Title: Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection,
Abstract: Among underwater perceptual sensors, imaging sonar has been highlighted for
its perceptual robustness underwater. The major challenge of imaging sonar,
however, arises from the difficulty in defining visual features despite limited
resolution and high noise levels. Recent developments in deep learning provide
a powerful solution for computer-vision researches using optical images.
Unfortunately, deep learning-based approaches are not well established for
imaging sonars, mainly due to the scant data in the training phase. Unlike the
abundant publically available terrestrial images, obtaining underwater images
is often costly, and securing enough underwater images for training is not
straightforward. To tackle this issue, this paper presents a solution to this
field's lack of data by introducing a novel end-to-end image-synthesizing
method in the training image preparation phase. The proposed method present
image synthesizing scheme to the images captured by an underwater simulator.
Our synthetic images are based on the sonar imaging models and noisy
characteristics to represent the real data obtained from the sea. We validate
the proposed scheme by training using a simulator and by testing the simulated
images with real underwater sonar images obtained from a water tank and the
sea.