Due to the importance of underwater exploration in the development and utilization of deep-sea resources, underwater autonomous operation is more and more important to avoid the dangerous high-pressure deep-sea environment. For underwater autonomous operation, the intelligent computer vision is the most important technology. In an underwater environment, weak illumination and low-quality image enhancement, as a preprocessing procedure, is necessary for underwater vision. In this paper, a combination of max-RGB method and shades of gray method is applied to achieve the enhancement of underwater vision, and then a CNN (Convolutional Neutral Network) method for solving the weakly illuminated problem for underwater images is proposed to train the mapping relationship to obtain the illumination map. After the image processing, a deep CNN method is proposed to perform the underwater detection and classification, according to the characteristics of underwater vision, two improved schemes are applied to modify the deep CNN structure. In the first scheme, a 1∗1 convolution kernel is used on the 26∗26 feature map, and then a downsampling layer is added to resize the output to equal 13∗13. In the second scheme, a downsampling layer is added firstly, and then the convolution layer is inserted in the network, the result is combined with the last output to achieve the detection. Through comparison with the Fast RCNN, Faster RCNN, and the original YOLO V3, scheme 2 is verified to be better in detecting underwater objects. The detection speed is about 50 FPS (Frames per Second), and mAP (mean Average Precision) is about 90%. The program is applied in an underwater robot; the real-time detection results show that the detection and classification are accurate and fast enough to assist the robot to achieve underwater working operation.

Seabed fishing depends on humans in common, for instance, the sea cucumber, sea urchin, and scallop fishing, which is always a very dangerous task. Considering the underwater complex environment conditions such as low temperature, dim vision, and high pressure, collecting the marine products using underwater robots is commonly regarded as a feasible solution. The key technique of the underwater robot development is to detect and locate the main target from underwater vision. This research is based on the deep convolutional neural network (CNN) to realize the target recognition from underwater vision. The RPN (Region Proposal Network) is used to optimize the feature extraction capability. Deep learning dataset is prepared using an underwater video obtained from a sea cucumber fishing ROV (Remote Operated Vehicle). The inspiration of the network structure and the improvements come from the Faster RCNN and Hypernet method, and for the underwater dataset, the method proposed in this paper shows a good performance of recall and object detection accuracy. The detection runs with a speed of 17 fps on a GPU, which is applicable to be used for real-time processing.

The marginal ice zone (MIZ) is located at the junction of ice-covered areas and open water, where waves cause ice floes to break up and change their state of motion, thus threatening the safety of ships navigating the ice. This study employs the Structured Arbitrary Lagrangian–Eulerian (S-ALE) method and the numerical wave-making method based on dynamic boundary conditions to numerically examine the motion response of ice floes in waves. The longitudinal motion of ice floes in waves can be classified into two distinct states, namely irregular and regular, depending on the wavelength. In the short-wave range, the ice floes exhibit primarily irregular motion, whereas in the long-wave range, their motion becomes regular, resembling that of isolated ice floes. The longitudinal motion response of the ice floes remains unaffected by their size. However, the longitudinal velocity, surge velocity, and displacement of the ice floes are influenced by the wavelength. Furthermore, the numerical calculations are compared to the model test conducted in a towing tank using paraffin artificial ices, revealing a qualitative agreement between the experimental and numerical results.

The objective of this paper was to establish a comprehensive methodology for the optimized design of propellers for ice-class vessels, aiming to enhance hydrodynamic efficiency while ensuring structural integrity. This paper begins by introducing a novel approach for calculating blade stress, which takes into account both extreme ice loads and hydrodynamic loads, to be utilized in the propeller strength design process. Subsequently, a backpropagation (BP) neural network model was developed based on the data obtained from B-series propeller charts and integrated with a genetic algorithm to achieve a preliminary optimized design of the propeller’s hydrodynamic performance. To illustrate the application of this methodology, a case study of an ice-breaking tug propeller design is presented, detailing the optimization design process, including the preliminary, intermediate, and final design stages. The study also addresses key aspects such as geometric parameterization, the selection of optimization variables, the implementation of optimization algorithms, and the balance of multi-objective trade-offs. The proposed design approach can serve as a valuable reference for the practical engineering design of propellers for ice-class vessels, providing a systematic framework for achieving optimal performance in challenging operating conditions.