  The performance of existing underwater object detection methods degrades
seriously when facing domain shift problem caused by complicated underwater
environments. Due to the limitation of the number of domains in the dataset,
deep detectors easily just memorize a few seen domain, which leads to low
generalization ability. Ulteriorly, it can be inferred that the detector
trained on as many domains as possible is domain-invariant. Based on this
viewpoint, we propose a domain generalization method from the aspect of data
augmentation. First, the style transfer model transforms images from one source
domain to another, enriching the domain diversity of the training data. Second,
interpolating different domains on feature level, new domains can be sampled on
the domain manifold. With our method, detectors will be robust to domain shift.
Comprehensive experiments on S-UODAC2020 datasets demonstrate that the proposed
method is able to learn domain-invariant representations, and outperforms other
domain generalization methods. The source code is available at
https://github.com/mousecpn.
