用轻量神经边缘直方图提升水下声学目标识别效率
Neural Edge Histogram Descriptors for Underwater Acoustic Target Recognition
- 将图像领域的神经边缘直方图方法移植到声学信号分类
- 融合统计与结构纹理特征，性能媲美大模型
- 计算成本大幅降低，适合资源受限场景
众多海上应用依赖被动声纳识别声学目标。尽管预训练模型在分类任务中日益普及，但其往往需要大量计算资源，且在新数据域上因数据分布差异导致性能下降。为解决此问题，本文将原本用于图像分类的神经边缘直方图描述符（NEHD）方法适配至被动声纳信号分类。通过全面评估统计与结构纹理特征，证明二者结合可达到与大型预训练模型相当的性能。所提出的基于NEHD的方法在显著降低计算成本的同时保持高精度，为水下目标识别提供了一种轻量高效的解决方案。
原文摘要 · Abstract (English)
Numerous maritime applications rely on the ability to recognize acoustic targets using passive sonar. While there is a growing reliance on pre-trained models for classification tasks, these models often require extensive computational resources and may not perform optimally when transferred to new domains due to dataset variations. To address these challenges, this work adapts the neural edge histogram descriptors (NEHD) method originally developed for image classification, to classify passive sonar signals. We conduct a comprehensive evaluation of statistical and structural texture features, demonstrating that their combination achieves competitive performance with large pre-trained models. The proposed NEHD-based approach offers a lightweight and efficient solution for underwater target recognition, significantly reducing computational costs while maintaining accuracy.
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