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.
@article{arxiv.2508.12343,
title = {AquaFeat: A Features-Based Image Enhancement Model for Underwater Object Detection},
author = {Emanuel C. Silva and Tatiana T. Schein and Stephanie L. Brião and Guilherme L. M. Costa and Felipe G. Oliveira and Gustavo P. Almeida and Eduardo L. Silva and Sam S. Devincenzi and Karina S. Machado and Paulo L. J. Drews-Jr},
journal= {arXiv preprint arXiv:2508.12343},
year = {2025}
}