Year: 2024
Type: article
Abstract: Underwater detection poses significant challenges due to the unique characteristics of the underwater environment, such as light attenuation, scattering, water turbidity, and the presence of small or camouflaged objects. To gain a clearer understanding of these challenges, we first review two common detection tasks: object detection (OD) and salient object detection (SOD). Next, we examine the difficulties of adapting existing OD and SOD techniques to underwater settings. Additionally, we introduce a new Underwater Object Multitask (UOMT) dataset, complete with benchmarks. This survey, along with the proposed dataset, aims to provide valuable resources to researchers and practitioners to develop more effective techniques to address the challenges of underwater detection. The UOMT dataset and benchmarks are available at https://github.com/yiwangtz/UOMT.
Authors: Yu Wei, Yi Wang, Baofeng Zhu, Chi Lin, Dan Wu, Xinwei Xue, Ruili Wang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Awards: G2995702002, G5176622573, G8789890064
OpenAlex ID: W4405790574