Frequent bad weather at sea severely damages the quality of visual images captured by imaging equipment. Ship instance segmentation in adverse weather conditions remains a major challenge because of poor visibility at sea. Existing approaches for instance segmentation are primarily designed for clear days and rarely consider the aforementioned severe weather. Blurred ship objects can easily cause missed ship detection and decrease the instance segmentation performance on ship images, especially in the case of frequent fog at sea. To this end, we propose a ship instance segmentation framework (IRDCLNet) based on Interference Reduction and Dynamic Contour Learning in foggy scenes. The Interference Reduction Module is proposed to reduce the interference caused by fog and solves the problem of missed ship detection. Meanwhile, we present Dynamic Contour Learning to predict the overall contour of the blurred ships to assist in mask prediction. To handle the scarcity of ocean data in foggy weather, we build the Foggy ShipInsseg dataset, which contains 5,739 real and simulated foggy ship images with 10,900 fine instance mask annotations. Experiments on the Foggy ShipInsseg dataset show that our IRDCLNet outperforms the Mask R-CNN and CondInst baselines and achieves the state-of-the-art performance.

In maritime scenes, instance segmentation of small object ships is of vital importance. Small ship objects in images have the characteristics of smaller size, lower image cover rate and fewer appearance features. However, existing instance segmentation methods fail to recognize and segment them and can cause missed ship segmentation. To this end, we propose a dual-branch activation network (DANet) for small object instance segmentation of ship images. DANet consists of a Feature Encoding, a Dual Mask Branch, and a Dual Activation Branch. The Feature Encoding adopts feature refinement and a pyramid structure to obtain more fine-grained features. The proposed Dual Mask Branch extracts dual-path mask features for encoding small object information. We propose a Dual Activation Branch to activate more small object regions and generate instance features. Furthermore, we build the Small ShipInsSeg dataset from a total of 5,256 images and 11,612 instances. The experiments show that DANet outperforms the SparseNet baseline and achieves state-of-the-art performance. Additionally, our method achieves a good trade-off between accuracy and speed.

Accurate ship object detection ensures navigation safety and effective maritime traffic management. Existing ship target detection models often have the problem of missed detection in complex marine environments, and it is hard to achieve high accuracy and real-time performance simultaneously. To address these issues, this paper proposes a lightweight ship object detection model called YOLOv7-Ship to perform end-to-end ship detection in complex marine environments. At first, we insert the improved “coordinate attention mechanism” (CA-M) in the backbone of the YOLOv7-Tiny model at the appropriate location. Then, the feature extraction capability of the convolution module is enhanced by embedding omnidimensional dynamic convolution (ODconv) into the efficient layer aggregation network (ELAN). Furthermore, content-aware feature reorganization (CARAFE) and SIoU are introduced into the model to improve its convergence speed and detection precision for small targets. Finally, to handle the scarcity of ship data in complex marine environments, we build the ship dataset, which contains 5100 real ship images. Experimental results show that, compared with the baseline YOLOv7-Tiny model, YOLOv7-Ship improves the mean average precision (mAP) by 2.2% on the self-built dataset. The model also has a lightweight feature with a detection speed of 75 frames per second, which can meet the need for real-time detection in complex marine environments to a certain extent, highlighting its advantages for the safety of maritime navigation.

This study investigates the trajectory-tracking problem of fully actuated unmanned surface vessels (USVs). A three-degree-of-freedom (3-DOF) dynamic model was used with three control variables: surge force, sway force, and yaw moment, where the model uncertainties, environmental disturbances, and actuator saturation were also considered. An improved nonlinear model predictive control (NMPC) method was investigated to improve the control performance in the trajectory tracking of USVs, and a nonlinear optimization problem based on the finite horizon was developed to minimize the divergence between the actual and the reference states. Moreover, a nonlinear disturbance observer (NDOB) was employed to estimate the environmental disturbance, which could enhance the robustness of the controller. Furthermore, an event-triggered mechanism was also employed to reduce the computational frequency of NMPC. Simulations of slide mode control, NMPC, and improved NMPC were performed in the trajectory-tracking control. Besides, the simulation results verified the effectiveness and robustness of the proposed improved NMPC-NDOB scheme.

This paper tackles the highly challenging problem of automatic berthing for autonomous surface vessels (ASVs), encompassing trajectory planning, trajectory tracking, and collision avoidance. Firstly, a novel A* algorithm integrated with a quasi-uniform B-spline and quadratic interpolation method (A*QB) is proposed for generating a smooth trajectory from the initial position to the berth, utilizing an offline-generated scaled map. Secondly, the optimal nonlinear model predictive control (NMPC)-based trajectory-tracking framework is established, incorporating the model’s uncertainty, the input saturation, and environmental disturbances, based on a 3-DOF model of a ship. Finally, considering the collision risks during port berthing, a COLREGs-based collision avoidance method is investigated. Consequently, a novel trajectory-tracking and COLREGs-based collision avoidance (TTCCA) scheme is proposed, ensuring that the ASV navigates along the desired trajectory, safely avoids both static and dynamic obstacles, and successfully reaches the berth. To validate the TTCCA approach, numerical simulations are conducted across four scenarios with comparisons to existing methods. The experimental results demonstrate the effectiveness and superiority of the proposed scheme.