Neural Edge Histogram Descriptors for Underwater Acoustic Target Recognition

Atharva Agashe, Davelle Carreiro, Alexandra Van Dine, Joshua Peeples
3/17/2025
cs.LGcs.SDeess.AS

No official code found. Best implementation: Advanced-Vision-and-Learning-Lab/NEHD_UATR (6 stars, MIT, last commit 2025-04-07).

Abstract

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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Code Implementations(1)

Advanced-Vision-and-Learning-Lab/NEHD_UATRCommunity100%
MIT

Neural Edge Histogram Descriptors for Underwater Acoustic Target Recognition

62PythonJan 13, 20251 years agoMIT

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Cite this paper

@article{agashe2025neural,
  title  = {Neural Edge Histogram Descriptors for Underwater Acoustic Target Recognition},
  author = {Atharva Agashe and Davelle Carreiro and Alexandra Van Dine and Joshua Peeples},
  year   = {2025},
  eprint = {2503.13763},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.13763v1}
}

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