aperture radar (SAR) serves as a prominent remote sensing (RS) technology, permitting continuous maritime surveillance regardless of weather or time. Although deep learning-based detectors have achieved promising results in SAR imagery, the majority of current algorithms rely on axis-aligned bounding boxes, which are insufficient for accurately representing arbitrarily oriented ships, especially under speckle noise, complex coastal clutter, and real-time deployment constraints. To address this limitation, we propose a YOLO-based oriented ship detector (YOSDet). Specifically, a dynamic aggregation module (DAM) is incorporated into the backbone to enhance feature representation against non-stationary backscattering. An objective-guided detection head (OGDH) is developed to decouple classification and localization, complemented by a localization quality estimator (LQE) to calibrate classification confidence by mitigating the impact of scattering center shifts. Comparative evaluations conducted on three public SAR ship detection benchmarks validate the effectiveness of YOSDet. The proposed model outperforms existing detectors, achieving $mAP$ scores of 96.8%, 88.5%, and 67.3% on the SSDD+, HRSID, and SRSDD-v1.0 datasets, respectively. Furthermore, the consistency of our approach in both nearshore and offshore environments is confirmed through rigorous quantitative and qualitative assessments. Full article

The coherent speckle noise in synthetic aperture radar (SAR) imagery, together with complex sea clutter and large variations in ship target scales, poses significant challenges to accurate and robust ship detection, particularly under strict lightweight constraints required by satellite-borne and airborne platforms. To address this issue, this paper proposes a high-precision lightweight detection network, termed High-Lightweight Net (HLNet), specifically designed for SAR ship detection. The network incorporates a novel multi-scale backbone, Multi-Scale Net (MSNet), which integrates dynamic feature completion and multi-core parallel convolutions to alleviate small-target feature loss and suppress background interference. To further enhance multi-scale feature fusion while reducing model complexity, a lightweight path aggregation feature pyramid network, High-Lightweight Feature Pyramid (HLPAFPN), is introduced by reconstructing fusion pathways and removing redundant channels. In addition, a lightweight detection head, High-Lightweight Head (HLHead), is designed by combining grouped convolutions with distribution focal loss to improve localization robustness under low signal-to-noise ratio conditions. Extensive experiments conducted on the public SSDD and HRSID datasets demonstrate that HLNet achieves mAP₅₀ scores of 98.3% and 91.7%, respectively, with only 0.66 M parameters. Extensive evaluations on the more challenging CSID subset, composed of complex scenes selected from SSDD and HRSID, demonstrate that HLNet attains an mAP₅₀ of 75.9%, outperforming the baseline by 4.3%. These results indicate that HLNet achieves an effective balance between detection accuracy and computational efficiency, making it well-suited for deployment on resource-constrained SAR platforms. Full article

Ships target detection plays a crucial role in safeguarding maritime transportation. However, affected by factors such as ocean waves, extreme weather, and target diversity (e.g., large size differences, arbitrary rotation, and occlusion), existing deep learning-based detection methods struggle to achieve a satisfactory balance among accuracy, speed, and model size in complex marine environments. To address this challenge, this paper proposes a real-time ship detection algorithm (C-YOLO) integrating global perception and multi-scale feature enhancement. First, a Transformer encoder is added before the detection head, which suppresses interference from sea clutter and cloud mist occlusion through long-range dependency modeling, improving the detection of small and occluded ships. Second, a Dual-Effect Focused Residual Fusion Module is designed to replace the backbone’s multi-scale pooling structure, combining the advantages of CBAM (background noise suppression) and SK-Net (dynamic scale adaptation) to simultaneously capture features of ships of different sizes. Finally, a CZIoU loss function is proposed, which integrates constraints on angle, center point, vertex, and area to address rotation, deformation, and multi-scale issues in ship detection. Experimental results on the SeaShips 7000 dataset show that the proposed C-YOLO achieves a Recall of 0.842, mAP@50 of 0.797, and mAP@50:95 of 0.552, outperforming mainstream algorithms such as YOLOv7 (Recall = 0.785, mAP@50 = 0.781), YOLOv9s (Recall = 0.819, mAP@50 = 0.755), and SSD (Recall = 0.802, mAP@50 = 0.833). With 76.75 M parameters and an inference speed of 119 FPS, the model maintains efficient real-time performance while ensuring detection accuracy. This method effectively reduces false detection and missed detection rates in complex scenarios such as port monitoring and maritime traffic control, providing a reliable technical solution for intelligent maritime surveillance and safe navigation—with significant practical value for improving maritime transportation efficiency and reducing safety risks. Full article

Marine vertical centrifugal pump vibration severely impacts equipment reliability and ship structural integrity, with low-frequency vibration being a key challenge for traditional passive isolation systems. To address this, this study aims to optimize the pump base’s vibration isolation performance by integrating symmetrically distributed acoustic black holes (ABHs) and locally resonant metamaterials. A combined numerical and experimental approach was adopted: an H-shaped ABH-coupling plate dynamic model was established and validated, followed by parametric evaluation of base structures, ABH parameters (length, l_ABH), damping layer configurations, and metamaterial arrays. Experimental tests were conducted using simulated pump excitation on the optimal prototype. The results show the optimal configuration—symmetrical ABH (l_ABH= 100 mm) with a full damping layer and 3 × 3 metamaterial array—achieves 11.97 dB low-frequency and 22.01 dB high-frequency vibration suppression, forming a 24.8–27.6 Hz bandgap and 7.43 dB isolation at characteristic frequencies, with an overall 13% performance improvement. This work verifies the feasibility of the symmetrical ABH–metamaterial hybrid system, providing a novel technical solution for high-performance vibration-noise reduction in marine power equipment. Full article

To address the data quality challenges in ship maintenance data, such as high missing rates, anomalous noise, and multi-source heterogeneity, this paper proposes a data quality enhancement method based on a multi-strategy enhanced Harris Hawks Optimization algorithm for optimizing the Kolmogorov–Arnold Stacked Denoising Autoencoder. First, leveraging the Kolmogorov–Arnold theory, the fixed activation functions of the traditional Stacked Denoising Autoencoder are reconstructed into self-learnable B-spline basis functions. Combined with a grid expansion technique, the KASDAE model is constructed, significantly enhancing its capability to represent complex nonlinear features. Second, the Harris Hawks Optimization algorithm is enhanced by incorporating a Logistic–Tent compound chaotic map, an elite hierarchy strategy, and a nonlinear logarithmic decay mechanism. These improvements effectively balance global exploration and local exploitation, thereby increasing the convergence accuracy and stability for hyperparameter optimization. Building on this, an IHHO-KASDAE collaborative cleaning framework is established to achieve the repair of anomalous data and the imputation of missing values. Experimental results on a real-world ship maintenance dataset demonstrate the effectiveness of the proposed method: it achieves an 18.3% reduction in reconstruction mean squared error under a 20% missing rate compared to the best baseline method; attains an F1-score of 0.89 and an AUC value of 0.929 under a 20% anomaly rate; and stabilizes the final fitness value of the IHHO optimizer at 0.0216, which represents improvements of 31.7%, 25.6%, and 12.2% over the Particle Swarm Optimization, Differential Evolution, and the original HHO algorithm, respectively. The proposed method outperforms traditional statistical methods, deep learning models, and other intelligent optimization algorithms in terms of reconstruction accuracy, anomaly detection robustness, and algorithmic convergence stability, thereby providing a high-quality data foundation for subsequent applications such as maintenance cost prediction and fault diagnosis. Full article

We developed a hybrid system combining deep learning-based recognition with fuzzy inference to enhance the detection, recognition, and identification of maritime targets. In the system, deep learning provides strong feature extraction, while fuzzy logic mitigates uncertainty in low-visibility or occluded conditions. The system uses confidence score, screen ratio, and estimated distance as input and processes them through fuzzy inference with triangular membership functions and center of area defuzzification. This integration improves decision robustness and suppresses input noise. Experimental results demonstrate enhanced stability and reduced misjudgment in dynamic maritime environments, highlighting the applicability of a hybrid deep learning–fuzzy inference systems to intelligent ships and unmanned maritime vehicle sensing tasks. Full article