# AquaFeat: A Features-Based Image Enhancement Model for Underwater Object Detection

arXiv: https://arxiv.org/abs/2508.12343 · HTML: https://mlsanity.com/paper/2508.12343/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 17 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis](https://mlsanity.com/paper/2008.12603/) (2020)
- [FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Vision](https://mlsanity.com/paper/2308.03594/) (2023)
- [Osmosis: RGBD Diffusion Prior for Underwater Image Restoration](https://mlsanity.com/paper/2403.14837/) (2024)
- [Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future](https://mlsanity.com/paper/2410.05577/) (2024)
- [AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection](https://mlsanity.com/paper/2308.11918/) (2023)
- [Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions](https://mlsanity.com/paper/2208.03319/) (2022)
- [UDBE: Unsupervised Diffusion-based Brightness Enhancement in Underwater Images](https://mlsanity.com/paper/2501.16211/) (2025)

## Built on by (most cited first, 1 of 1)

- [AquaFeat+: an Underwater Vision Learning-based Enhancement Method for Object Detection, Classification, and Tracking](https://mlsanity.com/paper/2601.09652/) (2026)

## Abstract (arXiv)

The severe image degradation in underwater environments impairs object detection models, as traditional image enhancement methods are often not optimized for such downstream tasks. To address this, we propose AquaFeat, a novel, plug-and-play module that performs task-driven feature enhancement. Our approach integrates a multi-scale feature enhancement network trained end-to-end with the detector's loss function, ensuring the enhancement process is explicitly guided to refine features most relevant to the detection task. When integrated with YOLOv8m on challenging underwater datasets, AquaFeat achieves state-of-the-art Precision (0.877) and Recall (0.624), along with competitive mAP scores (mAP@0.5 of 0.677 and mAP@[0.5:0.95] of 0.421). By delivering these accuracy gains while maintaining a practical processing speed of 46.5 FPS, our model provides an effective and computationally efficient solution for real-world applications, such as marine ecosystem monitoring and infrastructure inspection.