针对声呐数据的深度学习需突破图像模型套路，亟待高质量数据集支持。
Deep learning for echo sounder data
- 提出声呐数据特性决定不能直接套用图像模型
- 当前研究受限于缺乏标准格式与高质量数据集
- 适合从事水下声学、海洋监测的算法研究者阅读
过去十年，机器学习技术彻底改变了图像和文本数据的处理与解读方式。在水下观测中，声学数据是主要信息来源，深度学习方法已被应用于回声图等声学数据，但迄今效果有限。本文认为，由于声学数据的内在特性，真正的进展可能需要开发超越图像处理范式的深度学习方法。目前，方法创新的潜力受到两大瓶颈制约：缺乏统一的数据格式与组织规范，更严重的是缺少公开可用、高质量的数据集及明确的性能评估目标。为推动该领域发展，亟需解决上述问题。
原文摘要 · Abstract (English)
There is no doubt that over the last decade, techniques from the field of machine learning have revolutionized how we process and interpret data, especially images and text. For underwater observations acoustics is a primary source of information, and naturally, deep learning methods have been applied to echograms and other acoustics data, but so far with rather modest results. Here, we argue that due to intrinsic properties of acoustic data, substantial advances will likely require research into deep learning methods beyond mere recycling of models and techniques from image processing. Currently, the potential for breakthroughs in method development is hindered by the lack of standard data formats and organization, and even more by the lack of readily available, high quality data sets with established performance goals. To advance the field, these shortcomings should be remedied
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