Cost-effective ecological monitoring in shallow waters using amphibious unmanned aerial vehicles (AUAV) and deep learning-based computer vision
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an amphibious unmanned aerial vehicle (AUAV) with deep learning for efficient marine monitoring. The AUAV system achieves high accuracy in detecting underwater litter and segmenting sea cucumbers, offering a practical alternative for shallow-water ecological surveys.
Area Of Science
- Marine Biology
- Robotics
- Artificial Intelligence
Background
- Traditional marine monitoring methods are inefficient and costly.
- Modern techniques like AUVs and P-SSS have limitations in cost, labor, and data quality.
Purpose Of The Study
- To develop an efficient and practical shallow-water ecological monitoring system.
- To integrate deep learning with an amphibious unmanned aerial vehicle (AUAV) for marine surveys.
Main Methods
- An amphibious unmanned aerial vehicle (AUAV) equipped for aerial and underwater imaging with real-time GNSS.
- Integration of YOLOv8 deep learning model for underwater litter detection and sea cucumber instance segmentation.
- Quantitative analysis of sea cucumber size using instance segmentation results.
Main Results
- YOLOv8 achieved a mean average precision (mAP) of 0.764 for underwater litter detection, a significant improvement over previous benchmarks.
- YOLOv8 demonstrated high performance in sea cucumber object detection (mAP 0.792) and instance segmentation (mAP 0.798).
- The framework enabled accurate pixel-level size estimation of sea cucumbers.
Conclusions
- The AUAV-YOLOv8 framework provides a highly efficient and quantitative solution for shallow-water ecological monitoring.
- This integrated system offers a practical and cost-effective alternative to conventional underwater survey platforms.
- The study highlights the potential of AI-powered drones for advancing marine research and conservation.
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