• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

加热诱饵以模拟无人机可探测到的活体动物的热信号。

Heating decoys to mimic thermal signatures of live animals for drones.

作者信息

Jones Landon R, Mensah Cerise, Elmore Jared A, Evans Kristine O, Pfeiffer Morgan B, Blackwell Bradley F, Iglay Raymond B

机构信息

Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Mississippi State, MS 39762, USA.

Museum of Natural Science, Mississippi Department of Wildlife, Fisheries, and Parks, Jackson, MS 39202, USA.

出版信息

MethodsX. 2024 Aug 30;13:102933. doi: 10.1016/j.mex.2024.102933. eCollection 2024 Dec.

DOI:10.1016/j.mex.2024.102933
PMID:39286441
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11404203/
Abstract

Thermal sensors mounted on drones (unoccupied aircraft systems) are popular and effective tools for monitoring cryptic animal species, although few studies have quantified sampling error of animal counts from thermal images. Using decoys is one effective strategy to quantify bias and count accuracy; however, plastic decoys do not mimic thermal signatures of representative species. Our objective was to produce heat signatures in animal decoys to realistically match thermal images of live animals obtained from a drone-based sensor. We tested commercially available methods to heat plastic decoys of three different size classes, including chemical foot warmers, manually heated water, electric socks, pad, or blanket, and mini and small electric space heaters. We used criteria in two categories, 1) external temperature differences from ambient temperatures (ambient difference) and 2) color bins from a palette in thermal images obtained from a drone near the ground and in the air, to determine if heated decoys adequately matched respective live animals in four body regions. Three methods achieved similar thermal signatures to live animals for three to four body regions in external temperatures and predominantly matched the corresponding yellow color bins in thermal drone images from the ground and in the air. Pigeon decoys were best and most consistently heated with three-foot warmers. Goose and deer decoys were best heated by mini and small space heaters, respectively, in their body cavities, with a heated sock in the head of the goose decoy. The materials and equipment for our best heating methods were relatively inexpensive, commercially available items that provide sustained heat and could be adapted to various shapes and sizes for a wide range of avian and mammalian species. Our heating methods could be used in future studies to quantify bias and validate methodologies for drone surveys of animals with thermal sensors.•We determined optimal heating methods for plastic animal decoys with inexpensive and commercially available equipment to mimic thermal signatures of live animals.•Methods could be used to quantify bias and improve thermal surveys of animals with drones in future studies.

摘要

安装在无人机(无人飞行器系统)上的热传感器是监测隐秘动物物种的常用且有效的工具,尽管很少有研究对热图像中动物数量的采样误差进行量化。使用诱饵是量化偏差和计数准确性的一种有效策略;然而,塑料诱饵无法模拟代表性物种的热信号。我们的目标是在动物诱饵中产生热信号,使其与从基于无人机的传感器获取的活体动物热图像真实匹配。我们测试了市售的加热三种不同尺寸塑料诱饵的方法,包括化学暖脚器、手动加热水、电袜子、垫子或毯子,以及小型和微型电暖器。我们使用两类标准来确定加热后的诱饵在四个身体部位是否与相应的活体动物充分匹配,这两类标准分别是:1)与环境温度的外部温差(环境温差),以及2)从地面和空中靠近无人机获取的热图像调色板中的颜色区间。三种方法在外部温度下,能使三到四个身体部位的热信号与活体动物相似,并且在地面和空中的热无人机图像中,主要与相应的黄色颜色区间匹配。用三个暖脚器加热鸽子诱饵效果最佳且最稳定。鹅和鹿的诱饵分别在其体腔内用微型和小型电暖器加热效果最佳,鹅诱饵头部用加热袜子加热。我们最佳加热方法所需的材料和设备相对便宜,是可提供持续热量的市售物品,并且可以根据各种鸟类和哺乳动物的不同形状和大小进行调整。我们的加热方法可用于未来的研究,以量化偏差并验证使用热传感器对动物进行无人机调查的方法。

•我们用便宜且市售的设备确定了塑料动物诱饵的最佳加热方法,以模拟活体动物的热信号。

•这些方法可用于量化偏差,并在未来的研究中改进对动物的无人机热成像调查。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b023/11404203/23f15e8b4c32/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b023/11404203/3157d7930ff1/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b023/11404203/6468d39d1f0a/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b023/11404203/23f15e8b4c32/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b023/11404203/3157d7930ff1/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b023/11404203/6468d39d1f0a/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b023/11404203/23f15e8b4c32/gr2.jpg

相似文献

1
Heating decoys to mimic thermal signatures of live animals for drones.加热诱饵以模拟无人机可探测到的活体动物的热信号。
MethodsX. 2024 Aug 30;13:102933. doi: 10.1016/j.mex.2024.102933. eCollection 2024 Dec.
2
Evidence on the efficacy of small unoccupied aircraft systems (UAS) as a survey tool for North American terrestrial, vertebrate animals: a systematic map.关于小型无人航空器系统(UAS)作为北美陆地脊椎动物调查工具的功效的证据:一项系统综述。
Environ Evid. 2023 Feb 13;12(1):3. doi: 10.1186/s13750-022-00294-8.
3
Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys.可见光和热图像融合可提高无人机调查中动物自动检测和分类的精度。
Sci Rep. 2023 Jun 27;13(1):10385. doi: 10.1038/s41598-023-37295-7.
4
A dataset for multi-sensor drone detection.一个用于多传感器无人机检测的数据集。
Data Brief. 2021 Oct 27;39:107521. doi: 10.1016/j.dib.2021.107521. eCollection 2021 Dec.
5
Aerial Wildlife Image Repository for animal monitoring with drones in the age of artificial intelligence.人工智能时代,用于无人机动物监测的空中野生动物图像库。
Database (Oxford). 2024 Jul 23;2024. doi: 10.1093/database/baae070.
6
On the move: Influence of animal movements on count error during drone surveys.动态:无人机调查期间动物移动对计数误差的影响。
Ecol Evol. 2024 Sep 29;14(10):e70287. doi: 10.1002/ece3.70287. eCollection 2024 Oct.
7
Remotely Piloted Aircraft System (RPAS)-Based Wildlife Detection: A Review and Case Studies in Maritime Antarctica.基于遥控飞机系统(RPAS)的野生动物探测:南极洲海洋地区的综述与案例研究
Animals (Basel). 2020 Dec 14;10(12):2387. doi: 10.3390/ani10122387.
8
Thermal support for the very-low-birth-weight infant: role of supplemental conductive heat.
J Pediatr. 1984 Nov;105(5):810-4. doi: 10.1016/s0022-3476(84)80312-1.
9
Drone images afford more detections of marine wildlife than real-time observers during simultaneous large-scale surveys.无人机图像在同时进行的大规模调查中比实时观察员提供了更多的海洋野生动物检测。
PeerJ. 2023 Nov 3;11:e16186. doi: 10.7717/peerj.16186. eCollection 2023.
10
U-Space and UTM Deployment as an Opportunity for More Complex UAV Operations Including UAV Medical Transport.将通用空间(U-Space)和通用交通管理(UTM)部署作为开展包括无人机医疗运输在内的更复杂无人机操作的契机。
J Intell Robot Syst. 2022;106(1):12. doi: 10.1007/s10846-022-01681-6. Epub 2022 Aug 24.

本文引用的文献

1
Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys.可见光和热图像融合可提高无人机调查中动物自动检测和分类的精度。
Sci Rep. 2023 Jun 27;13(1):10385. doi: 10.1038/s41598-023-37295-7.
2
Automated detection of koalas using low-level aerial surveillance and machine learning.利用低空航拍监测和机器学习自动检测考拉。
Sci Rep. 2019 Mar 1;9(1):3208. doi: 10.1038/s41598-019-39917-5.
3
Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery.
利用无人机系统(UAS)和热成像技术自动检测和计数海洋野生动物。
Sci Rep. 2017 Mar 24;7:45127. doi: 10.1038/srep45127.
4
A multifactorial study of variation in interclutch interval and annual reproductive success in the feral pigeon,Columba livia.一项关于野鸽(Columba livia)间卵间隔和年度繁殖成功率变化的多因素研究。
Oecologia. 1989 Mar;80(1):87-92. doi: 10.1007/BF00789936. Epub 2013 Mar 13.