Neural Edge Histogram Descriptors for Underwater Acoustic Target Recognition
This provides a lightweight and efficient solution for underwater target recognition, addressing computational and domain-transfer issues in maritime applications.
The paper tackled the problem of recognizing acoustic targets in passive sonar by adapting neural edge histogram descriptors from image classification, achieving competitive performance with large pre-trained models while reducing computational costs.
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.