Underwater video analysis is particularly challenging due to factors such as low lighting, color distortion, and turbidity, which compromise visual data quality and directly impact the performance of perception modules in robotic applications. This work proposes AquaFeat+, a plug-and-play pipeline designed to enhance features specifically for automated vision tasks, rather than for human perceptual quality. The architecture includes modules for color correction, hierarchical feature enhancement, and an adaptive residual output, which are trained end-to-end and guided directly by the loss function of the final application. Trained and evaluated in the FishTrack23 dataset, AquaFeat+ achieves significant improvements in object detection, classification, and tracking metrics, validating its effectiveness for enhancing perception tasks in underwater robotic applications.

翻译：水下视频分析因光照不足、色彩失真及水体浑浊等因素而极具挑战，这些因素会损害视觉数据质量，并直接影响机器人应用中感知模块的性能。本文提出AquaFeat+，一种即插即用的处理流程，旨在专门针对自动化视觉任务（而非人类感知质量）进行特征增强。该架构包含色彩校正、分层特征增强和自适应残差输出模块，这些模块以端到端方式训练，并直接由最终应用的损失函数指导。在FishTrack23数据集上进行训练与评估后，AquaFeat+在目标检测、分类与跟踪指标上均取得显著提升，验证了其在水下机器人应用中增强感知任务的有效性。