Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery

Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrat...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 24; H. 21; S. 7048
Hauptverfasser:	Al-Shimaysawee, Laith A. H., Finn, Anthony, Weber, Delene, Schebella, Morgan F., Brinkworth, Russell S. A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 31.10.2024 MDPI
Schlagworte:	Algorithms Animals Automation bio-inspired vision signal processing Cameras canopy cover effects Datasets Deep learning Drones Endangered & extinct species Equipment and supplies Eucalyptus Forests Image processing Image Processing, Computer-Assisted - methods Infrared Rays insect vision koala monitoring Marine mammals Natural resources Neural networks Neural Networks, Computer occlusion analysis Phascolarctidae - physiology Plantations Protection and preservation Technology application Wildlife conservation insect vision occlusion analysis canopy cover effects koala monitoring bio-inspired vision signal processing
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. In the case of koala detection in eucalyptus plantations, there is a risk to spotters during forestry operations. In addition, fatigue and tedium associated with the difficult and repetitive task of checking every tree means automated detection options are particularly desirable. However, obtaining high detection rates with minimal false alarms remains a challenging task, particularly when there is low contrast between the animals and their surroundings. Koalas are also small and often partially or fully occluded by canopy, tree stems, or branches, or the background is highly complex. Biologically inspired vision systems are known for their superior ability in suppressing clutter and enhancing the contrast of dim objects of interest against their surroundings. This paper introduces a biologically inspired detection algorithm to locate koalas in eucalyptus plantations and evaluates its performance against ten other detection techniques, including both image processing and neural-network-based approaches. The nature of koala occlusion by canopy cover in these plantations was also examined using a combination of simulated and real data. The results show that the biologically inspired approach significantly outperformed the competing neural-network- and computer-vision-based approaches by over 27%. The analysis of simulated and real data shows that koala occlusion by tree stems and canopy can have a significant impact on the potential detection of koalas, with koalas being fully occluded in up to 40% of images in which koalas were known to be present. Our analysis shows the koala’s heat signature is more likely to be occluded when it is close to the centre of the image (i.e., it is directly under a drone) and less likely to be occluded off the zenith. This has implications for flight considerations. This paper also describes a new accurate ground-truth dataset of aerial high-dynamic-range infrared imagery containing instances of koala heat signatures. This dataset is made publicly available to support the research community.
AbstractList	Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. In the case of koala detection in eucalyptus plantations, there is a risk to spotters during forestry operations. In addition, fatigue and tedium associated with the difficult and repetitive task of checking every tree means automated detection options are particularly desirable. However, obtaining high detection rates with minimal false alarms remains a challenging task, particularly when there is low contrast between the animals and their surroundings. Koalas are also small and often partially or fully occluded by canopy, tree stems, or branches, or the background is highly complex. Biologically inspired vision systems are known for their superior ability in suppressing clutter and enhancing the contrast of dim objects of interest against their surroundings. This paper introduces a biologically inspired detection algorithm to locate koalas in eucalyptus plantations and evaluates its performance against ten other detection techniques, including both image processing and neural-network-based approaches. The nature of koala occlusion by canopy cover in these plantations was also examined using a combination of simulated and real data. The results show that the biologically inspired approach significantly outperformed the competing neural-network- and computer-vision-based approaches by over 27%. The analysis of simulated and real data shows that koala occlusion by tree stems and canopy can have a significant impact on the potential detection of koalas, with koalas being fully occluded in up to 40% of images in which koalas were known to be present. Our analysis shows the koala’s heat signature is more likely to be occluded when it is close to the centre of the image (i.e., it is directly under a drone) and less likely to be occluded off the zenith. This has implications for flight considerations. This paper also describes a new accurate ground-truth dataset of aerial high-dynamic-range infrared imagery containing instances of koala heat signatures. This dataset is made publicly available to support the research community. Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas ( ). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. In the case of koala detection in eucalyptus plantations, there is a risk to spotters during forestry operations. In addition, fatigue and tedium associated with the difficult and repetitive task of checking every tree means automated detection options are particularly desirable. However, obtaining high detection rates with minimal false alarms remains a challenging task, particularly when there is low contrast between the animals and their surroundings. Koalas are also small and often partially or fully occluded by canopy, tree stems, or branches, or the background is highly complex. Biologically inspired vision systems are known for their superior ability in suppressing clutter and enhancing the contrast of dim objects of interest against their surroundings. This paper introduces a biologically inspired detection algorithm to locate koalas in eucalyptus plantations and evaluates its performance against ten other detection techniques, including both image processing and neural-network-based approaches. The nature of koala occlusion by canopy cover in these plantations was also examined using a combination of simulated and real data. The results show that the biologically inspired approach significantly outperformed the competing neural-network- and computer-vision-based approaches by over 27%. The analysis of simulated and real data shows that koala occlusion by tree stems and canopy can have a significant impact on the potential detection of koalas, with koalas being fully occluded in up to 40% of images in which koalas were known to be present. Our analysis shows the koala's heat signature is more likely to be occluded when it is close to the centre of the image (i.e., it is directly under a drone) and less likely to be occluded off the zenith. This has implications for flight considerations. This paper also describes a new accurate ground-truth dataset of aerial high-dynamic-range infrared imagery containing instances of koala heat signatures. This dataset is made publicly available to support the research community. Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. In the case of koala detection in eucalyptus plantations, there is a risk to spotters during forestry operations. In addition, fatigue and tedium associated with the difficult and repetitive task of checking every tree means automated detection options are particularly desirable. However, obtaining high detection rates with minimal false alarms remains a challenging task, particularly when there is low contrast between the animals and their surroundings. Koalas are also small and often partially or fully occluded by canopy, tree stems, or branches, or the background is highly complex. Biologically inspired vision systems are known for their superior ability in suppressing clutter and enhancing the contrast of dim objects of interest against their surroundings. This paper introduces a biologically inspired detection algorithm to locate koalas in eucalyptus plantations and evaluates its performance against ten other detection techniques, including both image processing and neural-network-based approaches. The nature of koala occlusion by canopy cover in these plantations was also examined using a combination of simulated and real data. The results show that the biologically inspired approach significantly outperformed the competing neural-network- and computer-vision-based approaches by over 27%. The analysis of simulated and real data shows that koala occlusion by tree stems and canopy can have a significant impact on the potential detection of koalas, with koalas being fully occluded in up to 40% of images in which koalas were known to be present. Our analysis shows the koala's heat signature is more likely to be occluded when it is close to the centre of the image (i.e., it is directly under a drone) and less likely to be occluded off the zenith. This has implications for flight considerations. This paper also describes a new accurate ground-truth dataset of aerial high-dynamic-range infrared imagery containing instances of koala heat signatures. This dataset is made publicly available to support the research community.Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. In the case of koala detection in eucalyptus plantations, there is a risk to spotters during forestry operations. In addition, fatigue and tedium associated with the difficult and repetitive task of checking every tree means automated detection options are particularly desirable. However, obtaining high detection rates with minimal false alarms remains a challenging task, particularly when there is low contrast between the animals and their surroundings. Koalas are also small and often partially or fully occluded by canopy, tree stems, or branches, or the background is highly complex. Biologically inspired vision systems are known for their superior ability in suppressing clutter and enhancing the contrast of dim objects of interest against their surroundings. This paper introduces a biologically inspired detection algorithm to locate koalas in eucalyptus plantations and evaluates its performance against ten other detection techniques, including both image processing and neural-network-based approaches. The nature of koala occlusion by canopy cover in these plantations was also examined using a combination of simulated and real data. The results show that the biologically inspired approach significantly outperformed the competing neural-network- and computer-vision-based approaches by over 27%. The analysis of simulated and real data shows that koala occlusion by tree stems and canopy can have a significant impact on the potential detection of koalas, with koalas being fully occluded in up to 40% of images in which koalas were known to be present. Our analysis shows the koala's heat signature is more likely to be occluded when it is close to the centre of the image (i.e., it is directly under a drone) and less likely to be occluded off the zenith. This has implications for flight considerations. This paper also describes a new accurate ground-truth dataset of aerial high-dynamic-range infrared imagery containing instances of koala heat signatures. This dataset is made publicly available to support the research community.
Audience	Academic
Author	Finn, Anthony Brinkworth, Russell S. A. Al-Shimaysawee, Laith A. H. Schebella, Morgan F. Weber, Delene
AuthorAffiliation	2 College of Science and Engineering, Flinders University, Tonsley, SA 5042, Australia; russell.brinkworth@flinders.edu.au 1 UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia; anthony.finn@unisa.edu.au (A.F.); delene.weber@unisa.edu.au (D.W.); morgan.schebella@unisa.edu.au (M.F.S.)
AuthorAffiliation_xml	– name: 2 College of Science and Engineering, Flinders University, Tonsley, SA 5042, Australia; russell.brinkworth@flinders.edu.au – name: 1 UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia; anthony.finn@unisa.edu.au (A.F.); delene.weber@unisa.edu.au (D.W.); morgan.schebella@unisa.edu.au (M.F.S.)
Author_xml	– sequence: 1 givenname: Laith A. H. orcidid: 0000-0002-6535-9300 surname: Al-Shimaysawee fullname: Al-Shimaysawee, Laith A. H. – sequence: 2 givenname: Anthony orcidid: 0000-0002-2690-0838 surname: Finn fullname: Finn, Anthony – sequence: 3 givenname: Delene orcidid: 0000-0002-8293-4960 surname: Weber fullname: Weber, Delene – sequence: 4 givenname: Morgan F. orcidid: 0000-0002-7436-438X surname: Schebella fullname: Schebella, Morgan F. – sequence: 5 givenname: Russell S. A. orcidid: 0000-0003-0270-3538 surname: Brinkworth fullname: Brinkworth, Russell S. A.
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/39517943$$D View this record in MEDLINE/PubMed
BookMark	eNpdkktv1DAQxyNURB9w4AugSFzKIcWv-HFCUSmwolIvcLbGzmTrVRIXJ6nUb4_DlqVFsjTWzG_-nhnPaXE0xhGL4i0lF5wb8nFiglFFhH5RnFDBRKUZI0dP7sfF6TTtCGGcc_2qOOampsoIflK4q3voF5hDHMvYlc0yxwFmbMsbt0M_V59xzmaNNv02pjDfDlPZxVR-j9BD-S8cxnIzdglSzm0wBejLzQBbTA-vi5cd9BO-ebRnxc8vVz8uv1XXN183l8115YU0cyVpawiSmsmOGqUd1kR7ZXydj3R1R1EBZUYCEll3mgmtqQevwEktHSX8rNjsddsIO3uXwgDpwUYI9o8jpq2FNAffo6WupihBgdBOGKmdq4lhQkhuEIliWevTXutucQO2Hsc5Qf9M9HlkDLd2G-8tpbXQkq4K548KKf5acJrtECaPfQ8jxmWynDKtBFNkRd__h-7iksY8q5WSmauZytTFntpC7iCMXcwP5wFAi0PweSG6kP2NzgUwqpXJCe-e9nAo_u_nZ-DDHvApTlPC7oBQYtfFsofF4r8B_Ny9Ng
Cites_doi	10.3390/drones4020020 10.3390/s120607587 10.1016/j.infrared.2019.06.003 10.3389/fncir.2012.00108 10.3390/s16010097 10.1016/S0042-6989(97)00105-3 10.3390/s18051474 10.1007/BF00224353 10.1371/journal.pcbi.1000555 10.1080/01431161.2018.1523580 10.5194/isprsarchives-XL-3-W3-355-2015 10.1002/ecs2.2194 10.1071/AM18006 10.1038/srep45127 10.1371/journal.pone.0002784 10.1109/DICTA47822.2019.8945846 10.1167/8.11.8 10.1090/psapm/040/1059485 10.1111/2041-210X.13581 10.1109/LGRS.2017.2772030 10.1109/ISSNIP.2011.6146617 10.1016/j.ecoinf.2023.102383 10.1109/ICCSPA.2013.6487251 10.1007/s00422-022-00950-9 10.1016/S0959-4388(02)00390-2 10.3390/rs14102432 10.1109/TNNLS.2023.3347633 10.1139/juvs-2016-0029 10.1016/j.biocon.2020.108598 10.1109/CVPRW.2014.40 10.1109/TGRS.2013.2242477 10.2352/ISSN.2470-1173.2020.8.IMAWM-085 10.3390/s140813778 10.1109/ISSNIP.2007.4496855 10.1016/j.bbrc.2020.09.052 10.1093/biosci/biab073 10.1109/ICRA.2014.6907094 10.1117/12.804351 10.1088/1748-3190/ac2988 10.1145/3372278.3390710 10.1109/CVPR.2017.690 10.1080/01431161.2017.1280639 10.1007/s10344-021-01549-4 10.3354/esr00877 10.3390/rs16061081 10.20944/preprints202009.0480.v2 10.1007/s10530-014-0784-0 10.1109/LGRS.2014.2323236 10.1071/WR18122 10.1371/journal.pone.0242204 10.1007/978-3-319-70090-8_53 10.1523/JNEUROSCI.1277-13.2013 10.1016/j.cois.2018.09.005 10.1071/AM20043 10.1109/CVPRW.2012.6238919 10.1002/wsb.629 10.1016/j.rse.2018.06.028 10.1016/j.isprsjprs.2020.08.026 10.1016/j.cub.2019.10.035 10.1016/j.rse.2017.08.026 10.1016/j.compag.2024.108713 10.1007/BF00203134 10.1071/WR20169 10.3390/rs16091535 10.1109/TAES.2015.140878 10.1038/s41598-019-39917-5 10.1080/01431161.2017.1390621 10.1111/mam.12046 10.1016/j.patcog.2016.04.002 10.3390/rs16081350 10.1109/DICTA47822.2019.8946002 10.1609/aaai.v32i1.11414 10.1098/rspb.2005.3424
ContentType	Journal Article
Copyright	COPYRIGHT 2024 MDPI AG 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2024 by the authors. 2024
Copyright_xml	– notice: COPYRIGHT 2024 MDPI AG – notice: 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2024 by the authors. 2024
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA
DOI	10.3390/s24217048
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database ProQuest One Academic ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	MEDLINE MEDLINE - Academic CrossRef Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1424-8220
ExternalDocumentID	oai_doaj_org_article_1b51e6a7a48b4968bb509244639ee072 PMC11548612 A815421879 39517943 10_3390_s24217048
Genre	Journal Article
GrantInformation_xml	– fundername: the University of South Australia (UniSA), and the National 684 Institute for Forest Products Innovation (NIFPI): grantid: NIF082-1819 – fundername: University of South Australia (UniSA); National Institute for Forest Products Innovation (NIFPI) grantid: NIF082-1819 – fundername: University of South Australia President’s Scholarship and Research Training Program
GroupedDBID	--- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. ABJCF ALIPV ARAPS CGR CUY CVF ECM EIF HCIFZ KB. M7S NPM PDBOC 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 PUEGO 5PM
ID	FETCH-LOGICAL-c469t-61d90e0526f1978be508c79c59c56b5f1e7a1296ae065f824881cac7ab686b103
IEDL.DBID	DOA
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001351074000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1424-8220
IngestDate	Mon Nov 10 04:31:32 EST 2025 Tue Nov 04 02:05:29 EST 2025 Fri Sep 05 09:25:31 EDT 2025 Tue Oct 07 07:35:57 EDT 2025 Tue Nov 04 18:13:25 EST 2025 Wed Feb 19 02:03:09 EST 2025 Sat Nov 29 07:09:51 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	21
Keywords	insect vision occlusion analysis canopy cover effects koala monitoring bio-inspired vision signal processing
Language	English
License	Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c469t-61d90e0526f1978be508c79c59c56b5f1e7a1296ae065f824881cac7ab686b103
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-7436-438X 0000-0003-0270-3538 0000-0002-2690-0838 0000-0002-6535-9300 0000-0002-8293-4960
OpenAccessLink	https://doaj.org/article/1b51e6a7a48b4968bb509244639ee072
PMID	39517943
PQID	3126287527
PQPubID	2032333
ParticipantIDs	doaj_primary_oai_doaj_org_article_1b51e6a7a48b4968bb509244639ee072 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11548612 proquest_miscellaneous_3128742702 proquest_journals_3126287527 gale_infotracacademiconefile_A815421879 pubmed_primary_39517943 crossref_primary_10_3390_s24217048
PublicationCentury	2000
PublicationDate	20241031
PublicationDateYYYYMMDD	2024-10-31
PublicationDate_xml	– month: 10 year: 2024 text: 20241031 day: 31
PublicationDecade	2020
PublicationPlace	Switzerland
PublicationPlace_xml	– name: Switzerland – name: Basel
PublicationTitle	Sensors (Basel, Switzerland)
PublicationTitleAlternate	Sensors (Basel)
PublicationYear	2024
Publisher	MDPI AG MDPI
Publisher_xml	– name: MDPI AG – name: MDPI
References	Peng (ref_83) 2020; 169 Rey (ref_30) 2017; 200 Focardi (ref_10) 2001; 29 ref_14 ref_58 ref_57 ref_56 ref_55 ref_54 Qi (ref_87) 2019; Volume 10843 ref_53 ref_52 Beranek (ref_20) 2020; 43 ref_51 Lyu (ref_15) 2024; 79 Srinivasan (ref_44) 2021; 564 ref_59 Horn (ref_76) 1990; 40 Hanson (ref_6) 2015; 17 Longmore (ref_26) 2017; 38 Steen (ref_25) 2012; 12 Petso (ref_35) 2022; 68 Kassim (ref_23) 2020; 2020 Griffiths (ref_50) 2019; 11159 Shi (ref_79) 2017; 15 Christiansen (ref_24) 2014; 14 ref_61 ref_60 Finn (ref_74) 2022; 116 Baxter (ref_90) 2018; 9 ref_69 ref_68 ref_21 Ocholla (ref_12) 2024; 33 ref_62 Brinkworth (ref_63) 2008; 8 ref_29 ref_28 Kellenberger (ref_1) 2018; 216 Serres (ref_48) 2018; 30 Wilmott (ref_8) 2019; 41 Gooday (ref_17) 2018; 6 (ref_73) 2006; 273 Egelhaaf (ref_43) 2002; 12 ref_70 Corcoran (ref_31) 2021; 12 Barrios (ref_13) 2024; 218 ref_36 ref_34 Lethbridge (ref_11) 2019; 46 ref_33 ref_75 Magrath (ref_32) 2021; 71 Aghaziyarati (ref_78) 2019; 101 Witczuk (ref_16) 2018; 39 Jansonius (ref_71) 1991; 168 Sudholz (ref_22) 2021; 49 ref_39 Rees (ref_18) 2018; 35 ref_38 ref_37 Sanders (ref_4) 2014; 116 Drews (ref_66) 2020; 30 Kays (ref_19) 2019; 40 (ref_27) 2016; 40 Nguyen (ref_42) 2020; 2020 Han (ref_80) 2014; 11 Linchant (ref_2) 2015; 45 ref_47 ref_46 ref_45 Chen (ref_81) 2013; 52 ref_89 ref_88 (ref_65) 1997; 37 ref_86 ref_41 ref_85 ref_40 ref_84 ref_3 ref_49 Srinivasan (ref_67) 1990; 240 ref_9 (ref_64) 1992; 68 Wiederman (ref_72) 2013; 33 Wei (ref_82) 2016; 58 ref_5 ref_7 Deng (ref_77) 2016; 52
References_xml	– ident: ref_14 doi: 10.3390/drones4020020 – volume: 12 start-page: 7587 year: 2012 ident: ref_25 article-title: Automatic detection of animals in mowing operations using thermal cameras publication-title: Sensors doi: 10.3390/s120607587 – volume: 101 start-page: 78 year: 2019 ident: ref_78 article-title: Small infrared target detection using absolute average difference weighted by cumulative directional derivatives publication-title: Infrared Phys. Technol. doi: 10.1016/j.infrared.2019.06.003 – ident: ref_9 – volume: 33 start-page: 101139 year: 2024 ident: ref_12 article-title: Livestock detection in African rangelands: Potential of high-resolution remote sensing data publication-title: Remote Sens. Appl. Soc. Environ. – volume: 29 start-page: 133 year: 2001 ident: ref_10 article-title: Comparative evaluation of thermal infrared imaging and spotlighting to survey wildlife publication-title: Wildl. Soc. Bull. – ident: ref_45 doi: 10.3389/fncir.2012.00108 – ident: ref_55 doi: 10.3390/s16010097 – volume: 37 start-page: 3407 year: 1997 ident: ref_65 article-title: Processing of natural time series of intensities by the visual system of the blowfly publication-title: Vis. Res. doi: 10.1016/S0042-6989(97)00105-3 – ident: ref_3 doi: 10.3390/s18051474 – volume: 168 start-page: 631 year: 1991 ident: ref_71 article-title: Fast temporal adaptation of on-off units in the first optic chiasm of the blowfly publication-title: J. Comp. Physiol. A doi: 10.1007/BF00224353 – ident: ref_49 doi: 10.1371/journal.pcbi.1000555 – volume: 40 start-page: 407 year: 2019 ident: ref_19 article-title: Hot monkey, cold reality: Surveying rainforest canopy mammals using drone-mounted thermal infrared sensors publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2018.1523580 – ident: ref_61 – volume: 116 start-page: 371 year: 2014 ident: ref_4 article-title: Acoustic monitoring of nocturnally migrating birds accurately assesses the timing and magnitude of migration through the Great Lakes publication-title: Condor Ornithol. Appl. – ident: ref_28 doi: 10.5194/isprsarchives-XL-3-W3-355-2015 – volume: 9 start-page: e02194 year: 2018 ident: ref_90 article-title: Learning to fly: Integrating spatial ecology with unmanned aerial vehicle surveys publication-title: Ecosphere doi: 10.1002/ecs2.2194 – volume: 41 start-page: 157 year: 2019 ident: ref_8 article-title: Are koalas detected more effectively by systematic spotlighting or diurnal searches? publication-title: Aust. Mammal. doi: 10.1071/AM18006 – ident: ref_29 doi: 10.1038/srep45127 – ident: ref_70 doi: 10.1371/journal.pone.0002784 – ident: ref_62 doi: 10.1109/DICTA47822.2019.8945846 – volume: 8 start-page: 8 year: 2008 ident: ref_63 article-title: Photoreceptor processing improves salience facilitating small target detection in cluttered scenes publication-title: J. Vis. doi: 10.1167/8.11.8 – volume: Volume 10843 start-page: 108430M year: 2019 ident: ref_87 article-title: Evaluation of real-time object detection model based on small targets publication-title: Proceedings of the 9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging – volume: 40 start-page: 87 year: 1990 ident: ref_76 article-title: The hadamard product publication-title: Proc. Symp. Appl. Math. doi: 10.1090/psapm/040/1059485 – volume: 12 start-page: 1103 year: 2021 ident: ref_31 article-title: Automated detection of wildlife using drones: Synthesis, opportunities and constraints publication-title: Methods Ecol. Evol. doi: 10.1111/2041-210X.13581 – volume: 15 start-page: 33 year: 2017 ident: ref_79 article-title: High-boost-based multiscale local contrast measure for infrared small target detection publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2017.2772030 – ident: ref_46 doi: 10.1109/ISSNIP.2011.6146617 – volume: 79 start-page: 102383 year: 2024 ident: ref_15 article-title: Deer survey from drone thermal imagery using enhanced faster R-CNN based on ResNets and FPN publication-title: Ecol. Inform. doi: 10.1016/j.ecoinf.2023.102383 – ident: ref_52 doi: 10.1109/ICCSPA.2013.6487251 – volume: 116 start-page: 661 year: 2022 ident: ref_74 article-title: Exploration of motion inhibition for the suppression of false positives in biologically inspired small target detection algorithms from a moving platform publication-title: Biol. Cybern. doi: 10.1007/s00422-022-00950-9 – volume: 240 start-page: 279 year: 1990 ident: ref_67 article-title: Matched filtering in the visual system of the fly: Large monopolar cells of the lamina are optimized to detect moving edges and blobs publication-title: Proc. R. Soc. B Biol. Sci. – volume: 12 start-page: 699 year: 2002 ident: ref_43 article-title: Vision in flying insects publication-title: Curr. Opin. Neurobiol. doi: 10.1016/S0959-4388(02)00390-2 – ident: ref_58 doi: 10.3390/rs14102432 – ident: ref_34 doi: 10.1109/TNNLS.2023.3347633 – volume: 6 start-page: 100 year: 2018 ident: ref_17 article-title: An assessment of thermal-image acquisition with an unmanned aerial vehicle (UAV) for direct counts of coastal marine mammals ashore publication-title: J. Unmanned Veh. Syst. doi: 10.1139/juvs-2016-0029 – ident: ref_7 doi: 10.1016/j.biocon.2020.108598 – ident: ref_59 – ident: ref_37 doi: 10.1109/CVPRW.2014.40 – volume: 52 start-page: 574 year: 2013 ident: ref_81 article-title: A local contrast method for small infrared target detection publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2013.2242477 – ident: ref_53 – volume: 2020 start-page: 85-1 year: 2020 ident: ref_23 article-title: Small object bird detection in infrared drone videos using mask R-CNN deep learning publication-title: Electron. Imaging doi: 10.2352/ISSN.2470-1173.2020.8.IMAWM-085 – volume: 14 start-page: 13778 year: 2014 ident: ref_24 article-title: Automated detection and recognition of wildlife using thermal cameras publication-title: Sensors doi: 10.3390/s140813778 – ident: ref_69 doi: 10.1109/ISSNIP.2007.4496855 – volume: 564 start-page: 4 year: 2021 ident: ref_44 article-title: Vision, perception, navigation and ‘cognition’in honeybees and applications to aerial robotics publication-title: Biochem. Biophys. Res. Commun. doi: 10.1016/j.bbrc.2020.09.052 – volume: 71 start-page: 1038 year: 2021 ident: ref_32 article-title: A comprehensive overview of technologies for species and habitat monitoring and conservation publication-title: BioScience doi: 10.1093/biosci/biab073 – ident: ref_36 doi: 10.1109/ICRA.2014.6907094 – ident: ref_47 doi: 10.1117/12.804351 – ident: ref_68 doi: 10.1088/1748-3190/ac2988 – ident: ref_84 doi: 10.1145/3372278.3390710 – ident: ref_40 – ident: ref_41 doi: 10.1109/CVPR.2017.690 – volume: 38 start-page: 2623 year: 2017 ident: ref_26 article-title: Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2017.1280639 – volume: 68 start-page: 3 year: 2022 ident: ref_35 article-title: Review on methods used for wildlife species and individual identification publication-title: Eur. J. Wildl. Res. doi: 10.1007/s10344-021-01549-4 – volume: 35 start-page: 81 year: 2018 ident: ref_18 article-title: The potential of unmanned aerial systems for sea turtle research and conservation: A review and future directions publication-title: Endanger. Species Res. doi: 10.3354/esr00877 – ident: ref_56 doi: 10.3390/rs16061081 – ident: ref_5 doi: 10.20944/preprints202009.0480.v2 – volume: 17 start-page: 977 year: 2015 ident: ref_6 article-title: Feral cat eradication in the presence of endemic San Nicolas Island foxes publication-title: Biol. Invasions doi: 10.1007/s10530-014-0784-0 – volume: 11 start-page: 2168 year: 2014 ident: ref_80 article-title: A robust infrared small target detection algorithm based on human visual system publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2014.2323236 – volume: 46 start-page: 639 year: 2019 ident: ref_11 article-title: Estimating kangaroo density by aerial survey: A comparison of thermal cameras with human observers publication-title: Wildl. Res. doi: 10.1071/WR18122 – ident: ref_21 doi: 10.1371/journal.pone.0242204 – ident: ref_86 doi: 10.1007/978-3-319-70090-8_53 – volume: 33 start-page: 13225 year: 2013 ident: ref_72 article-title: Correlation between OFF and ON channels underlies dark target selectivity in an insect visual system publication-title: J. Neurosci. doi: 10.1523/JNEUROSCI.1277-13.2013 – volume: 30 start-page: 46 year: 2018 ident: ref_48 article-title: Insect-inspired vision for autonomous vehicles publication-title: Curr. Opin. Insect Sci. doi: 10.1016/j.cois.2018.09.005 – ident: ref_75 – volume: 43 start-page: 260 year: 2020 ident: ref_20 article-title: Trialling a real-time drone detection and validation protocol for the koala (Phascolarctos cinereus) publication-title: Aust. Mammal. doi: 10.1071/AM20043 – ident: ref_88 doi: 10.1109/CVPRW.2012.6238919 – volume: 40 start-page: 181 year: 2016 ident: ref_27 article-title: Visible and thermal infrared remote sensing for the detection of white-tailed deer using an unmanned aerial system publication-title: Wildl. Soc. Bull. doi: 10.1002/wsb.629 – volume: 216 start-page: 139 year: 2018 ident: ref_1 article-title: Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.06.028 – ident: ref_85 – volume: 169 start-page: 364 year: 2020 ident: ref_83 article-title: Wild animal survey using UAS imagery and deep learning: Modified Faster R-CNN for kiang detection in Tibetan Plateau publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2020.08.026 – volume: 30 start-page: 209 year: 2020 ident: ref_66 article-title: Dynamic signal compression for robust motion vision in flies publication-title: Curr. Biol. doi: 10.1016/j.cub.2019.10.035 – volume: 200 start-page: 341 year: 2017 ident: ref_30 article-title: Detecting animals in African Savanna with UAVs and the crowds publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.08.026 – volume: 218 start-page: 108713 year: 2024 ident: ref_13 article-title: Monitoring mammalian herbivores via convolutional neural networks implemented on thermal UAV imagery publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2024.108713 – ident: ref_89 – volume: 68 start-page: 23 year: 1992 ident: ref_64 article-title: A theory of maximizing sensory information publication-title: Biol. Cybern. doi: 10.1007/BF00203134 – volume: 49 start-page: 46 year: 2021 ident: ref_22 article-title: A comparison of manual and automated detection of Rusa deer (Rusa timorensis) from RPAS-derived thermal imagery publication-title: Wildl. Res. doi: 10.1071/WR20169 – ident: ref_54 doi: 10.3390/rs16091535 – volume: 52 start-page: 60 year: 2016 ident: ref_77 article-title: Infrared small-target detection using multiscale gray difference weighted image entropy publication-title: IEEE Trans. Aerosp. Electron. Syst. doi: 10.1109/TAES.2015.140878 – ident: ref_39 doi: 10.1038/s41598-019-39917-5 – volume: 39 start-page: 5504 year: 2018 ident: ref_16 article-title: Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests-preliminary results publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2017.1390621 – ident: ref_60 – volume: 45 start-page: 239 year: 2015 ident: ref_2 article-title: Are unmanned aircraft systems (UAS s) the future of wildlife monitoring? A review of accomplishments and challenges publication-title: Mammal Rev. doi: 10.1111/mam.12046 – volume: 11159 start-page: 111590V year: 2019 ident: ref_50 article-title: Pixel-wise infrared tone remapping for rapid adaptation to high dynamic range variations publication-title: Electro-Opt. Infrared Syst. Technol. Appl. XVI – volume: 58 start-page: 216 year: 2016 ident: ref_82 article-title: Multiscale patch-based contrast measure for small infrared target detection publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2016.04.002 – ident: ref_57 – ident: ref_33 doi: 10.3390/rs16081350 – ident: ref_51 doi: 10.1109/DICTA47822.2019.8946002 – ident: ref_38 doi: 10.1609/aaai.v32i1.11414 – volume: 2020 start-page: 3189691 year: 2020 ident: ref_42 article-title: An Evaluation of Deep Learning Methods for Small Object Detection publication-title: J. Electr. Comput. Eng. – volume: 273 start-page: 1211 year: 2006 ident: ref_73 article-title: Small object detection neurons in female hoverflies publication-title: Proc. R. Soc. B Biol. Sci. doi: 10.1098/rspb.2005.3424
SSID	ssj0023338
Score	2.439317
Snippet	Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex...
SourceID	doaj pubmedcentral proquest gale pubmed crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database
StartPage	7048
SubjectTerms	Algorithms Animals Automation bio-inspired vision signal processing Cameras canopy cover effects Datasets Deep learning Drones Endangered & extinct species Equipment and supplies Eucalyptus Forests Image processing Image Processing, Computer-Assisted - methods Infrared Rays insect vision koala monitoring Marine mammals Natural resources Neural networks Neural Networks, Computer occlusion analysis Phascolarctidae - physiology Plantations Protection and preservation Technology application Wildlife conservation
SummonAdditionalLinks	– databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3di9QwEB90zwd98Pu0ekoUwadybZo2yZPs6R4ewnqIwr2VJDu5W7hrz21X8L930nZ7uwi-CH1qAp0yH_lNMvkNwDuuJZIf8VhzI2Jh0Mfa-yKWLuUuE4LMpG82IedzdXamT4cNt2Yoq9zExC5QL2oX9sgPs5QXhO5zLj9c_4xD16hwujq00LgNe4GpTExg72g2P_02plwZZWA9n1BGyf1hEw5AZRKa_WytQh1Z_98heWtN2q2X3FqAjh_8r-gP4f4APdm0t5VHcAurx3Bvi5DwCdjZSP7Nas-m67YmRIsL9tWG_Zr4E7Zd6VbFppfn9In24qphBHvZl9pcGnYzvKzYSeVXobqdTTsjZydXgS7j91P4cTz7_vFzPHRhiB2lzi3llgudYKCF8SmlnBYJ0jmpXU5PYXOfojQEGgqDhGa84hQRUmecNLZQhU2TbB8mVV3hc2CLRFNCohC5dsIYp7xJuBOokgKllxjB241WyuuebKOkJCWorhxVF8FR0Nc4IfBjdy_q1Xk5uFuZ2jzFwkgjlBW6UNYSMCIkQ3gMMZE8gvdB22XwYlIpydtfRiA5Ax9WOVUELXnoxB7BwUap5eDeTXmj0QjejMPkmOG0xVRYr7s5Sopw3S-CZ739jDJnOu-Y-SJQO5a181O7I9XyoiP_DvRJimDpi3_L9RLucoJf_Sp7AJN2tcZXcMf9apfN6vXgJn8AnHYdOQ priority: 102 providerName: ProQuest
Title	Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery
URI	https://www.ncbi.nlm.nih.gov/pubmed/39517943 https://www.proquest.com/docview/3126287527 https://www.proquest.com/docview/3128742702 https://pubmed.ncbi.nlm.nih.gov/PMC11548612 https://doaj.org/article/1b51e6a7a48b4968bb509244639ee072
Volume	24
WOSCitedRecordID	wos001351074000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nj9MwEB3BwgEOiG8CS2UQEqdoE-fD9jELXVGhLRUCqZwixx3vVtpNUJsiceG3M3bSkIoDF6Qoh9gHZ8bjeS923gC84UogxREPFddpmGq0obI2D4WJuUnSlKZJV2xCzOdyuVSLUakvdyaskwfuDHcSV1mMuRY6lVWqcllVlOIoJ1FmRYyEX30J9ezJVE-1EmJenY5QQqT-ZOs2PkXkivyMso8X6f97KR7losNzkqPEc3Yf7vWIkRXdSB_ADawfwt2RjuAjqKaDZjdrLCt2bUNAFFfsU-U-s4TvsfUnrmpWXF00m3V7eb1lhFbZx0ZfafaneV2zWW037lA6K_zcZLNrp3Lx8zF8PZt-efch7IsnhIYYb0uUcKUidGouNiamWCEhMSOUyejKq8zGKDTl-lwjgRArOQVybLQRusplXsVR8gSO6qbGZ8BWkSIeIRG5MqnWRlodcZOijHIUVmAAr_dGLb93GhklcQtn-XKwfACnztxDBydr7R-Qs8ve2eW_nB3AW-es0gUfeYTG2_1DQON0MlZlIQkRcldAPYDjvT_LPiq3ZRLznBhixkUAr4Zmiie3SaJrbHa-jxSp-0svgKed-4cxJyrzgnoByIOJcfBShy31-tJrdjvVI0lo8vn_MMMLuMMJW3Up9BiO2s0OX8Jt86NdbzcTuCmWwt_lBG6dTueLzxMfHXQ__zWlZ4vZ-eLbb5NvExw
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9NAEB2VFAk48P1hKLAgECer9trx7h4QCrRVo7ShhyK1J7PejNtIrV0SB9Q_xW9kxk7cREjcekDyyWtZu_bbmTe7s28A3kmjkOaR9I20sR9bzH2T54mvXChdFMcEk6bYhBoO9dGROViD34uzMJxWubCJtaEelY7XyDejUCbE7rtSfbr44XPVKN5dXZTQaGAxwMtfFLJNP_a36P--l3Jn-_DLrj-vKuA7CgUripVGJkCWOclDCqEyJIrilHFdupKsm4eoLDnBxCJ551xLQnjorFM2S3SShUFE770B6zwK3YH1g_7-wXEb4kUU8TX6RVFkgs0pb7iqgIsLLXm9ujjA3y5gyQeu5mcuObyde__bp7oPd-fUWvSaufAA1rB4CHeWBBcfQbbdipuLMhe9WVUSY8eR-JrxepS_hVWdmlaI3tkJDak6PZ8KovViUNozK66ax4XoF_mEs_dFr57Eon_OciCXj-HbtQzyCXSKssBnIEaBoYBLI0rjYmudzm0gXYw6SFDlCj14u0BBetGIiaQUhDFU0hYqHnxmfLQPsP53faOcnKRzc5KGWTfExCob6yw2ic4yIn7E1IhvIgZKevCB0ZWylSIIUX-bwxbUT9b7SnuaqLPkSvMebCxAlM7N1zS9QpAHb9pmMjy8m2QLLGf1M1rFfJzRg6cNXts-R6ZbKw96oFeQvDKo1ZZifFqLm7M8lCba_fzf_XoNt3YP9_fSvf5w8AJuS6KaDaPYgE41meFLuOl-VuPp5NV8igr4ft1Q_wO9_nig
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9NAEB2VFiE48P1hKLAgECer9trx7h4QCqQRUSDkAFJ7MuvNbBuptUvsgPrX-HXM2o6bCIlbD0g-ea1o13kz88Y7-wbgFVcCyY64r7iO_Vij9ZW1iS9MyE0UxwSTptmEmEzkwYGabsHv1VkYV1a58om1o54Vxn0j34tCnhC771GqbtuyiOlg-O7sh-86SLmd1lU7jQYiYzz_Relb-XY0oP_6NefD_a8fPvpthwHfUFpYUd40UwE6yRMbUjqVIdEVI5Tp0ZVkPRui0BQQE40Uqa3khPbQaCN0lsgkC4OIfvcK7BAlj8nGdqajz9PDLt2LKPtrtIyiSAV7pdt8FYFrNLQWAetGAX-Hg7V4uFmruRb8hrf-59d2G262lJv1Gxu5A1uY34Uba0KM9yDb70TPWWFZf1kVxORxxr5k7juVP8CqLlnLWf_kiJZUHZ-WjOg-Gxf6RLOL4XnORrlduKp-1q-Nm41OnUzI-X34dimLfADbeZHjI2CzQFEiJhG5MrHWRlodcBOjDBIUVqAHL1eISM8akZGUkjMHm7SDjQfvHVa6B5wueH2jWBylrZtJw6wXYqKFjmUWq0RmGRFCYnDEQxEDwT1445CWOu9FcKL5NocwaJ5OByztS6LU3HWg92B3Bai0dWtleoEmD150w-SQ3C6TzrFY1s9IEbtjjh48bLDbzTlSvVqR0AO5geqNRW2O5PPjWvTcyUZJouOP_z2v53CN8J1-Gk3GT-A6JwbaEI1d2K4WS3wKV83Pal4unrXWyuD7ZSP9D6P-gWA
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Evaluation+of+Automated+Object-Detection+Algorithms+for+Koala+Detection+in+Infrared+Aerial+Imagery&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Al-Shimaysawee%2C+Laith+A+H&rft.au=Finn%2C+Anthony&rft.au=Weber%2C+Delene&rft.au=Schebella%2C+Morgan+F&rft.date=2024-10-31&rft.eissn=1424-8220&rft.volume=24&rft.issue=21&rft_id=info:doi/10.3390%2Fs24217048&rft_id=info%3Apmid%2F39517943&rft.externalDocID=39517943
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon