Journal of Marine Science and Engineering

This paper addresses the critical challenge of detecting weak, small targets in sonar intensity images for linear-array active sonar, where target signatures are not only obscured by low signal-to-interference ratio (SIR) but also strongly resemble structural interference arising from beamforming processing. We propose an end-to-end detection method that integrates controllable simulation with spatiotemporal structure-aware modeling. First, a physics-informed simulation system is constructed, centered on the Bellhop ray-tracing model. It incorporates multiple environmental effects, including multi-highlight targets, spectrally shaped noise, range-dependent reverberation, discrete scatterers, multipath propagation, and platform perturbations. Through closed-loop SIR calibration and point spread function (PSF)-constrained automatic annotation, a high-fidelity dataset with traceable parameters is generated. Second, the YOLOv8-Mamba-P2 detection network is designed. It introduces gated long-range spatial mixing modules (inspired by Mamba) to model global context and enhance the ability to discriminate interference structures, and extends a P2 small-scale detection branch to improve the perception and localization capabilities for weak targets. This enables precise target detection within complex backgrounds. Experimental results demonstrate the algorithm’s superior performance in low-SIR and strong reverberation conditions, achieving significant improvements in recall and localization accuracy while maintaining real-time inference efficiency, offering a promising framework for sonar target detection under the simulated conditions considered, with potential applicability to complex marine environments pending further real-world validation.

Mercury (Hg) contamination in deep-sea ecosystems is of increasing concern due to its strong bioaccumulation potential and implications for seafood safety. However, depth-resolved information on Hg speciation and tissue-specific accumulation in deep-sea fish remains limited, particularly in semi-enclosed marginal seas such as the East Sea of Korea. In this study, total mercury (THg) and methylmercury (MeHg) concentrations were quantified in multiple tissues (muscle, liver, gill, bone, and skin) of deep-sea fish collected across a pronounced depth gradient (100–1300 m). Hg concentrations increased significantly with sampling depth (p < 0.05), indicating depth-driven enrichment processes. MeHg accounted for 61.8–87.4% of THg and predominated in muscle and liver, highlighting its toxicological relevance. Human health risk assessment based on Estimated Daily Intake (EDI) and Target Hazard Quotient (THQ) suggested that average exposure levels remained below international safety thresholds. Nevertheless, several deep-sea taxa (e.g., Lycodes tanakae and Malacocottus gibber) approached cautionary levels under high-consumption scenarios. These findings demonstrate that habitat depth is a key ecological driver of Hg accumulation in deep-sea fish and underscore the importance of depth-resolved, species-specific monitoring for effective seafood safety management as deep-sea fisheries expand.

Marine target classification is a key technology for unmanned surface vehicles (USVs) to perform ocean surveillance. Traditional maritime target classification methods require improvements in both accuracy and processing speed when handling classification tasks. In this paper, a distributed maritime target classification (DMTC) method based on broad learning and MobilityFirst is proposed. Firstly, a multi-model collaborative classification and fusion framework is proposed to achieve feature consistency fusion. Secondly, to enhance the security and privacy of communication in autonomous surface vehicles, the MobilityFirst approach is employed to improve information complementarity among multiple models within the distributed framework. Finally, the broad learning system, as the model’s classification layer, reduces the training complexity. Extensive experimental results demonstrate that this proposed approach surpasses single-model and distributed methods in accuracy, F1 score, and the area under the precision–recall curve (AUPR). This approach offers a clear advantage in multi-ship classification tasks while simultaneously enhancing the model’s generalization capability.