This paper investigates the problem of dim frequency line detection and recovery in the so-called lofargram. Theoretically, long enough time integration can always enhance the detection characteristic. But this does not hold for irregularly fluctuating lines. Deep learning has been shown to perform very well for sophisticated visual inference tasks. With the composition of multiple processing layers, very complex high level representations that amplify the important aspects of input while suppressing irrelevant variations can be learned. Hence, DeepLofargram is proposed, composed of a deep convolutional neural network and its visualization counterpart. Plugging into specifically designed multi-task loss, an end-to-end training jointly learns to detect and recover the spatial location of potential lines. Leveraging on this deep architecture, performance limits of low SNR can be achieved as low as −24 dB on average and −26 dB for some. This is far beyond the perception of human vision and significantly improves the state-of-the-art.

中文翻译：

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DeepLo​​fargram：基于深度学习的波动暗淡频率线检测和恢复

本文研究了所谓的Lofargram中暗线检测和恢复的问题。从理论上讲，足够长的时间积分可以始终提高检测性能。但这不适用于不规则波动的线。深度学习已被证明在复杂的视觉推理任务中表现出色。通过多个处理层的组合，可以了解非常复杂的高级表示形式，可以放大输入的重要方面，同时抑制不相关的变化。因此，提出了由深度卷积神经网络及其可视化对应物组成的DeepLo​​fargram。插入专门设计的多任务丢失功能后，端到端培训共同学习如何检测和恢复潜在线的空间位置。利用这种深层架构，低SNR的性能极限可以平均低至-24 dB，有些则可以达到-26 dB。这远远超出了人类视觉的感知范围，并显着改善了最新技术水平。