activity
20232026
most citedUnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detection

97 citations · 121 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

RSFusionDet: Underwater RGB-Sonar Multimodal Object Detection

Zhuoyan Liu, Yihan Wang, Bo Wang +2

Underwater unimodal object detection faces many challenges in sensor imaging, such as optical images limited by underwater noise and visible distance, and sonar images limited by l…

cs.CV2024★ 13 cited

U-DECN: End-to-End Underwater Object Detection ConvNet with Improved DeNoising Training

Zhuoyan Liu, Bo Wang, Bing Wang +1

Underwater object detection has higher requirements of running speed and deployment efficiency for the detector due to its specific environmental challenges. NMS of two- or one-sta…

cs.CV2023★ 10 cited

ShareCMP: Polarization-Aware RGB-P Semantic Segmentation

Zhuoyan Liu, Bo Wang, Lizhi Wang +2

Multimodal semantic segmentation is developing rapidly, but the modality of RGB-\textbf{P}olarization remains underexplored. To delve into this problem, we construct a UPLight RGB-…

cs.CV2023

Lightweight Full-Convolutional Siamese Tracker

Yunfeng Li, Bo Wang, Xueyi Wu +2

Although single object trackers have achieved advanced performance, their large-scale models hinder their application on limited resources platforms. Moreover, existing lightweight…

cs.CV2023★ 97 cited

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

Zhuoyan Liu, Bo Wang, Ye Li +2

Underwater object detection faces the problem of underwater image degradation, which affects the performance of the detector. Underwater object detection methods based on noise red…

cs.CV2023★ 1 cited

Underwater Object Tracker: UOSTrack for Marine Organism Grasping of Underwater Vehicles

Yunfeng Li, Bo Wang, Ye Li +4

A visual single-object tracker is an indispensable component of underwater vehicles (UVs) in marine organism grasping tasks. Its accuracy and stability are imperative to guide the…