Scholarlink

<p>Underwater Acoustic Target Recognition (UATR) plays a significant role in intelligent sonar and underwater environmental monitoring systems, but successful recognition in underwater environments of complex scenarios is a significant challenge because of the effects of ambient noise, signal attenuation, and dynamic propagation. The recent developments in deep learning have enhanced automatic generation of features in acoustic signal classification, and the vast majority of existing models represent a trade-off between recognition accuracy and computational efficiency. In this regard we introduce Lightweight Hybrid Attention Network with Multi Scale Feature Integration (DCAT) a new deep learning system that combines depthwise separable convolutions to extract local features efficiently in parallel in conjunction with transformer-based global temporal dependency modeling attention modules. The key innovation of DCAT is its adaptive fusion mechanism, which conditionally combines contextual information of the two transformer branches with different receptive fields with varying scales with the purpose of allowing the model to ideally capture small-scale local features and long-range acoustic structures. The framework employs robust preprocessing and feature engineering, including Zero Crossing Rate (ZCR), Root Mean Square Energy (RMSE), Mel-Frequency Cepstral Coefficients (MFCC), and Chroma features, computed from 22.05&#xa0;kHz sampled audio, combined with data augmentation techniques—pitch shifting, time stretching, and Gaussian noise addition—to enhance generalization to real-world acoustic variability. Evaluated on two benchmark datasets, DeepShip and ShipsEar, DCAT achieves superior classification accuracies of 98.84% and 99.16%, respectively, while maintaining extremely low computational complexity of only 0.52&#xa0;million parameters and 6.1&#xa0;million FLOPs, supporting real-time inference with latency below 0.7 ms per sample. Comparative studies show that DCAT is more accurate and more efficient compared to state-of-the-art networks, including Transformer, ResNet1D, and AResNet, which substantiate its ability to trade-off discriminative power with resource economy. The suggested model sets a new standard of performance efficiency of underwater acoustic target recognition, which offers a promising framework to the next-generation autonomous sonar, underwater surveillance, and marine ecological monitoring systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A lightweight hybrid attention network with multi-scale feature integration for intelligent recognition of underwater acoustic targets

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月27日

查看全文
  • Nahid-Al Mahmud,
  • Tao Zhang,
  • Yasir Iqbal,
  • Farhana Bari Sumona,
  • Anjum Iqbal,
  • Yanzhang Geng,
  • Wajid Khan,
  • Basem Abu Zneid,
  • Ievgen Zaitsev

摘要

Underwater Acoustic Target Recognition (UATR) plays a significant role in intelligent sonar and underwater environmental monitoring systems, but successful recognition in underwater environments of complex scenarios is a significant challenge because of the effects of ambient noise, signal attenuation, and dynamic propagation. The recent developments in deep learning have enhanced automatic generation of features in acoustic signal classification, and the vast majority of existing models represent a trade-off between recognition accuracy and computational efficiency. In this regard we introduce Lightweight Hybrid Attention Network with Multi Scale Feature Integration (DCAT) a new deep learning system that combines depthwise separable convolutions to extract local features efficiently in parallel in conjunction with transformer-based global temporal dependency modeling attention modules. The key innovation of DCAT is its adaptive fusion mechanism, which conditionally combines contextual information of the two transformer branches with different receptive fields with varying scales with the purpose of allowing the model to ideally capture small-scale local features and long-range acoustic structures. The framework employs robust preprocessing and feature engineering, including Zero Crossing Rate (ZCR), Root Mean Square Energy (RMSE), Mel-Frequency Cepstral Coefficients (MFCC), and Chroma features, computed from 22.05 kHz sampled audio, combined with data augmentation techniques—pitch shifting, time stretching, and Gaussian noise addition—to enhance generalization to real-world acoustic variability. Evaluated on two benchmark datasets, DeepShip and ShipsEar, DCAT achieves superior classification accuracies of 98.84% and 99.16%, respectively, while maintaining extremely low computational complexity of only 0.52 million parameters and 6.1 million FLOPs, supporting real-time inference with latency below 0.7 ms per sample. Comparative studies show that DCAT is more accurate and more efficient compared to state-of-the-art networks, including Transformer, ResNet1D, and AResNet, which substantiate its ability to trade-off discriminative power with resource economy. The suggested model sets a new standard of performance efficiency of underwater acoustic target recognition, which offers a promising framework to the next-generation autonomous sonar, underwater surveillance, and marine ecological monitoring systems.