Underwater dynamic targets often display significant blurriness in their forward-looking sonar imagery, accompanied by sparse feature representation. This phenomenon presents several challenges, including disturbances in the trajectories of underwater targets and alterations in target identification throughout the tracking process, thereby complicating the continuous monitoring of moving targets. This research proposes a new framework for underwater acoustic data interpolation and underwater object tracking. Considering the character of underwater acoustic images, a Swin Transformer is integrated in the architecture of the YOLOv5 network; then, an improved Deep Simple Online and Real-time Tracking method is developed. By enlarging the bounding box output generated by the detector and subsequently integrating it into the tracker, the sensing horizon of the tracker is broadened. This strategy enables the extraction of noise features surrounding the target, thereby augmenting the target’s characteristics and improving the stability of the tracking process. The experimental results demonstrate that the proposed method effectively reduces the frequency of changes in target identification numbers, minimizes the occurrence of trajectory interruptions, and decreases the overall percentage of trajectory interruptions. Additionally, it significantly enhances tracking stability, particularly in scenarios involving intersecting target paths and encounters.

Detection of targets on sea surfaces is an important area of application that can bring great benefits to the management and control systems in marine environments. However, there are few open-source datasets accessible for the purpose of object detection on seas and rivers. In this paper, a study is conducted on the improved detection algorithms based on the YOLOv5 model. The dataset for the tests contains ten categories of typical objects that are commonly seen in the contexts of seas, including ships, devices, and structures. Multiple augmentation methods are employed in the pre-processing of the input data, which are verified to be effective in enhancing the generalization ability of the algorithm. Moreover, a new form of the loss function is proposed that highlights the effects of the high-quality boxes during training. The results demonstrate that the adapted loss function contributes to a boost in the model performance. According to the ablation studies, the synthesized methods raise the inference accuracy by making up for several shortcomings of the baseline model for the detection tasks of single or multiple targets from varying backgrounds.

To address the challenges of high computational cost and lengthy design cycles in the high-precision optimization of ray-like underwater gliders, this study proposes a high-accuracy, low-cost parametric modeling and optimization method. The proposed framework begins by extracting the characteristic contours of the manta ray and reconstructing the airfoil sections using the Class-Shape Transformation (CST) method, resulting in a flexible parametric geometry capable of smooth deformation. High-fidelity Computational Fluid Dynamics (CFD) simulations are employed to evaluate the hydrodynamic characteristics, and detailed flow field analyses are conducted to identify the most influential geometric features affecting lift and drag performance. On this basis, a Kriging-based sequential optimization framework is developed. The surrogate model is adaptively refined through dynamic infilling of sample points based on combined Mean Squared Prediction (MSP) and Expected Improvement (EI) criteria, thus improving optimization efficiency while maintaining predictive accuracy. Comparative case studies demonstrate that the proposed method achieves a 116% improvement in lift-to-drag ratio and a more uniform flow distribution, confirming its effectiveness in enhancing both design accuracy and computational efficiency. The results indicate that this approach provides a practical and efficient tool for the parametric design and hydrodynamic optimization of bio-inspired underwater vehicles.

As the core equipment for in situ deep-sea scientific research, the design quality and operational performance of the sampling platform determine the efficiency of deep-sea operations, including long-term continuous observation, high-quality sampling and preservation, and in situ experimental studies. To address technical bottlenecks commonly observed in conventional platforms, including track sinkage, excessive motion drag, and low propulsion efficiency, this study proposes the design and development of a novel sampling platform with tracked-propeller dual-mode propulsion, deployable from either a surface vessel or a large manned submersible, capable of high-throughput, multi-sequence fidelity water sampling, in situ sediment incubation, and seabed mudstone sampling in deep sea. Through hydrodynamic performance analysis and design optimization, both lightweight design and drag reduction were achieved. Furthermore, load verification of the main frame under multiple operating conditions was conducted; results demonstrate that the structure meets strength and stiffness requirements and ensures reliability in typical service environments. This study provides a theoretical basis and technical reference for the development of similar deep-sea sampling platforms and holds substantial engineering application value.