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Open AccessArticle
Benchmarking YOLO Models for Marine Search and Rescue in Variable Weather Conditions
by
Aysha Alshibli
Aysha Alshibli and
Qurban Memon
Qurban Memon *
ECE Department, College of Engineering, UAE University, Al Ain 15551, United Arab Emirates
*
Author to whom correspondence should be addressed.
Automation 2025, 6(3), 35; https://doi.org/10.3390/automation6030035 (registering DOI)
Submission received: 8 June 2025
/
Revised: 13 July 2025
/
Accepted: 24 July 2025
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Published: 2 August 2025
Abstract
Deep learning with unmanned aerial vehicles (UAVs) is transforming maritime search and rescue (SAR) by enabling rapid object identification in challenging marine environments. This study benchmarks the performance of YOLO models for maritime SAR under diverse weather conditions using the SeaDronesSee and AFO datasets. The results show that while YOLOv7 achieved the highest mAP@50, it struggled with detecting small objects. In contrast, YOLOv10 and YOLOv11 deliver faster inference speeds but compromise slightly on precision. The key challenges discussed include environmental variability, sensor limitations, and scarce annotated data, which can be addressed by such techniques as attention modules and multimodal data fusion. Overall, the research results provide practical guidance for deploying efficient deep learning models in SAR, emphasizing specialized datasets and lightweight architectures for edge devices.
Share and Cite
MDPI and ACS Style
Alshibli, A.; Memon, Q.
Benchmarking YOLO Models for Marine Search and Rescue in Variable Weather Conditions. Automation 2025, 6, 35.
https://doi.org/10.3390/automation6030035
AMA Style
Alshibli A, Memon Q.
Benchmarking YOLO Models for Marine Search and Rescue in Variable Weather Conditions. Automation. 2025; 6(3):35.
https://doi.org/10.3390/automation6030035
Chicago/Turabian Style
Alshibli, Aysha, and Qurban Memon.
2025. "Benchmarking YOLO Models for Marine Search and Rescue in Variable Weather Conditions" Automation 6, no. 3: 35.
https://doi.org/10.3390/automation6030035
APA Style
Alshibli, A., & Memon, Q.
(2025). Benchmarking YOLO Models for Marine Search and Rescue in Variable Weather Conditions. Automation, 6(3), 35.
https://doi.org/10.3390/automation6030035
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