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Enhancing life jacket detection for maritime search and rescue using YOLO models and illumination-robust
Jun Kit Kwong1, Ban-Hoe Kwan1, Humaira Nisar2
1Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Bandar Sungai Long, Cheras, 43000, Kajang, Selangor, Malaysia.
Scientific Reports
|June 8, 2026
Summary
Computer vision automates life jacket detection in maritime search and rescue (SAR) operations. YOLOv10 offers the best performance for faster, reliable identification of individuals in distress at sea.
Area of Science:
- Computer Vision
- Maritime Safety
- Artificial Intelligence
Background:
- Maritime Search and Rescue (SAR) faces challenges with vast areas and poor visibility.
- Automated detection of life jackets is crucial for improving SAR effectiveness.
Purpose of the Study:
- To investigate computer vision and object detection for automated life jacket detection in SAR.
- To develop and evaluate YOLO models and preprocessing techniques for challenging lighting conditions.
Main Methods:
- A custom dataset of life jacket images was created.
- YOLO object detection models (v5-v12) were evaluated.
- Image preprocessing techniques were applied to enhance detection in varied lighting.
Main Results:
- Preprocessing significantly improved detection in overexposed and underexposed images.
- YOLOv10 demonstrated the best balance of precision and real-time inference speed (43.9 FPS).
- YOLOv10 provides the optimal trade-off for time-sensitive rescue applications.
Conclusions:
- Automated life jacket detection using computer vision enhances SAR operations.
- YOLOv10 is a promising model for real-time maritime rescue applications.
- This study provides a benchmark for improving SAR outcomes through AI.
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