使用两无人机 (AUAV) 和基于深度学习的计算机视觉,在浅水中进行具有成本效益的生态监测
Fan Zhao1, Jiaqi Wang1, Yijia Chen1
1Graduate School of Frontier Sciences, The University of Tokyo, Japan.
Marine environmental research
|February 12, 2026
概括
本研究介绍了一种带有深度学习的两无人机 (AUAV),用于高效的海洋监测. 该AUAV系统在检测水下垃圾和细分海黄瓜方面取得了很高的准确性,为浅水生态调查提供了切实可行的替代方案.
科学领域:
- 海洋生物学 海洋生物学
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 传统的海洋监测方法是低效和昂贵的.
- 像AUV和P-SSS这样的现代技术在成本,劳动力和数据质量方面都有局限性.
研究的目的:
- 开发一个高效和实用的浅水生态监测系统.
- 将深度学习与两无人机 (AUAV) 整合起来,用于海洋调查.
主要方法:
- 一种两无人驾驶飞行器 (AUAV),配备实时GNSS,用于空中和水下成像.
- 整合YOLOv8深度学习模型用于水下垃圾检测和海黄实例细分.
- 使用实例细分结果对海黄瓜大小进行定量分析.
主要成果:
- 对于水下垃圾检测,YOLOv8的平均精度 (mAP) 为0.764,比之前的基准值有显著改善.
- 在海物体检测 (mAP 0.792) 和实例细分 (mAP 0.798) 中,YOLOv8表现出高性能.
- 该框架允许精确的像素级大小估计海黄瓜.
结论:
- AUAV-YOLOv8框架为浅水生态监测提供了高效和定量解决方案.
- 这种综合系统为传统的水下勘测平台提供了实用且具有成本效益的替代方案.
- 该研究强调了人工智能驱动的无人机在促进海洋研究和保护方面的潜力.
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