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Article

Benchmarking YOLO Models for Marine Search and Rescue in Variable Weather Conditions

ECE Department, College of Engineering, UAE University, Al Ain 15551, United Arab Emirates
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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 / Published: 2 August 2025
(This article belongs to the Section Intelligent Control and Machine Learning)

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.
Keywords: YOLO; YOLOv7; YOLOv10; YOLOv11; marine object detection; search and rescue YOLO; YOLOv7; YOLOv10; YOLOv11; marine object detection; search and rescue

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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