Aiming at the problems of the underwater existence of some targets with relatively small size, low contrast, and a lot of surrounding interference information, which lead to a high leakage rate and low recognition accuracy, a new improved YOLOv7 underwater target detection algorithm is proposed. First, the original YOLOv7 anchor frame information is updated by the K-Means algorithm to generate anchor frame sizes and ratios suitable for the underwater target dataset; second, we use the PConv (Partial Convolution) module instead of part of the standard convolution in the multi-scale feature fusion module to reduce the amount of computation and number of parameters, thus improving the detection speed; then, the existing CIou loss function is improved with the ShapeIou_NWD loss function, and the new loss function allows the model to learn more feature information during the training process; finally, we introduce the SimAM attention mechanism after the multi-scale feature fusion module to increase attention to the small feature information, which improves the detection accuracy. This method achieves an average accuracy of 85.7% on the marine organisms dataset, and the detection speed reaches 122.9 frames/s, which reduces the number of parameters by 21% and the amount of computation by 26% compared with the original YOLOv7 algorithm. The experimental results show that the improved algorithm has a great improvement in detection speed and accuracy.

Aiming to solve the problems of large-scale changes, the dense occlusion of ship targets, and a low detection accuracy caused by challenges in the localization and identification of small targets, this paper proposes a ship target-detection algorithm based on the improved YOLOv5s model. First, in the neck part, a weighted bidirectional feature pyramid network is used from top to bottom and from bottom to top to solve the problem of a large target scale variation. Second, the CNeB2 module is designed to enhance the correlation of coded spatial space, reduce interference from redundant information, and enhance the model’s ability to distinguish dense targets. Finally, the Separated and Enhancement Attention Module attention mechanism is introduced to enhance the proposed model’s ability to identify and locate small targets. The proposed model is verified by extensive experiments on the sea trial dataset. The experimental results show that compared to the YOLOv5 algorithm, the accuracy, recall rate, and mean average precision of the proposed algorithm are increased by 1.3%, 1.2%, and 2%, respectively; meanwhile, the average precision value of the proposed algorithm for the dense occlusion category is increased by 4.5%. In addition, the average precision value of the proposed algorithm for the small target category is increased by 5% compared to the original YOLOv5 algorithm. Moreover, the detection speed of the proposed algorithm is 66.23 f/s, which can meet the requirements for detection speed and ensure high detection accuracy and, thus, realize high-speed and high-precision ship detection.

Ship heave motion measurement is crucial for ensuring vessel stability, navigation precision, and maritime engineering safety. In order to achieve accurate heave motion measurement, a method based on an adaptive digital high–pass filter is proposed. The approach involves constructing a ship heave motion model, conducting an analysis of heave motion, determining the optimal cutoff frequency for the adaptive filter based on an analysis of filtering and sensor errors, and designing an adaptive delay–free digital high–pass filter. Through simulation experiments in various sea conditions and platform tests, the method demonstrates superior performance. In comparison to fixed–parameter complementary filters, it exhibits a reduction of over 50% in maximum error and mean square error.

Recently, sound-based diagnosis systems have been given much attention in many fields due to the advantages of their simple structure, non-touching measurement style, and low-power dissipation. In order to improve the efficiency of coal production and the safety of the coal mining process, accurate information is always essential. It is indicated that the sound signal produced during the cutting process of the coal mining shearer contains much cutting pattern identification information. In this paper, the original acoustic signal is first collected through an industrial microphone. To analyze the signal deeply, an adaptive Hilbert–Huang transform (HHT) was applied to decompose the sound to several intrinsic mode functions (IMFs) to subsequently acquire 1024 Hilbert marginal spectrum points. The 1024 time-frequency nodes were reorganized as a 32 × 32 feature map. Moreover, the LeNet-5 convolutional neural network (CNN), with three convolution layers and two sub-sampling layers, was used as the cutting pattern recognizer. A simulation example, with 10,000 training samples and 2000 testing samples, was conducted to prove the effectiveness of the proposed method. Finally, 1971 testing sound series were recognized accurately through the trained CNN and the proposed method achieved an identification rate of 98.55%.

Aiming at the underwater docking process of an autonomous underwater vehicle (AUV) and conical docking bay, this paper systematically analyzes the influence of docking geometry parameters and ocean current conditions on hydrodynamic characteristics. By establishing a three-dimensional mathematical model and using computational fluid dynamics (CFD) methods, this study focused on investigating the effects of the taper angle and diameter of the docking chamber inlet, as well as the magnitude and direction angle of ocean currents, on the docking resistance, lift, velocity field, and pressure field distribution of an AUV. The results show the following: increasing the dock inlet taper can reduce the AUV docking drag; the dock inlet diameter is positively correlated with the AUV docking drag; the larger the current speed is, the more drastic the change of the AUV drag is; the larger the current direction angle is, the larger the velocity difference between the upper and lower flow fields of the AUV is, leading to a significant lift effect. The research results provide a theoretical basis for the structure optimization and control strategy design of AUV underwater docking systems.

Digital twin technology, as a data-driven and model-driven innovation means, plays a crucial role in the process of digital transformation and intelligent upgrading of the marine industry, helping the industry to move towards a new stage of more intelligent and efficient development. In order to solve the defects of the Autonomous Underwater Vehicle (AUV) underwater support platform structure deformation field, digital twin technology and model reduction technology are applied to an AUV underwater support platform, and a five-dimensional digital twin model of the AUV underwater support platform is studied, including five dimensions: physical world, digital world, twin data center, service application, and data connection. The digital twin of the subsea support platform is established by using the digital twin modeling technology. The POD method is used to calculate the deformation field matrix of the support structure of the subsea support platform under the 0–5 sea state, and the corresponding eigenvalues and eigenvectors are obtained. By intercepting the eigenvectors corresponding to the eigenvalues of the high energy proportion, the low-order equation is constructed, and the reduced-order model under each sea state can be quickly solved. The experimental results show that the model reduction technology can greatly shorten the model solving time, and the calculated results are highly consistent with the simulation results of the finite element full-order model, which can realize the rapid analysis of the deformation response of the subsea support platform structure, and provide a theoretical basis and technical support for the subsequent simulation, state evaluation, visual monitoring, and predictive maintenance.