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 - Funding Information:
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.
Publisher Copyright:
© The Author(s) 2024.
PY - 2024/6
Y1 - 2024/6
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 - Data augmentation
KW - Float detection
KW - Maritime SAR
KW - Remote sensing
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
AN - SCOPUS:85193485929
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
ER -