Work

MASS intelligent collision avoidance decision-making based on ship pose estimation and COLREGs quantification

Year: 2025

Type: article

Abstract: Collision avoidance decision-making is crucial for the autonomous navigation of Maritime Autonomous Surface Ships (MASS). Visual pose estimation and navigation light signals provide essential information for evaluating encounter situations under the “in sight” condition defined by COLREGs. This study proposes an intelligent collision avoidance method based on ship pose estimation (SPE) and COLREGs quantification. First, the annotation specifications for the SPE dataset are formulated, and the dataset is constructed accordingly. Then, by optimizing the network structure and loss function of YOLO11m-pose, we develop the GEW-YOLO architecture and train it on the SPE dataset. Subsequently, pose estimation results are used to classify encounter situations and guide decision-making based on encounter characteristics and historical data. In addition, ablation experiments were conducted to validate the proposed GEW-YOLO model, showing a 20.2 % reduction in parameters, a 13.2 % decrease in GFLOPs, and improvements of 5.2 % and 4.7 % in mAP50-95 for detection and pose estimation compared to the baseline. Finally, collision avoidance experiments conducted on self-developed MASS confirm that the proposed method can make accurate collision avoidance decisions across various encounter situations.

Source: Ocean Engineering

DOI: 10.1016/j.oceaneng.2025.121679