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相关实验视频

Updated: May 30, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

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一个基于深度学习的三维海洋塑料垃圾实时检测嵌入式系统.

Xiaoyu Yang1, Yunhao Chen1, Yuehai Zhou1

  • 1College of Ocean and Earth Sciences, Xiamen University, Xiamen 361005, China; National and Local Joint Engineering Research Center for Navigation and Location Service Technology, Xiamen University, Xiamen 361005, China.

Marine pollution bulletin
|January 31, 2025
PubMed
概括

这项研究引入了使用深度学习的实时海洋塑料垃圾检测系统. 该系统提高了水下图像质量,并优化了嵌入式设备的模型,提高了海洋环境保护的检测精度.

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科学领域:

  • 环境科学 环境科学
  • 计算机科学 计算机科学
  • 海洋生物学 海洋生物学

背景情况:

  • 海洋塑料污染对全球海洋生态系统构成重大威胁.
  • 开发有效的实时解决方案来检测和清除海洋塑料至关重要.

研究的目的:

  • 实施一个三维海洋塑料垃圾实时检测 (3D-MPLRD) 系统.
  • 提高深度学习模型的有效性,用于水下塑料检测.

主要方法:

  • 利用深度学习技术来检测海洋塑料垃圾.
  • 适用于水下条件的图像质量评估和增强.
  • 压缩和量化了用于嵌入式设备部署的YOLOv5模型.

主要成果:

  • 与在原始数据集上训练的模型相比,3D-MPLRD系统表现出更高的性能.
  • 实现了更高的精度,回忆,F1得分和平均精度.
  • 在嵌入式系统上成功部署了优化模型.

结论:

  • 开发的3D-MPLRD系统为海洋环境保护提供了一种有效的,基于情报的方法.
  • 该系统为未来的自动化海洋垃圾管理提供了基础参考.
关键词:
数据集优化数据集优化深度学习是一种深度学习.海洋塑料污染 海洋塑料污染对象检测检测对象检测对象检测水下图像增强水下图像增强

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  • 整合图像增强和模型优化是现实世界应用的关键.