Addressing unfamiliar ship type recognition in real-scenario vessel monitoring: a multi-angle metric networks framework

Intelligent ship monitoring technology, driven by its exceptional data fitting ability, has emerged as a crucial component within the field of intelligent maritime perception. However, existing deep learning-based ship monitoring studies primarily focus on minimizing the discrepancy between predicted and true labels during model training. This approach, unfortunately, restricts the model to learning only from labeled ship samples within the training set, limiting its capacity to recognize new and unseen ship categories. To address this challenge and enhance the model’s generalization ability and adaptability, a novel framework is presented, termed MultiAngle Metric Networks. The proposed framework incorporates ResNet as its foundation. By employing a novel multi-scale loss function and a new similarity measure, the framework effectively learns ship patterns by minimizing sample distances within the same category and maximizing distances between samples of different categories. The experimental results indicate that the proposed framework achieves the highest level of ship monitoring accuracy when evaluated on three distinct ship monitoring datasets. Even in the case of unfamiliar ships, where the detection performance of conventional models significantly deteriorates, the framework maintains stable and efficient detection capabilities. These experimental results highlight the framework’s ability to effectively generalize its understanding beyond the training samples and adapt to real-world scenarios.