# WQT and DG-YOLO: towards domain generalization in underwater object detection

**Authors:** Hong Liu, Pinhao Song, Runwei Ding  
**arXiv:** [2004.06333](https://arxiv.org/abs/2004.06333)  
**Date:** 2020-04-14  
**Summary page:** https://www.summarizepaper.com/en/paper/2004.06333v1/

## Key points

- The paper addresses the issue of domain shift in underwater object detection methods
- The authors propose a General Underwater Object Detector (GUOD) that can perform effectively in various underwater conditions
- They introduce a data augmentation method called Water Quality Transfer (WQT) to overcome limitations of small datasets and increase training data diversity
- WQT transfers water quality characteristics from one image to another to enhance domain diversity
- The authors propose a novel approach called DG-YOLO, which consists of YOLOv3, DIM, and IRM penalty components, to extract semantic information from augmented datasets generated by WQT
- Experiments on original and synthetic URPC2019 datasets show promising performance of WQT+DG-YOLO in domain generalization for underwater object detection
- The proposed GUOD combines WQT with DG-YOLO for improved performance across different water quality conditions.

## Summary

The paper titled "WQT and DG-YOLO: towards domain generalization in underwater object detection" addresses the issue of domain shift in conventional object detection methods when applied to underwater circumstances. The authors propose a General Underwater Object Detector (GUOD) that can effectively perform in various underwater conditions, even with limited underwater datasets. To overcome the limitations of the small dataset and increase the diversity of the training data, the authors introduce a data augmentation method called Water Quality Transfer (WQT). This technique aims to enhance the domain diversity of the original dataset by transferring water quality characteristics from one image to another. In order to extract semantic information from the augmented dataset generated by WQT, the authors propose a novel approach called DG-YOLO. DG-YOLO consists of three main components: YOLOv3, DIM (Deep Implicit Models), and IRM penalty. These components work together to mine semantic information and improve object detection performance in underwater environments. The effectiveness of WQT+DG-YOLO is evaluated through experiments conducted on both original and synthetic URPC2019 datasets. The results demonstrate that this approach achieves promising performance in terms of domain generalization for underwater object detection. Overall, this paper presents a comprehensive solution for addressing domain shift issues in underwater object detection. By introducing WQT for data augmentation and combining it with DG-YOLO, the proposed GUOD shows improved performance across different types of water quality conditions.