用于海洋调查的珊瑚分割和分类:半监督机器学习方法

M. Johnson-Roberson, Suresh Kumar, S. Willams
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引用次数: 28

摘要

本文提出了一种结合视觉和声学数据对珊瑚进行自主分割和分类的技术。自主水下航行器(auv)促进了珊瑚礁多模态传感器信息的实时捕获。通过自主提取和鉴定某些感兴趣的珊瑚物种,可以帮助监测这些珊瑚礁的环境。该技术采用了两个阶段的分割和分类过程来收集AUV自主任务期间珊瑚密度的统计数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Segmentation and Classification of Coral for Oceanographic Surveys: A Semi-Supervised Machine Learning Approach
This work presents a technique for the autonomous segmentation and classification of coral through the combination of visual and acoustic data. Autonomous Underwater Vehicles (AUVs) facilitate the live capture of multi-modal sensor information about coral reefs. Environmental monitoring of these reefs can be aided though the autonomous extraction and identification of certain coral species of interest. The technique presented employs a two phase procedure of segmentation and classification to gather statistics about coral density during autonomous missions with an AUV.
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