Toward Low-Resource Hydroacoustic Signature-Based Ship Detection With Out-of-Domain Detection Capability

Hydroacoustic signature-based ship detection (HSD) involves identifying and classifying vessels by analyzing the underwater sound patterns they emit, including propeller noise, engine vibrations, and hull flow disturbances. This technique is useful in naval surveillance, anti-submarine warfare, maritime traffic monitoring, and underwater environmental studies where visual or radar detection is limited. Since HSD is independent of visibility conditions and covert due to its passiveness, it can be used as a complementary modality along with traditional ship detection methods. However, machine-learning based automatic HSD is sensitive to factors like variability in ocean acoustic environments, background noise, similarity of signatures among different classes of ships, etc., especially in low-resource conditions where there is lack of sufficient training data to train a generalized HSD model. Furthermore, out-of-domain (OOD) detection is necessary for reliable surveillance in real-world applications. Motivated by all these, in this paper, we present different methods to improve the performance of low-resource HSD with OOD detection capability. Specifically, we first propose to use pre-trained PaSST and EAT models for efficient feature extraction for low-resource HSD. Since different encoder layers of these pre-trained models encode the input differently, a detailed layer-wise analysis of the performance is conducted on both models to select the optimum layer for HSD-specific feature extraction. Following this, we incorporate OOD detection capability to the HSD model using GMM-based, K Nearest Neighbor (KNN) based and OpenMax-based thresholding approaches. The results obtained on a publicly available dataset indicate that the HSD model trained using PaSST features, when coupled with the GMM-based OOD detector achieves the best performance, with an AUROC of around 93% and an accuracy score of nearly 86%.