# Beneath the Surface: The Role of Underwater Image Enhancement in Object Detection

arXiv: https://arxiv.org/abs/2411.14626 · HTML: https://mlsanity.com/paper/2411.14626/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 54 references that are in the map.
- First posted on arXiv 21 Nov 2024.

## Builds on (most cited first, 17 of 17)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)
- [DSSD : Deconvolutional Single Shot Detector](https://mlsanity.com/paper/1701.06659/) (2017)
- [TIDE: A General Toolbox for Identifying Object Detection Errors](https://mlsanity.com/paper/2008.08115/) (2020)
- [Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank](https://mlsanity.com/paper/2303.09101/) (2023)
- [A Dataset And Benchmark Of Underwater Object Detection For Robot Picking](https://mlsanity.com/paper/2106.05681/) (2021)
- [RoIMix: Proposal-Fusion among Multiple Images for Underwater Object Detection](https://mlsanity.com/paper/1911.03029/) (2019)
- [A New Dataset, Poisson GAN and AquaNet for Underwater Object Grabbing](https://mlsanity.com/paper/2003.01446/) (2020)
- [Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement](https://mlsanity.com/paper/2308.15816/) (2023)
- [Towards Real-Time Advancement of Underwater Visual Quality with GAN](https://mlsanity.com/paper/1712.00736/) (2017)
- [Underwater object detection using Invert Multi-Class Adaboost with deep learning](https://mlsanity.com/paper/2005.11552/) (2020)
- [Is Underwater Image Enhancement All Object Detectors Need?](https://mlsanity.com/paper/2311.18814/) (2023)
- [Reveal of Domain Effect: How Visual Restoration Contributes to Object Detection in Aquatic Scenes](https://mlsanity.com/paper/2003.01913/) (2020)
- [Assessing fish abundance from underwater video using deep neural networks](https://mlsanity.com/paper/1807.05838/) (2018)

## Built on by (most cited first, 3 of 3)

- [VRS-UIE: Value-Driven Reordering Scanning for Underwater Image Enhancement](https://mlsanity.com/paper/2505.01224/) (2025)
- [An Empirical Study on the Robustness of YOLO Models for Underwater Object Detection](https://mlsanity.com/paper/2509.17561/) (2025)
- [Harmonizing the Deep: A Unified Information Pipeline for Robust Marine Biodiversity Assessment Across Heterogeneous Domains](https://mlsanity.com/paper/2601.13975/) (2026)

## Abstract (arXiv)

Underwater imagery often suffers from severe degradation resulting in low visual quality and reduced object detection performance. This work aims to evaluate state-of-the-art image enhancement models, investigate their effects on underwater object detection, and explore their potential to improve detection performance. To this end, we apply nine recent underwater image enhancement models, covering physical, non-physical and learning-based categories, to two recent underwater image datasets. Following this, we conduct joint qualitative and quantitative analyses on the original and enhanced images, revealing the discrepancy between the two analyses, and analyzing changes in the quality distribution of the images after enhancement. We then train three recent object detection models on the original datasets, selecting the best-performing detector for further analysis. This detector is subsequently re-trained on the enhanced datasets to evaluate changes in detection performance, highlighting the adverse effect of enhancement on detection performance at the dataset level. Next, we perform a correlation study to examine the relationship between various enhancement metrics and the mean Average Precision (mAP). Finally, we conduct an image-level analysis that reveals images of improved detection performance after enhancement. The findings of this study demonstrate the potential of image enhancement to improve detection performance and provide valuable insights for researchers to further explore the effects of enhancement on detection at the individual image level, rather than at the dataset level. This could enable the selective application of enhancement for improved detection. The data generated, code developed, and supplementary materials are publicly available at: https://github.com/RSSL-MTU/Enhancement-Detection-Analysis.