Underwater Object Detection for Smooth and Autonomous Operations of Naval Missions: A Pilot Dataset

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

doi:10.1007/978-981-97-1417-9_11

Underwater Object Detection for Smooth and Autonomous Operations of Naval Missions: A Pilot Dataset

Yijun Yan, Yinhe Li, Hanhe Lin, Md Mostafa Kamal Sarker, Jinchang Ren (Lead / Corresponding author), John McCall

Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Advances in Brain Inspired Cognitive Systems
Subtitle of host publication	13th International Conference, BICS 2023, Proceedings
Editors	Jinchang Ren, Amir Hussain, Iman Yi Liao, Rongjun Chen, Kaizhu Huang, Huimin Zhao, Xiaoyong Liu, Ping Ma, Thomas Maul
Publisher	Springer Singapore
Pages	113-122
Number of pages	10
ISBN (Electronic)	9789819714179
ISBN (Print)	9789819714162
DOIs	https://doi.org/10.1007/978-981-97-1417-9_11
Publication status	E-pub ahead of print - 22 May 2024
Event	13th International Conference on Brain Inspired Cognitive Systems, BICS 2023 - Kuala Lumpur, Malaysia Duration: 5 Aug 2023 → 6 Aug 2023 https://easychair.org/cfp/BICS2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14374 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Brain Inspired Cognitive Systems, BICS 2023
Country/Territory	Malaysia
City	Kuala Lumpur
Period	5/08/23 → 6/08/23
Internet address	https://easychair.org/cfp/BICS2023

Keywords

Deep learning
Image enhancement
Underwater object detection

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-981-97-1417-9_11

Cite this

Yan, Y., Li, Y., Lin, H., Sarker, M. M. K., Ren, J., & McCall, J. (2024). Underwater Object Detection for Smooth and Autonomous Operations of Naval Missions: A Pilot Dataset. In J. Ren, A. Hussain, I. Y. Liao, R. Chen, K. Huang, H. Zhao, X. Liu, P. Ma, & T. Maul (Eds.), Advances in Brain Inspired Cognitive Systems: 13th International Conference, BICS 2023, Proceedings (pp. 113-122). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14374 LNAI). Springer Singapore. Advance online publication. https://doi.org/10.1007/978-981-97-1417-9_11

Yan, Yijun ; Li, Yinhe ; Lin, Hanhe et al. / Underwater Object Detection for Smooth and Autonomous Operations of Naval Missions : A Pilot Dataset. Advances in Brain Inspired Cognitive Systems: 13th International Conference, BICS 2023, Proceedings. editor / Jinchang Ren ; Amir Hussain ; Iman Yi Liao ; Rongjun Chen ; Kaizhu Huang ; Huimin Zhao ; Xiaoyong Liu ; Ping Ma ; Thomas Maul. Springer Singapore, 2024. pp. 113-122 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Underwater Object Detection for Smooth and Autonomous Operations of Naval Missions: A Pilot Dataset",

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.",

keywords = "Deep learning, Image enhancement, Underwater object detection",

author = "Yijun Yan and Yinhe Li and Hanhe Lin and Sarker, {Md Mostafa Kamal} and Jinchang Ren and John McCall",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 13th International Conference on Brain Inspired Cognitive Systems, BICS 2023 ; Conference date: 05-08-2023 Through 06-08-2023",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Jinchang Ren and Amir Hussain and Liao, {Iman Yi} and Rongjun Chen and Kaizhu Huang and Huimin Zhao and Xiaoyong Liu and Ping Ma and Thomas Maul",

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Yan, Y, Li, Y, Lin, H, Sarker, MMK, Ren, J & McCall, J 2024, Underwater Object Detection for Smooth and Autonomous Operations of Naval Missions: A Pilot Dataset. in J Ren, A Hussain, IY Liao, R Chen, K Huang, H Zhao, X Liu, P Ma & T Maul (eds), Advances in Brain Inspired Cognitive Systems: 13th International Conference, BICS 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14374 LNAI, Springer Singapore, pp. 113-122, 13th International Conference on Brain Inspired Cognitive Systems, BICS 2023, Kuala Lumpur, Malaysia, 5/08/23. https://doi.org/10.1007/978-981-97-1417-9_11

Underwater Object Detection for Smooth and Autonomous Operations of Naval Missions: A Pilot Dataset. / Yan, Yijun; Li, Yinhe; Lin, Hanhe et al.
Advances in Brain Inspired Cognitive Systems: 13th International Conference, BICS 2023, Proceedings. ed. / Jinchang Ren; Amir Hussain; Iman Yi Liao; Rongjun Chen; Kaizhu Huang; Huimin Zhao; Xiaoyong Liu; Ping Ma; Thomas Maul. Springer Singapore, 2024. p. 113-122 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14374 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Underwater Object Detection for Smooth and Autonomous Operations of Naval Missions

T2 - 13th International Conference on Brain Inspired Cognitive Systems, BICS 2023

AU - Yan, Yijun

AU - Li, Yinhe

AU - Lin, Hanhe

AU - Sarker, Md Mostafa Kamal

AU - Ren, Jinchang

AU - McCall, John

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024/5/22

Y1 - 2024/5/22

N2 - 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.

AB - 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.

KW - Deep learning

KW - Image enhancement

KW - Underwater object detection

U2 - 10.1007/978-981-97-1417-9_11

DO - 10.1007/978-981-97-1417-9_11

M3 - Conference contribution

AN - SCOPUS:85195109163

SN - 9789819714162

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 113

EP - 122

BT - Advances in Brain Inspired Cognitive Systems

A2 - Ren, Jinchang

A2 - Hussain, Amir

A2 - Liao, Iman Yi

A2 - Chen, Rongjun

A2 - Huang, Kaizhu

A2 - Zhao, Huimin

A2 - Liu, Xiaoyong

A2 - Ma, Ping

A2 - Maul, Thomas

PB - Springer Singapore

Y2 - 5 August 2023 through 6 August 2023

ER -

Yan Y, Li Y, Lin H, Sarker MMK, Ren J, McCall J. Underwater Object Detection for Smooth and Autonomous Operations of Naval Missions: A Pilot Dataset. In Ren J, Hussain A, Liao IY, Chen R, Huang K, Zhao H, Liu X, Ma P, Maul T, editors, Advances in Brain Inspired Cognitive Systems: 13th International Conference, BICS 2023, Proceedings. Springer Singapore. 2024. p. 113-122. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2024 May 22. doi: 10.1007/978-981-97-1417-9_11

Underwater Object Detection for Smooth and Autonomous Operations of Naval Missions: A Pilot Dataset

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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