Towards Domain Generalization In Underwater Object Detection
Hong Liu, Pinhao Song, Runwei Ding
Abstract
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 using 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, Domain Generalization YOLO (DG-YOLO) is proposed, which consists of three parts: YOLOv3, Domain Invariant Module and Invariant Risk Minimization penalty. Finally, experiments on original and synthetic URPC2019 dataset prove that WQT combined with DG-YOLO achieves promising performance of domain generalization in underwater object detection. The source code can be found at https://github.com/mousecpn/DG-YOLO.