Enhanced underwater acoustic target recognition using parallel dual-branch network with attention mechanism

Abstract

Ship-radiated noise serves as a crucial source of underwater acoustic signals for vessel classification, but its identification is often hindered by environmental variability and internal noise interference. To address these challenges, we propose a dual-branch Residual Attention-Long Short-Term Memory (ResA-LSTM) network for underwater acoustic target recognition. The proposed model integrates a Residual Attention (ResA) branch to extract spatial features and a Bidirectional Long Short-Term Memory (Bi-LSTM) branch to capture long-term temporal dependencies from Mel spectrograms. The ResA module incorporates attention mechanisms and residual connections to enhance feature selection and improve robustness in noisy environments. Evaluations conducted on two public datasets, ShipsEar and DeepShip, demonstrate the effectiveness of our approach, achieving classification accuracies of 98.55 % and 99.31 %, respectively. Sensitivity analysis further confirms the model's ability to handle long-duration acoustic sequences, highlighting its potential for practical deployment in real-world underwater recognition tasks.