摘要：Compared to the flourishing researches on terrestrial optical images, deep learning in underwater imaging has not been highlighted. Although some approaches applied deep learning in their underwater imaging still no major application has been found in underwater sonar imaging. Notably, the fundamental limitation in underwater image data would be the main cause of the bottleneck. To alleviate this issue, this paper introduces a simulation-generated dataset for object detection in underwater sonar images. Specifically, this paper focuses on generating real sonarlike style-transferred synthetic sonar images for network training.
