Achieving Domain Generalization in Underwater Object Detection by Image Stylization and Domain Mixup

• URL: http://arxiv.org/abs/2104.02230v1
• Date: Tue, 6 Apr 2021 01:45:07 GMT
• Title: Achieving Domain Generalization in Underwater Object Detection by Image Stylization and Domain Mixup
• Authors: Pinhao Song, Linhui Dai, Peipei Yuan, Hong Liu and Runwei Ding
• Abstract summary: Existing underwater object detection methods degrade seriously when facing domain shift problem caused by complicated underwater environments. We propose a domain generalization method from the aspect of data augmentation. Comprehensive experiments on S-UODAC 2020 datasets demonstrate that the proposed method is able to learn domain-invariant representations.
• Score: 8.983901488753967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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