UnitModule: A Lightweight Joint Image Enhancement Module for Underwater
  Object Detection
Zhuoyan Liu Bo Wang Ye Li Jiaxian He Yunfeng Li
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 Underwater joint image enhancement
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 (URPCtest). 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.