  This paper investigates the problem of dim frequency line detection and
recovery in the so-called lofargram. Theoretically, time integration long
enough 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 representation that amplify the
important aspects of input while suppresses irrelevant variations can be
learned. Hence we propose a new DeepLofargram, composed of 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, the performance boundary is -24dB on average, and -26dB for some.
This is far beyond the perception of human visual and significantly improves
the state-of-the-art.
