Deep Learning Architecture based Multi Class Coral Reef Image Classification

Adithya Balaji; S. Yogesh; C. K. Kalyaan; R. Narayanamoorthi; Gerard Dooly; Samiappan Dhanalakshmi

doi:10.1109/OCEANSLimerick52467.2023.10244437

Deep Learning Architecture based Multi Class Coral Reef Image Classification

Adithya Balaji
, S. Yogesh
, C. K. Kalyaan
, R. Narayanamoorthi
, Gerard Dooly
, Samiappan Dhanalakshmi

SRM Institute of Science and Technology

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

Abstract

Deep learning techniques have abundant possibilities on how it can be used on a certain set of topics. One such topic we chose to use deep learning architecture to classify is the coral reef. Underwater resources like coral reefs are abundant. It is important to keep a track on coral reefs as it has a lot of benefits for the environment. As a result, there is a growing need for effective methods of monitoring and protecting these valuable ecosystems. One promising approach for addressing this need is the use of deep learning models for coral reef image classification and detection. We use YOLOv5 and its models classify the different types of coral reefs that are present under the water. Different classes of coral reefs are also detected using YOLOv5 algorithm and all the models of YOLOv5 are compared with each other.

Original language	English
Title of host publication	OCEANS 2023 - Limerick, OCEANS Limerick 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350332261
DOIs	https://doi.org/10.1109/OCEANSLimerick52467.2023.10244437
Publication status	Published - 2023
Event	2023 OCEANS Limerick, OCEANS Limerick 2023 - Limerick, Ireland Duration: 5 Jun 2023 → 8 Jun 2023

Publication series

Name	OCEANS 2023 - Limerick, OCEANS Limerick 2023

Conference

Conference	2023 OCEANS Limerick, OCEANS Limerick 2023
Country/Territory	Ireland
City	Limerick
Period	5/06/23 → 8/06/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 6 Clean Water and Sanitation

Keywords

Deep Learning
Neural Networks
Object Detection

Access to Document

10.1109/OCEANSLimerick52467.2023.10244437

Cite this

Balaji, A., Yogesh, S., Kalyaan, C. K., Narayanamoorthi, R., Dooly, G., & Dhanalakshmi, S. (2023). Deep Learning Architecture based Multi Class Coral Reef Image Classification. In OCEANS 2023 - Limerick, OCEANS Limerick 2023 (OCEANS 2023 - Limerick, OCEANS Limerick 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/OCEANSLimerick52467.2023.10244437

@inproceedings{41de05d409f2492fb4a4d9e2daa51cd5,

title = "Deep Learning Architecture based Multi Class Coral Reef Image Classification",

abstract = "Deep learning techniques have abundant possibilities on how it can be used on a certain set of topics. One such topic we chose to use deep learning architecture to classify is the coral reef. Underwater resources like coral reefs are abundant. It is important to keep a track on coral reefs as it has a lot of benefits for the environment. As a result, there is a growing need for effective methods of monitoring and protecting these valuable ecosystems. One promising approach for addressing this need is the use of deep learning models for coral reef image classification and detection. We use YOLOv5 and its models classify the different types of coral reefs that are present under the water. Different classes of coral reefs are also detected using YOLOv5 algorithm and all the models of YOLOv5 are compared with each other.",

keywords = "Deep Learning, Neural Networks, Object Detection",

author = "Adithya Balaji and S. Yogesh and Kalyaan, \{C. K.\} and R. Narayanamoorthi and Gerard Dooly and Samiappan Dhanalakshmi",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 OCEANS Limerick, OCEANS Limerick 2023 ; Conference date: 05-06-2023 Through 08-06-2023",

year = "2023",

doi = "10.1109/OCEANSLimerick52467.2023.10244437",

language = "English",

series = "OCEANS 2023 - Limerick, OCEANS Limerick 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "OCEANS 2023 - Limerick, OCEANS Limerick 2023",

}

Balaji, A, Yogesh, S, Kalyaan, CK, Narayanamoorthi, R, Dooly, G & Dhanalakshmi, S 2023, Deep Learning Architecture based Multi Class Coral Reef Image Classification. in OCEANS 2023 - Limerick, OCEANS Limerick 2023. OCEANS 2023 - Limerick, OCEANS Limerick 2023, Institute of Electrical and Electronics Engineers Inc., 2023 OCEANS Limerick, OCEANS Limerick 2023, Limerick, Ireland, 5/06/23. https://doi.org/10.1109/OCEANSLimerick52467.2023.10244437

Deep Learning Architecture based Multi Class Coral Reef Image Classification. / Balaji, Adithya; Yogesh, S.; Kalyaan, C. K. et al.
OCEANS 2023 - Limerick, OCEANS Limerick 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (OCEANS 2023 - Limerick, OCEANS Limerick 2023).

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

TY - GEN

T1 - Deep Learning Architecture based Multi Class Coral Reef Image Classification

AU - Balaji, Adithya

AU - Yogesh, S.

AU - Kalyaan, C. K.

AU - Narayanamoorthi, R.

AU - Dooly, Gerard

AU - Dhanalakshmi, Samiappan

PY - 2023

Y1 - 2023

N2 - Deep learning techniques have abundant possibilities on how it can be used on a certain set of topics. One such topic we chose to use deep learning architecture to classify is the coral reef. Underwater resources like coral reefs are abundant. It is important to keep a track on coral reefs as it has a lot of benefits for the environment. As a result, there is a growing need for effective methods of monitoring and protecting these valuable ecosystems. One promising approach for addressing this need is the use of deep learning models for coral reef image classification and detection. We use YOLOv5 and its models classify the different types of coral reefs that are present under the water. Different classes of coral reefs are also detected using YOLOv5 algorithm and all the models of YOLOv5 are compared with each other.

AB - Deep learning techniques have abundant possibilities on how it can be used on a certain set of topics. One such topic we chose to use deep learning architecture to classify is the coral reef. Underwater resources like coral reefs are abundant. It is important to keep a track on coral reefs as it has a lot of benefits for the environment. As a result, there is a growing need for effective methods of monitoring and protecting these valuable ecosystems. One promising approach for addressing this need is the use of deep learning models for coral reef image classification and detection. We use YOLOv5 and its models classify the different types of coral reefs that are present under the water. Different classes of coral reefs are also detected using YOLOv5 algorithm and all the models of YOLOv5 are compared with each other.

KW - Deep Learning

KW - Neural Networks

KW - Object Detection

UR - https://www.scopus.com/pages/publications/85173658089

U2 - 10.1109/OCEANSLimerick52467.2023.10244437

DO - 10.1109/OCEANSLimerick52467.2023.10244437

M3 - Conference contribution

AN - SCOPUS:85173658089

T3 - OCEANS 2023 - Limerick, OCEANS Limerick 2023

BT - OCEANS 2023 - Limerick, OCEANS Limerick 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 OCEANS Limerick, OCEANS Limerick 2023

Y2 - 5 June 2023 through 8 June 2023

ER -

Deep Learning Architecture based Multi Class Coral Reef Image Classification

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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