对比多种数据增强方法，提升合成孔径声呐目标识别准确率
A Comparison of Data Augmentation Methods for Training Deep Neural Networks on Synthetic Aperture Sonar
- 系统比较多种数据增强策略在声呐图像上的效果
- 增强可提升识别准确率，但效果因方法而异
- 适合研究声呐目标识别与深度学习融合的学者
本文研究基于深度神经网络（DNN）的合成孔径声呐（SAS）自动目标识别（ATR）问题。训练DNN面临的主要挑战是真实声呐数据标注样本少，因其采集成本高、耗时长。数据增强是一种有效缓解数据不足的通用策略，通过引入真实变化生成额外合成训练数据。已有研究探索了多种用于SAS-ATR的数据增强方法，包括常规图像增强（如对比度调整、裁剪）以及基于声呐物理特性的增强。本文在前人基础上，系统比较了多种现有增强策略在训练SAS-ATR DNN中的表现，并探讨其与现代DNN架构（如Transformer）结合的效果。结果表明，数据增强能提升目标识别准确率，但增益程度各异，且并非所有增强方法均有效。
原文摘要 · Abstract (English)
In this work we study Automatic Target Recognition (ATR) for Synthetic Aperture Sonar (SAS) data with a focus on deep neural networks (DNNs). The main challenge in training DNNs for SAS-ATR arises from the limited quantity of labeled target examples due to the significant costs and time required to collect real-world SAS data. One successful general strategy for mitigating the problem of limited training data is augmentation, which generates additional synthetic training data by introducing realistic variations to available data. Prior research has investigated a variety of augmentation strategies for SAS-ATR, including conventional image augmentations (e.g., contrast changes, cropping) as well as augmentations motivated the specific physics of SAS data. Building on prior work, we systematically compare many of these existing augmentation strategies for training DNNs for SAS-ATR. We also investigate the impact of augmentation when combined with modern DNN architectures such as transformers. The results indicate that augmentation can improve target recognition accuracy, although benefits vary, and not all augmentations are beneficial.
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