Underwater object detection for smooth and autonomous operations of naval missions: a pilot Dataset.

Yan, Yijun; Li, Yinhe; Lin, Hanhe; Sarker, Md Mostafa Kamal; Ren, Jinchang; McCall, John

Abstract

Underwater object detection is essential for ensuring autonomous naval operations. However, this task is challenging due to the complexities of underwater environments that often degrade image quality, thereby hampering the performance of detection and classification systems. On the other hand, the absence of a readily available dataset complicates the development and evaluation of underwater object detection approaches, particularly for deep learning approaches. To address this bottleneck, we have created a new dataset, called National Subsea Centre Underwater Images (NSCUI). It is comprised of 243 images, divided into three subsets that are captured in bright, low-light, and dark environments, respectively. To validate the utility of this dataset, we implemented three popular deep learning models in our experiments. We believe that the annotated NSCUI will significantly advance the development of underwater object detection through the application of deep learning techniques.

Citation

YAN, Y., LI, Y., LIN, H., SARKER, M.M.K., REN, J. and MCCALL, J. 2024. Underwater object detection for smooth and autonomous operations of naval missions: a pilot dataset. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 113-122. Available from: https://doi.org/10.1007/978-981-97-1417-9_11

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023)
• Start Date: Aug 5, 2023
• End Date: Aug 6, 2023
• Acceptance Date: Jul 28, 2023
• Online Publication Date: May 22, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Jun 13, 2024
• Publicly Available Date: May 23, 2025
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 113-122
• Series Title: Lecture notes in computer science (LNCS)
• Series Number: 14374
• Series ISSN: 0302-9743; 1611-3349
• Book Title: Advances in brain inspired cognitive systems
• ISBN: 9789819714162
• DOI: https://doi.org/10.1007/978-981-97-1417-9_11
• Keywords: Underwater object detection; Image enhancement; Deep learning
• Public URL: https://rgu-repository.worktribe.com/output/2372862