NEREON - An Underwater Dataset for Monocular Depth Estimation

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Dionisio, JMM (Author) Other; Pereira, PNAAS (Author) Other; Leite, PN (Author) Other; Neves, FS (Author) Other; Tavares, JMRS (Author) Other; Pinto, AM (Author) FEUP

Conference proceedings International

Title: OCEANS 2023 - LIMERICK

OCEANS Conference

Limerick, IRELAND, JUN 05-08, 2023

Indexing

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Other information

Authenticus ID: P-00Z-5VY

DOI: 10.1109/oceanslimerick52467.2023.10244675

Abstract (EN): Structures associated with offshore wind energy production require an arduous and cyclical inspection and maintenance (O&M) procedure. Moreover, the harsh challenges introduced by sub-sea phenomena hamper visibility, considerably affecting underwater missions. The lack of quality 3D information within these environments hinders the applicability of autonomous solutions in close-range navigation, fault inspection and intervention tasks since these have a very poor perception of the surrounding space. Deep learning techniques are widely used to solve these challenges in aerial scenarios. The developments in this subject are limited regarding underwater environments due to the lack of publicly disseminated underwater information. This article presents a new underwater dataset: NEREON, containing both 2D and 3D data gathered within real underwater environments at the ATLANTIS Coastal Test Centre. This dataset is adequate for monocular depth estimation tasks, which can provide useful information during O&M missions. With this in mind, a benchmark comparing different deep learning approaches in the literature was conducted and presented along with the NEREON dataset.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7