WQT and DG-YOLO: towards domain generalization in underwater object detection
This addresses domain generalization for underwater object detection, which is incremental as it builds on existing methods like YOLOv3 with new components.
The paper tackled domain shift in underwater object detection with limited datasets by proposing WQT for data augmentation and DG-YOLO for semantic mining, achieving promising domain generalization performance on URPC2019 datasets.
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.