|本期目录/Table of Contents|

[1]罗巍,陈曙东,王福涛,等.基于深度学习的大型食草动物种群监测方法[J].江苏农业科学,2020,48(20):247-255.
 Luo Wei,et al.Monitoring method of large herbivore population based on deep learning[J].Jiangsu Agricultural Sciences,2020,48(20):247-255.
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基于深度学习的大型食草动物种群监测方法(PDF)
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《江苏农业科学》[ISSN:1002-1302/CN:32-1214/S]

卷:
第48卷
期数:
2020年第20期
页码:
247-255
栏目:
农业工程与信息技术
出版日期:
2020-10-20

文章信息/Info

Title:
Monitoring method of large herbivore population based on deep learning
作者:
罗巍12陈曙东3王福涛4朱金峰4刘文亮4
1.北华航天工业学院,河北廊坊 065000; 2.河北省航天遥感信息处理与应用协同创新中心,河北廊坊 065000;
3.中国科学院微电子研究所智能制造电子研发中心,北京 100029;
4.中国科学院遥感与数字地球研究所人居环境遥感应用技术研究室,北京 100101
Author(s):
Luo Weiet al
关键词:
大型食草动物无人机遥感深度学习目标检测种群分布种群密度
Keywords:
-
分类号:
S127
DOI:
-
文献标志码:
A
摘要:
借助无人机遥感手段对分布在青海省玛多县的大型食草动物进行监测。监测对象包括家养藏羊、家养牦牛、马3种家养食草动物和藏原羚、藏野驴、岩羊3种野生食草动物。运用深度学习模型mask r-cnn对航拍影像中的各类大型食草动物进行检测和定位,得到的召回率、正确率、漏识别率均值分别为89%、98.4%、10.8%。通过提取Mask R-CNN检测过程中生成的掩膜获取动物的轮廓矢量,进而可以估算出各类大型食草动物的种群数量和分布信息。通过与青海省草原总站提供的家畜存栏信息对比,两者的差值百分比分别为家养藏羊7.5%,家养牦牛为8.8%,马为2.8%。
Abstract:
-

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备注/Memo

备注/Memo:
收稿日期:2019-12-03
基金项目:国家重点研发计划(编号:2017YFC0506501);国家自然科学基金面上项目(编号:41571504)。
作者简介:罗巍(1982—),男,甘肃兰州人,博士,讲师,主要从事地理信息系统、计算机视觉领域研究。Tel:(0316)2395892;E-mail:63424962@qq.com。
更新日期/Last Update: 2020-11-09