Underwater Detection: A Brief Survey and a New Multitask Dataset

Yu Wei, Yi Wang, Baofeng Zhu, Chi Lin, Dan Wu, Xinwei Xue, Ruili Wang

Research output: Journal PublicationArticlepeer-review

7 Citations (Scopus)

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 dif-ficulties 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.

Original languageEnglish
Article number100025
JournalInternational Journal of Network Dynamics and Intelligence
Volume3
Issue number4
DOIs
Publication statusPublished - 2024

Keywords

  • object detection
  • salient object detection
  • underwater dataset
  • underwater image enhancement

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications

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