Due to the strain on land resources, marine energy development is expanding, in which the submarine cable occupies an important position. Therefore, periodic inspections of submarine cables are required. Submarine cable inspection is typically performed using underwater vehicles equipped with cameras. However, the motion of the underwater vehicle body, the dim light underwater, and the property of light propagation in water lead to problems such as the blurring of submarine cable images, the lack of information on the position and characteristics of the submarine cable, and the blue–green color of the images. Furthermore, the submarine cable occupies a significant portion of the image as a linear entity. In this paper, we propose an improved YOLO-SC (YOLO-Submarine Cable) detection method based on the YOLO-V3 algorithm, build a testing environment for submarine cables, and create a submarine cable image dataset. The YOLO-SC network adds skip connections to feature extraction to make the position information of submarine cables more accurate, a top-down downsampling structure in multi-scale special fusion to reduce the network computation and broaden the network perceptual field, and lightweight processing in the prediction network to accelerate the network detection. Under laboratory conditions, we illustrate the effectiveness of these modifications through ablation studies. Compared to other algorithms, the average detection accuracy of the YOLO-SC model is increased by up to 4.2%, and the average detection speed is decreased by up to 1.616 s. The experiments demonstrate that the YOLO-SC model proposed in this paper has a positive impact on the detection of submarine cables.

Acoustic metamaterials, artificial composite structures with exotic material properties used to control elastic waves, have become a new frontier in physics, materials science, engineering and chemistry. In this paper, the research progress and development prospect of acoustic metamaterials are reviewed. Related studies on passive acoustic metamaterials and active acoustic metamaterials are introduced and compared. Additionally, we discuss approaches to material structure design, including topology optimization approaches, as well as bio-inspired and fractal geometry-based approaches to structure design. Finally, we summarize and look forward to the prospects and directions of acoustic metamaterial research. With the development of additive manufacturing technology, the research potential of acoustic metamaterials is huge.

Underwater vision-based detection plays an increasingly important role in underwater security, ocean exploration and other fields. Due to the absorption and scattering effects of water on light, as well as the movement of the carrier, underwater images generally have problems such as noise pollution, color cast and motion blur, which seriously affect the performance of underwater vision-based detection. To address these problems, this study proposes an end-to-end marine organism detection framework that can jointly optimize the image enhancement and object detection. The framework uses a two-stage detection network with dynamic intersection over union (IoU) threshold as the backbone and adds an underwater image enhancement module (UIEM) composed of denoising, color correction and deblurring sub-modules to greatly improve the framework’s ability to deal with severely degraded underwater images. Meanwhile, a self-built dataset is introduced to pre-train the UIEM, so that the training of the entire framework can be performed end-to-end. The experimental results show that compared with the existing end-to-end models applied to marine organism detection, the detection precision of the proposed framework can improve by at least 6%, and the detection speed has not been significantly reduced, so that it can complete the high-precision real-time detection of marine organisms.

Underwater magnetic surveying serves as the fundamental prerequisite for detecting sensitive underwater targets and magnetically susceptible submerged objects. However, when utilizing magnetic gradient tensor measurements for underwater positioning, the stability of the measurement apparatus can be significantly affected by hydrodynamic disturbances in the underwater environment, thereby having a substantial impact on data quality. Autonomous Underwater Vehicles (AUV) are unmanned underwater robots designed to independently perform various tasks and operations in underwater environments. In order to ensure the quality of data collection, this paper proposes a structure utilizing an Autonomous Underwater Vehicles platform equipped with a three-component magnetic gradiometer. This structure employs second-order tensor positioning algorithms and vertical gradient positioning algorithms, coupled with the Autonomous Underwater Vehicles’s inherent vertical profile motion, to effectively achieve the precise positioning of underwater cables. Simulation results indicate that, in the absence of geomagnetic background noise, both horizontal and vertical structures yield favorable positioning results. However, when introducing background noise of 40,000 nT, the horizontal structure exhibits larger positioning errors, whereas the vertical structure demonstrates smaller errors. Experimental results show that in near-field scenarios, both structures achieve relatively similar positioning accuracy. Nevertheless, under identical distances in the far field, the vertical structure reduces errors by a minimum of 30.78% compared to the horizontal structure, thereby confirming the feasibility of integrating magnetic gradient tensor measurement structures with Autonomous Underwater Vehicles platforms.

To overcome the shortcoming wherein the accuracy of subsea cable detection can be affected by the determination of the bias vector, scale factors, and non-orthogonality corrections of the vector magnetometer, a real-time attitude-independent route tracking method for subsea power cables is investigated theoretically and experimentally by means of scalar magnetic field checking. The measurement of the magnetic field B_c produced by the current in a cable is made immune to the influence of the platform attitude by extracting the component of B_c along the geomagnetic field using a high-bandwidth self-oscillating optically pumped magnetometer. The self-oscillating frequency is proved to be independent of the attitude of the magnetometer with the theoretical model. Experiments are carried out to test the attitude-independent performance, and the effectiveness of route tracking is verified by the results of the sea experiment. The proposed method will effectively improve the ability to locate subsea cables under high sea conditions.

Based on the theory of magnetoacoustic coupled dynamics, the purpose of this paper is to evaluate the dynamic stress concentration near an elliptical opening in exponential-gradient piezomagnetic materials under the action of antiplane shear waves. By the wave function expansion, the solutions for the acoustic wave fields and magnetic fields can be obtained. Stress analysis is performed by the complex function method and the conformal mapping method, which are used to solve the boundary conditions problem, and is used to express the dynamic stress concentration coefficient (DSCC) theoretically. As cases, numerical results of DSCCs are plotted and discussed with different incident wave numbers and material parameters by numerical simulation. Compared with circular openings, elliptical openings are widely used in material processing techniques and are more difficult to solve. Numerical results show that the dynamic stress concentration coefficient at the elliptical opening is strongly dependent on various parameters, which indicates that the elliptical opening is more likely to cause crack and damage to exponential-gradient piezomagnetic materials.