# AquaFeat+: an Underwater Vision Learning-based Enhancement Method for Object Detection, Classification, and Tracking

Source: [AquaFeat+: an Underwater Vision Learning-based Enhancement Method for Object Detection, Classification, and Tracking](<https://www.sota2.com/research/paper/69687480e25ad2fa9d1ec500>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-16T05:00:11.882Z

Authors: Emanuel da Costa Silva, Tatiana Ta\\'is Schein, Jos\\'e David Garc\\'ia Ramos, Eduardo Lawson da Silva, Stephanie Loi Bri\\~ao, Felipe Gomes de Oliveira, Paulo Lilles Jorge Drews-Jr

Published: 2026-01-14T17:38:41.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Multi-Object Tracking on FishTrack 23 1.0 (test)](<https://www.sota2.com/research/sota/multi-object-tracking-on-fishtrack-23-1-0-test.md>): Multi-Object Tracking; FishTrack 23 1.0 (test); HOTA: 55.206.

- [Image Classification on FishTrack23](<https://www.sota2.com/research/sota/image-classification-on-fishtrack23.md>): Image Classification; FishTrack23; Precision: 81.6.

- [Object Detection on FishTrack23](<https://www.sota2.com/research/sota/object-detection-on-fishtrack23.md>): Object Detection; FishTrack23; Precision: 0.77.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2601.09652>)

[PDF](<https://arxiv.org/pdf/2601.09652.pdf>)