Abstract
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.
Abstract (translated)
在水下环境中，图像质量严重退化会影响物体检测模型的性能，因为传统的图像增强方法往往没有针对此类下游任务进行优化。为了解决这一问题，我们提出了AquaFeat，这是一种新颖且即插即用的功能增强模块，旨在执行任务驱动型特征增强。我们的方法将多尺度特征增强网络与检测器的损失函数端到端集成，确保增强过程明确地指导那些对检测任务最为相关的特征进行优化。 当我们将AquaFeat与YOLOv8m在具有挑战性的水下数据集上结合使用时，在精度（Precision 0.877）和召回率（Recall 0.624）方面达到了业界领先水平，同时还在竞争性平均精确度分数（mAP@0.5为0.677，mAP@[0.5:0.95]为0.421）上表现出色。通过在保持每秒处理速度达46.5帧的实用性能的同时提供这些精度改进，我们的模型为实际应用提供了有效的且计算效率高的解决方案，例如海洋生态系统的监测和基础设施检查。
URL
https://arxiv.org/abs/2508.12343
