IJNDI/
Articles/
2504000037
  • Open Access
  • Article
Underwater Detection: A Brief Survey and a New Multitask Dataset
  • Yu Wei 1, 2,   
  • Yi Wang 1, *,   
  • Baofeng Zhu 1,   
  • Chi Lin 1,   
  • Dan Wu 1,   
  • Xinwei Xue 1,   
  • Ruili Wang 3, 4

Received: 27 Jun 2023 | Accepted: 25 Apr 2024 | Published: 25 Dec 2024

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.

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DOI
10.53941/ijndi.2024.100025
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Volume 3, Issue 4
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Wei, Y.; Wang, Y.; Zhu, B.; Lin, C.; Wu, D.; Xue, X.; Wang, R. Underwater Detection: A Brief Survey and a New Multitask Dataset. International Journal of Network Dynamics and Intelligence 2024, 3 (4), 100025. https://doi.org/10.53941/ijndi.2024.100025.
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