Enhancing Detection of Remotely-Sensed Floating Objects via Data Augmentation for Maritime SAR

Haitham Mahmoud; Ibnu F. Kurniawan; Adel Aneiba; A. Taufiq Asyhari

doi:10.1007/s12524-024-01869-3

Enhancing Detection of Remotely-Sensed Floating Objects via Data Augmentation for Maritime SAR

Haitham Mahmoud, Ibnu F. Kurniawan, Adel Aneiba, A. Taufiq Asyhari

Research Centre for Computational Science and Mathematical Modelling

Research output: Contribution to journal › Article › peer-review

19 Downloads (Pure)

Abstract

A figure of 33,000 search and rescue (SAR) incidents were responded to by the UK’s HM Coastguard in 2020, and over 1322 rescue missions were conducted by SAR helicopters during that year. Combined with Unmanned Aerial Vehicles (UAVs), artificial intelligence, and computer vision, SAR operations can be revolutionized through enabling rescuers to expand ground coverage with improved detection accuracy whilst reducing costs and personal injury risks. However, detecting small objects is one of the significant challenges associated with using computer vision on UAVs. Several approaches have been proposed for improving small object detection, including data augmentation techniques like replication and variation of image sizes, but their suitability for SAR application characteristics remains questionable. To address these issues, this paper evaluates four float detection algorithms against the baseline and augmented datasets to improve float detection for maritime SAR. Results demonstrated that YOLOv8 and YOLOv5 outperformed the others in which F1 scores ranged from 82.9 to 95.3%, with an enhancement range of 0.1–29.2%. These models were both of low complexity and capable of real-time response.

Original language	English
Pages (from-to)	1285-1295
Number of pages	11
Journal	Journal of the Indian Society of Remote Sensing
Volume	52
Issue number	6
Early online date	19 May 2024
DOIs	https://doi.org/10.1007/s12524-024-01869-3
Publication status	Published - 1 Jun 2024

Bibliographical note

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Funder

Open Access funding enabled and organized by CAUL and its Member Institutions. This work was supported in part by the British Council COP26 Trilateral Research Initiative Grants for Phase-I and Phase-II. A. Taufiq Asyhari acknowledged support from the Academic Research Startup Grant at Monash University. Ibnu F. Kurniawan acknowledged support from the Directorate General of Higher Education, Research, and Technology, Indonesia.

Keywords

Remote sensing
Maritime SAR
Data augmentation
Float detection

Access to Document

10.1007/s12524-024-01869-3Licence: CC BY

Kurniawan2024VoRFinal published version, 2 MBLicence: CC BY

Cite this

@article{db706f13702347dc831cc51696ab0e90,

title = "Enhancing Detection of Remotely-Sensed Floating Objects via Data Augmentation for Maritime SAR",

abstract = "A figure of 33,000 search and rescue (SAR) incidents were responded to by the UK{\textquoteright}s HM Coastguard in 2020, and over 1322 rescue missions were conducted by SAR helicopters during that year. Combined with Unmanned Aerial Vehicles (UAVs), artificial intelligence, and computer vision, SAR operations can be revolutionized through enabling rescuers to expand ground coverage with improved detection accuracy whilst reducing costs and personal injury risks. However, detecting small objects is one of the significant challenges associated with using computer vision on UAVs. Several approaches have been proposed for improving small object detection, including data augmentation techniques like replication and variation of image sizes, but their suitability for SAR application characteristics remains questionable. To address these issues, this paper evaluates four float detection algorithms against the baseline and augmented datasets to improve float detection for maritime SAR. Results demonstrated that YOLOv8 and YOLOv5 outperformed the others in which F1 scores ranged from 82.9 to 95.3%, with an enhancement range of 0.1–29.2%. These models were both of low complexity and capable of real-time response.",

keywords = "Remote sensing, Maritime SAR, Data augmentation, Float detection",

author = "Haitham Mahmoud and Kurniawan, {Ibnu F.} and Adel Aneiba and Asyhari, {A. Taufiq}",

note = "This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article{\textquoteright}s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article{\textquoteright}s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.",

year = "2024",

month = jun,

day = "1",

doi = "10.1007/s12524-024-01869-3",

language = "English",

volume = "52",

pages = "1285--1295",

journal = "Journal of the Indian Society of Remote Sensing",

issn = "0255-660X",

publisher = "Springer India",

number = "6",

}

TY - JOUR

T1 - Enhancing Detection of Remotely-Sensed Floating Objects via Data Augmentation for Maritime SAR

AU - Mahmoud, Haitham

AU - Kurniawan, Ibnu F.

AU - Aneiba, Adel

AU - Asyhari, A. Taufiq

N1 - This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

PY - 2024/6/1

Y1 - 2024/6/1

N2 - A figure of 33,000 search and rescue (SAR) incidents were responded to by the UK’s HM Coastguard in 2020, and over 1322 rescue missions were conducted by SAR helicopters during that year. Combined with Unmanned Aerial Vehicles (UAVs), artificial intelligence, and computer vision, SAR operations can be revolutionized through enabling rescuers to expand ground coverage with improved detection accuracy whilst reducing costs and personal injury risks. However, detecting small objects is one of the significant challenges associated with using computer vision on UAVs. Several approaches have been proposed for improving small object detection, including data augmentation techniques like replication and variation of image sizes, but their suitability for SAR application characteristics remains questionable. To address these issues, this paper evaluates four float detection algorithms against the baseline and augmented datasets to improve float detection for maritime SAR. Results demonstrated that YOLOv8 and YOLOv5 outperformed the others in which F1 scores ranged from 82.9 to 95.3%, with an enhancement range of 0.1–29.2%. These models were both of low complexity and capable of real-time response.

AB - A figure of 33,000 search and rescue (SAR) incidents were responded to by the UK’s HM Coastguard in 2020, and over 1322 rescue missions were conducted by SAR helicopters during that year. Combined with Unmanned Aerial Vehicles (UAVs), artificial intelligence, and computer vision, SAR operations can be revolutionized through enabling rescuers to expand ground coverage with improved detection accuracy whilst reducing costs and personal injury risks. However, detecting small objects is one of the significant challenges associated with using computer vision on UAVs. Several approaches have been proposed for improving small object detection, including data augmentation techniques like replication and variation of image sizes, but their suitability for SAR application characteristics remains questionable. To address these issues, this paper evaluates four float detection algorithms against the baseline and augmented datasets to improve float detection for maritime SAR. Results demonstrated that YOLOv8 and YOLOv5 outperformed the others in which F1 scores ranged from 82.9 to 95.3%, with an enhancement range of 0.1–29.2%. These models were both of low complexity and capable of real-time response.

KW - Remote sensing

KW - Maritime SAR

KW - Data augmentation

KW - Float detection

UR - http://www.scopus.com/inward/record.url?scp=85193485929&partnerID=8YFLogxK

U2 - 10.1007/s12524-024-01869-3

DO - 10.1007/s12524-024-01869-3

M3 - Article

SN - 0255-660X

VL - 52

SP - 1285

EP - 1295

JO - Journal of the Indian Society of Remote Sensing

JF - Journal of the Indian Society of Remote Sensing

IS - 6

ER -

Enhancing Detection of Remotely-Sensed Floating Objects via Data Augmentation for Maritime SAR

Abstract

Bibliographical note

Funder

Keywords

Access to Document

Other files and links

Fingerprint

Cite this