# UnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detection

Source: [UnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detection](<https://www.sota2.com/research/paper/68fa79d51e209131cea7f9a4>)

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

Data updated: 2025-10-27T06:56:50.936Z

Authors: Zhuoyan Liu, Bo Wang, Ye Li, Jiaxian He, Yunfeng Li

Published: 2023-09-09T07:30:20.000Z

## Abstract

Underwater object detection faces the problem of underwater image degradation, which affects the performance of the detector. Underwater object detection methods based on noise reduction and image enhancement usually do not provide images preferred by the detector or require additional datasets. In this paper, we propose a plug-and-play \\textbf{U}nderwater joi\\textbf{n}t \\textbf{i}mage enhancemen\\textbf{t} \\textbf{Module} (UnitModule) that provides the input image preferred by the detector. We design an unsupervised learning loss for the joint training of UnitModule with the detector without additional datasets to improve the interaction between UnitModule and the detector. Furthermore, a color cast predictor with the assisting color cast loss and a data augmentation called Underwater Color Random Transfer (UCRT) are designed to improve the performance of UnitModule on underwater images with different color casts. Extensive experiments are conducted on DUO for different object detection models, where UnitModule achieves the highest performance improvement of 2.6 AP for YOLOv5-S and gains the improvement of 3.3 AP on the brand-new test set (\\(\\text{URPC}\_{test}\\)). And UnitModule significantly improves the performance of all object detection models we test, especially for models with a small number of parameters. In addition, UnitModule with a small number of parameters of 31K has little effect on the inference speed of the original object detection model. Our quantitative and visual analysis also demonstrates the effectiveness of UnitModule in enhancing the input image and improving the perception ability of the detector for object features. The code is available at https://github.com/LEFTeyex/UnitModule.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2309.04708>)

[PDF](<https://arxiv.org/pdf/2309.04708.pdf>)