  A General Underwater Object Detector (GUOD) should perform well on most of
underwater circumstances. However, with limited underwater dataset,
conventional object detection methods suffer from domain shift severely. This
paper aims to build a GUOD with small underwater dataset with limited types of
water quality. First, we propose a data augmentation method Water Quality
Transfer (WQT) to increase domain diversity of the original small dataset.
Second, for mining the semantic information from data generated by WQT, DG-YOLO
is proposed, which consists of three parts: YOLOv3, DIM and IRM penalty.
Finally, experiments on original and synthetic URPC2019 dataset prove that
WQT+DG-YOLO achieves promising performance of domain generalization in
underwater object detection.
