Due to the expensive nature of field data gathering, the lack of training data often limits the performance of Automatic Target Recognition (ATR) systems. This problem is often addressed with domain adaptation techniques, however the currently existing methods fail to satisfy the constraints of resource and time-limited underwater systems. We propose to address this issue via an online fine-tuning of the ATR algorithm using a novel data-selection method. Our proposed data-mining approach relies on visual similarity and outperforms the traditionally employed hard-mining methods. We present a comparative performance analysis in a wide range of simulated environments and highlight the benefits of using our method for the rapid adaptation to previously unseen environments.

中文翻译：

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基于相似性的声纳 ATR 系统在线域自适应数据挖掘

由于现场数据收集的昂贵性质，缺乏训练数据通常会限制自动目标识别 (ATR) 系统的性能。这个问题通常通过域适应技术来解决，但是目前现有的方法无法满足资源和时间有限的水下系统的约束。我们建议通过使用一种新颖的数据选择方法对 ATR 算法进行在线微调来解决这个问题。我们提出的数据挖掘方法依赖于视觉相似性，并且优于传统采用的硬挖掘方法。我们在广泛的模拟环境中进行了比较性能分析，并强调了使用我们的方法快速适应以前看不见的环境的好处。