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

arXiv: https://arxiv.org/abs/2004.06333 · HTML: https://mlsanity.com/paper/2004.06333/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Unsupervised Domain Adaptation of Object Detectors: A Survey](https://mlsanity.com/paper/2105.13502/) (2021)
- [Domain Generalisation for Object Detection under Covariate and Concept Shift](https://mlsanity.com/paper/2203.05294/) (2022)
- [Persistent Homology Meets Object Unity: Object Recognition in Clutter](https://mlsanity.com/paper/2305.03815/) (2023)
- [UnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detection](https://mlsanity.com/paper/2309.04708/) (2023)
- [Underwater Object Classification and Detection: first results and open challenges](https://mlsanity.com/paper/2201.00977/) (2022)
- [A high-precision underwater object detection based on joint self-supervised deblurring and improved spatial transformer network](https://mlsanity.com/paper/2203.04822/) (2022)
- [Domain Similarity-Perceived Label Assignment for Domain Generalized Underwater Object Detection](https://mlsanity.com/paper/2401.05401/) (2023)
- [Underwater Object Detection Enhancement via Channel Stabilization](https://mlsanity.com/paper/2408.01293/) (2024)

## Abstract (arXiv)

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.