Towards Deployable Underwater Vessel Classification

2026-09-24

AI Summary

This study addresses the challenge of balancing accuracy and model compactness in underwater acoustic classification, alongside concerns regarding generalization due to insufficiently rigorous evaluation protocols. To this end, we propose a compact and efficient classification framework that integrates auditory-inspired time-frequency and cochlear multi-representation feature engineering, temporal statistical pooling, and a lightweight convolutional architecture. Furthermore, a strict recording-level data partitioning protocol is introduced to ensure reliable evaluation. Experimental results demonstrate that the proposed framework achieves an F1-score of 0.9918 on the ShipsEar dataset. On the DeepShip dataset, a small model with only 157K parameters attains an F1-score of 0.7226, outperforming larger counterparts and thereby validating its effectiveness for efficient deployment under stringent evaluation conditions.

Abstract

We propose a compact underwater acoustic classification framework combining multi-representation feature engineering, temporal statistical pooling, and compact convolutional architectures designed for acoustic time-frequency and cochlear representations. We investigate multiple conventional and auditory-inspired representations and first evaluate lightweight classifiers and Conventional Neural Networks (CNNs) on ShipsEar dataset. On the provided split, a two-layer CNN achieves a macro F1 of 0.9918, while a Radial Basis Function Support Vector Machine (RBF-SVM) reaches 0.9883. However, source-recording provenance cannot be reconstructed, preventing verification of recording-independent generalisation. We therefore evaluate on DeepShip dataset using recording-level partitioning before segmentation. Under this protocol, a 157K-parameter compact CNN achieves a test macro F1 of 0.7226, while an 11.17M-parameter ResNet18 provides no improvement in validation performance under the matched setting. These results demonstrate the importance of representation-aware feature and model design, together with rigorous recording-level evaluation, for classification performance and deployability in compact underwater acoustic systems.

Problem

• Underwater Vessel Classification
• Acoustic Classification
• Deployability
• Generalisation
• Compact Models

Innovation

• Underwater Acoustic Classification
• Compact CNN
• Multi-representation Feature Engineering
• Temporal Statistical Pooling
• Recording-level Evaluation