Search Results (173)

The growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain. Full article

The colonization of Legionella spp. in engineered water systems constitutes a major public health threat. In this study, a six-year environmental surveillance (2020–2025) of Legionella colonization in five different types of facilities in Crete, Greece is presented, including hotels, passenger ships, primary healthcare facilities, public hospitals, and private clinics. A total of 1081 water samples were collected and analyzed, and the overall positivity was calculated using culture-based methods. Only 16.46% of the samples exceeded the regulatory limit (>10³ CFU/L) in the total sample, with 44.59% overall Legionella positivity. Colonization by facility category showed the highest rates in primary healthcare facilities with 85.96%, followed by public hospitals (46.36%), passenger ships with 36.93%, hotels with 38.08%, and finally private clinics (21.42%). The association of environmental risk factors with Legionella positivity revealed a strong effect at hot water temperatures < 50 °C (RR = 2.05) and free chlorine residuals < 0.2 mg/L (RR = 2.22) (p < 0.0001). Serotyping analysis revealed the overall dominance of Serogroups 2–15 of L. pneumophila; nevertheless, Serogroup 1 was particularly prevalent in hospitals, passenger ships, and hotels. Based on these findings, the requirement for continuous environmental monitoring and risk management plans with preventive thermochemical controls tailored to each facility is highlighted. Finally, operational disruptions, such as those experienced during the COVID-19 pandemic, especially in primary care facilities and marine systems, require special attention. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

This study presents a new intelligent aquatic farming surveillance system that tackles real-time monitoring challenges in the industry. The main technical break-throughs of this system are evident in four key aspects: First, it achieves the smooth integration of remotely operated vehicles (ROVs), sensors, and real-time data transmission. Second, it uses a mobile communication architecture with buoy relay stations for distributed edge computing. This design supports future upgrades to Beyond 5G and satellite networks for deep-sea applications. Third, it features a multi-terminal control system that supports computers, smartphones, smartwatches, and centralized hubs, effectively enabling monitoring anytime, anywhere. Fourth, it incorporates a cost-effective modular design, utilizing commercial hardware and innovative system integration solutions, making it particularly suitable for farms with limited resources. The data indicates that the system’s 4G connection is both stable and reliable, demonstrating excellent performance in terms of data transmission success rates, control command response delays, and endurance. It has successfully processed 324,800 data transmission events, thoroughly validating its reliability in real-world production environments. This system integrates advanced technologies such as the Internet of Things, mobile communications, and multi-access control, which not only significantly enhance the precision oversight capabilities of marine farming but also feature a modular design that allows for future expansion into satellite communications. Notably, the system reduces operating costs while simultaneously improving aquaculture efficiency, offering a practical and intelligent solution for small farmers in resource-limited areas. Full article

Countries are embracing intelligent transportation systems (ITSs), the application of information and communication technologies to transportation, to address growing challenges in urban mobility, congestion, safety, and sustainability. Architecture Reference for Cooperative and Intelligent Transportation (ARC-IT) is a notable ITS framework comprising Enterprise, Functional, Physical, and Communications Views (or layers). This review focuses on the Communications View, examining how publish/subscribe middleware enhances ITS through the communication layer. It identified application areas across ITS infrastructure, transportation modes, and communication technologies, and highlights key challenges. In the infrastructure domain, publish/subscribe middleware enhances responsiveness and real-time processing in systems such as traffic surveillance, VANETs, and road sensor networks, especially when replacing legacy infrastructure is cost-prohibitive. Moreover, the middleware supports scalable, low-latency communication in land, air, and marine modes, enabling public transport coordination, cooperative driving, and UAV integration. At the communications layer, publish/subscribe systems facilitate interoperable, delay-tolerant data dissemination over heterogeneous platforms, including 4G/5G, ICN, and peer-to-peer networks. However, integrating publish/subscribe middleware in ITS has several challenges, including privacy risks, real-time data constraints, fault tolerance, bandwidth limitations, and security vulnerabilities. This paper provides a domain-informed foundation for researchers and practitioners developing resilient, scalable, and interoperable communication systems in next-generation ITSs. Full article

Aspergillus spp., particularly A. fumigatus, are increasingly reported as emerging pathogens in cetaceans, yet their clinical and ecological relevance remains poorly characterized. This systematic review synthesizes evidence from 34 studies involving 106 animals, identifying respiratory, neurological, and otic infections as the most frequent presentations with potential interspecies tropism. Invasive disease, frequently fatal, was linked to co-infections—especially with morbillivirus—and environmental stressors such as pollution- and climate-related immune suppression. Despite cetaceans’ role as sentinel species, antifungal susceptibility testing and species-level identification were inconsistently performed. Additionally, azole-resistant A. fumigatus strains were isolated from wild porpoises, indicating environmental antifungal exposure and potential public health implications. Aspergillosis remains underdiagnosed in free-ranging populations, particularly in remote or pelagic species. Conservation implications were scarcely addressed, despite evidence suggesting that fungal disease may contribute to morbidity, stranding, and population impact. This review underscores the need for enhanced surveillance, integrative diagnostics, and recognition of fungal pathogens in a One Health framework. The growing intersection of climate change, emerging mycoses, and wildlife conservation positions Aspergillus infections in cetaceans as both a marine mammal health concern and an ecological indicator of broader environmental changes. Full article

The rise of the Internet of Things (IoT) has made it possible to explore different types of communication, such as underwater IoT (UIoT). This new paradigm allows the interconnection of ships, boats, coasts, objects in the sea, cameras, and animals that require constant monitoring. The use of sensors for environmental monitoring, tracking marine fauna and flora, and monitoring the health of aquifers requires the integration of heterogeneous technologies as well as wireless communication technologies. Aquatic mobile sensor nodes face various limitations, such as bandwidth, propagation distance, and data transmission delay issues. Owing to their versatility, wireless sensor networks support remote monitoring and surveillance. In this work, an architecture for a general packet radio service (GPRS) wireless sensor network is presented. The network is used to monitor the geographic position over the coastal area of the Gulf of Mexico. The proposed architecture integrates cellular technology and some ad hoc network configurations in a single device such that coverage is improved without significantly affecting the energy consumption, as shown in the results. The network coverage and energy consumption are evaluated by analyzing the attenuation in a proposed channel model and the autonomy of the electronic system, respectively. Full article

Vibrio parahaemolyticus is a prevalent pathogen responsible for foodborne diseases in coastal regions. Understanding its dynamic relationship with various meteorological and marine factors is crucial for predicting outbreaks of bacterial foodborne illnesses. This study analyzes the occurrence of V. parahaemolyticus-induced foodborne illness in Zhejiang Province, China, from 2014 to 2018, using an 8-day time unit based on the temporal characteristics of marine products. The detection rate of V. parahaemolyticus exhibited a distinct cyclical pattern, peaking during the summer months. Meteorological and marine factors showed varying lag effects on the detection of V. parahaemolyticus, with specific lag periods as follows: sunshine duration (3 weeks), air temperature (3 weeks), total precipitation (8 weeks), relative humidity (7 weeks), sea surface temperature (1 week), and sea surface salinity (8 weeks). The SARIMAX model, which incorporates both marine and climatic factors, was developed to facilitate short-term forecasts of V. parahaemolyticus detection rates in coastal cities. The model’s performance was evaluated, and the actual values consistently fell within the 95% confidence interval of the predicted values, with a mean absolute error (MAE) of 0.047, indicating high accuracy. This framework provides both theoretical and practical insights for predicting and preventing future foodborne disease outbreaks. These findings can support food industry stakeholders—such as seafood suppliers, restaurants, regulatory agencies, and healthcare institutions—in anticipating high-risk periods and implementing targeted measures. These include enhancing cold chain management, conducting timely seafood inspections, strengthening cross-contamination controls during seafood processing, dynamically adjusting market surveillance intensity, and improving hygiene practices. In addition, hospitals and local health departments can use the model’s forecasts to allocate medical resources such as beds, medications, and staff in advance to better prepare for seasonal surges in foodborne illness. Full article

Unmanned Surface Vehicles (USVs) have emerged as vital tools in marine monitoring and management due to their high efficiency, low cost, and flexible deployment capabilities. This paper presents a systematic review focusing on four core areas of USV applications: communication networking, navigation, control, and data-driven operations. First, the characteristics and challenges of acoustic, electromagnetic, and optical communication methods for USV networking are analyzed, with an emphasis on the future trend toward multimodal communication integration. Second, a comprehensive review of global navigation, local navigation, cooperative navigation, and autonomous navigation technologies is provided, highlighting their applications and limitations in complex environments. Third, the evolution of USV control systems is examined, covering group control, distributed control, and adaptive control, with particular attention given to fault tolerance, delay compensation, and energy optimization. Finally, the application of USVs in data-driven marine tasks is summarized, including multi-sensor fusion, real-time perception, and autonomous decision-making mechanisms. This study aims to reveal the interaction and coordination mechanisms among communication, navigation, control, and data-driven operations from a system integration perspective, providing insights and guidance for the intelligent operations and comprehensive applications of USVs in marine environments. Full article

Acoustic sensing has many applications in engineering, one of which is fiber-optic hydrophones (FOHs). Conventional piezoelectric hydrophones face limitations related to size, electromagnetic interference, corrosion, and narrow operating bandwidth. Fiber-optic hydrophones, particularly those employing distributed feedback (DFB) lasers, offer a compelling alternative due to their mechanical flexibility, resistance to harsh conditions, and broad detection range. DFB lasers are highly sensitive to external perturbations such as temperature and strain, enabling the precise detection of underwater acoustic signals by monitoring the resultant shifts in lasing wavelength. This paper presents an enhanced interrogation mechanism that leverages Mach–Zehnder interferometers to translate wavelength shifts into measurable phase deviations, thereby providing cost-effective and high-resolution phase-based measurements. A dual interferometric setup is integrated with a standard demodulation algorithm to extend the dynamic range of these sensing systems. The experimental results demonstrate a substantial improvement in performance, with the dynamic range increasing from 125 dB to 139 dB at 1 kHz without degrading the noise floor. This enhancement significantly expands the utility of FOH-based systems in underwater environments, supporting applications such as underwater surveillance, submarine communication, and marine ecosystem monitoring. Full article

Illegal, unreported, and unregulated (IUU) fishing significantly threatens marine ecosystems, disrupts the ecological balance of the oceans, and poses serious challenges to global fisheries management. This contribution presents the efficacy of China’s summer fishing moratorium using BeiDou vessel monitoring system (VMS) data from 2805 fishing vessels in the East China Sea and Yellow Sea, integrated with a deep learning framework for spatiotemporal analysis. A preprocessing protocol addressing multidimensional noise in raw VMS datasets was developed, incorporating velocity normalization and gap filling to ensure data reliability. The CNN-BiLSTM hybrid model emerged as optimal for fishing behavior classification, achieving 89.98% accuracy and an 87.72% F1 score through synergistic spatiotemporal feature extraction. Spatial analysis revealed significant policy-driven reductions in fishing intensity during the moratorium (May–August), with hotspot areas suppressed to sporadic coastal distributions. However, concentrated vessel activity in Zhejiang’s nearshore waters suggested potential illegal fishing. Post-moratorium, fishing hotspots expanded explosively, peaking in October and clustering in Yushan, Zhoushan, and Yangtze River estuary fishing grounds. Quarterly patterns identified autumn–winter 2021 as peak fishing seasons, with hotspots covering >80% of East China Sea grounds. The framework enables real-time fishing state detection and adaptive spatial management via dynamic closure policies. The findings underscore the need for strengthened surveillance during moratoriums and post-ban catch regulation to mitigate overfishing risks. Full article

Multi-feature-based maritime radar target detection algorithms often rely on statistical models to accurately characterize sea clutter variations. However, it is a big challenge for these models to accurately characterize sea clutter due to the complexity of the marine environment. Moreover, the distribution of training samples captured from dynamic observation conditions is imbalanced. These multi-features extracted from inaccurate models and imbalanced data lead to overfitting or underfitting and degrade detection performance. To tackle these challenges, this paper proposes a mechanism–data collaborative method using the scattering coefficient as a representative feature. By establishing a mapping relationship between measured data and empirical values, the classical model is piecewise fitted to the measured data. A fusion strategy is then used to compensate for interval discontinuities, enabling accurate characterization of clutter properties in the current maritime environment. Based on the characterized clutter properties, a hybrid feature selection strategy is further proposed to construct a diverse and compact training sample set by integrating global density distribution with local gradient variation. The experiments based on field data are included to evaluate the effectiveness of the proposed method including sea clutter characterization accuracy and training sample selection across various scenarios. Experimental results demonstrate that the proposed method provides a more accurate representation of sea clutter characteristics. Moreover, the detectors trained with the proposed training samples exhibit strong generalization capability across diverse maritime environments under the condition of identical features and classifiers. These achievements highlight the importance of accurate sea clutter modeling and optimal training sample selection in improving target detection performance and ensuring the reliability of radar-based maritime surveillance. Full article

Due to the excellent concealment and high mobility, multiple small spherical underwater robots are essential for near coast defending missions. The formation of multiple small spherical underwater robots is particularly effective for tasks such as patrolling, reconnaissance, surveillance, and capturing sensitive targets. Moreover, some tasks need higher flexibility and mobility, such as reconnaissance, surveillance, or target encirclement at fixed locations. For this purpose, this paper proposes a position-based formation mechanism which is easily deployed for multiple spherical robots. A position planning method during the formation process is designed. This method creatively integrates the virtual linkage strategy with an improved consensus algorithm and the artificial potential field (APF) method. The virtual linkage strategy is in charge of computing the global formation desired target positions for robots according to the predefined position of the virtual leader joint. The improved consensus algorithm and APF are responsible for planning the local desired positions between two formation desired target positions, which is able to prevent collisions and excessive communication distance between robots. In order to verify the effectiveness of the proposed formation mechanism, adequate simulations and experiments are conducted. Thereby, the proposed formation frame offers great potential for future practical marine operations of the underwater multi-small robot systems. Full article

Conventional multi-object tracking approaches frequently exhibit performance degradation in marine radar (MR) imagery due to complex environmental challenges. To overcome these limitations, this paper proposes ShipMOT, an innovative multi-object tracking framework specifically engineered for robust maritime target tracking. The novel architecture features three principal innovations: (1) A dedicated CNN-based ship detector optimized for radar imaging characteristics; (2) A novel Nonlinear State Augmentation (NSA) filter that mathematically models ship motion patterns through nonlinear state space augmentation, achieving a 41.2% increase in trajectory prediction accuracy compared to conventional linear models; (3) A dual-criteria Bounding Box Similarity Index (BBSI) that integrates geometric shape correlation and centroid alignment metrics, demonstrating a 26.7% improvement in tracking stability under congested scenarios. For a comprehensive evaluation, a specialized benchmark dataset (Radar-Track) is constructed, containing 4816 annotated radar images with scenario diversity metrics, including non-uniform motion patterns (12.7% of total instances), high-density clusters (>15 objects/frame), and multi-node trajectory intersections. Experimental results demonstrate ShipMOT’s superior performance with state-of-the-art metrics of 79.01% HOTA and 88.58% MOTA, while maintaining real-time processing at 32.36 fps. Comparative analyses reveal significant advantages: 34.1% fewer ID switches than IoU-based methods and 28.9% lower positional drift compared to Kalman filter implementations. These advancements establish ShipMOT as a transformative solution for intelligent maritime surveillance systems, with demonstrated potential in ship traffic management and collision avoidance systems. Full article

In the ocean environment, passive acoustic monitoring (PAM) is an important technique for the surveillance of cetacean species. Manual detection for a large amount of PAM data is inefficient and time-consuming. To extract useful features from a large amount of PAM data for classifying different cetacean species, we propose an automatic detection and unsupervised clustering-based classification method for cetacean vocal signals. This paper overcomes the limitations of the traditional threshold-based method, and the threshold is set adaptively according to the mean value of the signal energy in each frame. Furthermore, we also address the problem of the high cost of data training and labeling in deep-learning-based methods by using the unsupervised clustering-based classification method. Firstly, the automatic detection method extracts vocal signals from PAM data and, at the same time, removes clutter information. Then, the vocal signals are analyzed for classification using a clustering algorithm. This method grabs the acoustic characteristics of vocal signals and distinguishes them from environmental noise. We process 194 audio files in a total of 25.3 h of vocal signal from two marine mammal public databases. Five kinds of vocal signals from different cetaceans are extracted and assembled to form 8 datasets for classification. The verification experiments were conducted on four clustering algorithms based on two performance metrics. The experimental results confirm the effectiveness of the proposed method. The proposed method automatically removes about 75% of clutter data from 1581.3MB of data in audio files and extracts 75.75 MB of the features detected by our algorithm. Four classical unsupervised clustering algorithms are performed on the datasets we made for verification and obtain an average accuracy rate of 84.83%. Full article