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Among underwater perceptual sensors, imaging sonar has been highlighted forits perceptual robustness underwater. The major challenge of imaging sonar,however, arises from the difficulty in defining visual features despite limitedresolution and high noise levels. Recent developments in deep learning providea powerful solution for computer-vision researches using optical images.Unfortunately, deep learning-based approaches are not well established forimaging sonars, mainly due to the scant data in the training phase. Unlike theabundant publically available terrestrial images, obtaining underwater imagesis often costly, and securing enough underwater images for training is notstraightforward. To tackle this issue, this paper presents a solution to thisfield's lack of data by introducing a novel end-to-end image-synthesizingmethod in the training image preparation phase. The proposed method presentimage synthesizing scheme to the images captured by an underwater simulator.Our synthetic images are based on the sonar imaging models and noisycharacteristics to represent the real data obtained from the sea. We validatethe proposed scheme by training using a simulator and by testing the simulatedimages with real underwater sonar images obtained from a water tank and thesea.

Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection