# Reveal of Domain Effect: How Visual Restoration Contributes to Object Detection in Aquatic Scenes

arXiv: https://arxiv.org/abs/2003.01913 · HTML: https://mlsanity.com/paper/2003.01913/

## Key facts

- Cited by 7 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning](https://mlsanity.com/paper/2104.02230/) (2021)
- [Is Underwater Image Enhancement All Object Detectors Need?](https://mlsanity.com/paper/2311.18814/) (2023)
- [A Gated Cross-domain Collaborative Network for Underwater Object Detection](https://mlsanity.com/paper/2306.14141/) (2023)
- [Underwater Object Classification and Detection: first results and open challenges](https://mlsanity.com/paper/2201.00977/) (2022)
- [Beneath the Surface: The Role of Underwater Image Enhancement in Object Detection](https://mlsanity.com/paper/2411.14626/) (2024)
- [ADOD: Adaptive Domain-Aware Object Detection with Residual Attention for Underwater Environments](https://mlsanity.com/paper/2312.06801/) (2023)
- [A high-precision underwater object detection based on joint self-supervised deblurring and improved spatial transformer network](https://mlsanity.com/paper/2203.04822/) (2022)

## Abstract (arXiv)

Underwater robotic perception usually requires visual restoration and object detection, both of which have been studied for many years. Meanwhile, data domain has a huge impact on modern data-driven leaning process. However, exactly indicating domain effect, the relation between restoration and detection remains unclear. In this paper, we generally investigate the relation of quality-diverse data domain to detection performance. In the meantime, we unveil how visual restoration contributes to object detection in real-world underwater scenes. According to our analysis, five key discoveries are reported: 1) Domain quality has an ignorable effect on within-domain convolutional representation and detection accuracy; 2) low-quality domain leads to higher generalization ability in cross-domain detection; 3) low-quality domain can hardly be well learned in a domain-mixed learning process; 4) degrading recall efficiency, restoration cannot improve within-domain detection accuracy; 5) visual restoration is beneficial to detection in the wild by reducing the domain shift between training data and real-world scenes. Finally, as an illustrative example, we successfully perform underwater object detection with an aquatic robot.