{"id":"https://openalex.org/W7164200400","doi":"https://doi.org/10.48550/arxiv.2606.10940","title":"Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals","display_name":"Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164200400","doi":"https://doi.org/10.48550/arxiv.2606.10940"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.10940","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10940","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.10940","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015808667","display_name":"Paul Fergus","orcid":"https://orcid.org/0000-0002-7070-4447"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fergus, Paul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138355785","display_name":"Philip Stephens","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stephens, Philip","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061166351","display_name":"Russell A. Hill","orcid":"https://orcid.org/0000-0002-7601-5802"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hill, Russell A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138308804","display_name":"Lee Oliver","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oliver, Lee","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114733272","display_name":"Katie Appleby","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Appleby, Katie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138342757","display_name":"Sarah Beatham","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Beatham, Sarah","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054097219","display_name":"Naomi Davies Walsh","orcid":"https://orcid.org/0000-0001-8120-6323"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Walsh, Naomi Davies","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015194402","display_name":"Stuart Nixon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nixon, Stuart","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088241739","display_name":"Naomi Matthews","orcid":"https://orcid.org/0000-0003-0466-5088"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matthews, Naomi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078591197","display_name":"Chris Sutherland","orcid":"https://orcid.org/0000-0003-2073-1751"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sutherland, Chris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5120188595","display_name":"Kelly Hitchcock","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hitchcock, Kelly","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10199","display_name":"Wildlife Ecology and Conservation","score":0.28790000081062317,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10199","display_name":"Wildlife Ecology and Conservation","score":0.28790000081062317,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10895","display_name":"Species Distribution and Climate Change","score":0.25999999046325684,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.0868000015616417,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5166000127792358},{"id":"https://openalex.org/keywords/camera-trap","display_name":"Camera trap","score":0.5141000151634216},{"id":"https://openalex.org/keywords/usable","display_name":"USable","score":0.5127999782562256},{"id":"https://openalex.org/keywords/camouflage","display_name":"Camouflage","score":0.489300012588501},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.46869999170303345},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.429500013589859},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4251999855041504},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.32030001282691956},{"id":"https://openalex.org/keywords/citizen-science","display_name":"Citizen science","score":0.31529998779296875}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5759999752044678},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5496000051498413},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5166000127792358},{"id":"https://openalex.org/C2779101711","wikidata":"https://www.wikidata.org/wiki/Q1723004","display_name":"Camera trap","level":3,"score":0.5141000151634216},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.5127999782562256},{"id":"https://openalex.org/C2776196576","wikidata":"https://www.wikidata.org/wiki/Q196113","display_name":"Camouflage","level":2,"score":0.489300012588501},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.429500013589859},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4251999855041504},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3625999987125397},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.32190001010894775},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.32030001282691956},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3174999952316284},{"id":"https://openalex.org/C197352329","wikidata":"https://www.wikidata.org/wiki/Q1093434","display_name":"Citizen science","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.314300000667572},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C130217890","wikidata":"https://www.wikidata.org/wiki/Q47041","display_name":"Biodiversity","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C2780646309","wikidata":"https://www.wikidata.org/wiki/Q1151380","display_name":"Netting","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2849000096321106},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C2987098735","wikidata":"https://www.wikidata.org/wiki/Q3808900","display_name":"Recall rate","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.26600000262260437},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C121099081","wikidata":"https://www.wikidata.org/wiki/Q665580","display_name":"Trap (plumbing)","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.10940","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10940","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.10940","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10940","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6619653701782227,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Camera":[0],"traps":[1],"have":[2,243],"become":[3],"a":[4,79,91,106,118,165,212,233],"cornerstone":[5],"of":[6,17,38,82,93,122,129,186],"biodiversity":[7],"monitoring,":[8],"but":[9],"the":[10,39,136,161,183,205,237,246],"artificial":[11],"intelligence":[12],"that":[13,33,37,242],"turns":[14],"vast":[15],"quantities":[16],"images":[18],"into":[19],"usable":[20],"ecological":[21],"data":[22,181],"is":[23,198,232],"often":[24],"locked":[25],"behind":[26],"commercial":[27],"platforms":[28],"or":[29,174],"trained":[30,109,206],"on":[31,112,135],"fauna":[32],"does":[34],"not":[35],"match":[36],"British":[40],"Isles.":[41],"In":[42],"an":[43,53,113,147],"attempt":[44],"to":[45,158,200,236],"remove":[46],"barriers":[47],"and":[48,65,75,99,110,143,188,218],"increase":[49],"uptake,":[50],"we":[51],"release":[52,204,231],"open-source":[54],"object":[55],"detection":[56],"model":[57],"for":[58,71,240],"31":[59,162],"classes,":[60,163],"28":[61],"common":[62],"UK":[63],"mammal":[64],"bird":[66],"species,":[67],"plus":[68],"utility":[69],"classes":[70],"humans,":[72],"calibration":[73],"poles,":[74],"vehicles,":[76],"drawn":[77],"from":[78,87,156,180,182],"curated":[80],"dataset":[81],"48,165":[83],"labelled":[84],"instances":[85],"assembled":[86],"multiple":[88,238],"sites":[89,187,197],"over":[90,126,245],"decade":[92],"operational":[94],"deployment":[95],"through":[96],"Conservation":[97],"AI":[98],"its":[100],"successor,":[101],"Trap":[102],"Tracker.":[103],"The":[104],"model,":[105],"YOLO26x":[107],"detector":[108],"tested":[111],"80/10/10":[114],"class-stratified":[115],"split,":[116,151],"achieves":[117],"mean":[119,152],"Average":[120],"Precision":[121],"0.984":[123],"at":[124,132,194,224],"Intersection":[125],"Union":[127],"(IoU)":[128],"0.5":[130],"(0.956":[131],"IoU":[133],"0.5-0.95)":[134],"held-out":[137,149],"validation":[138],"set,":[139],"with":[140,164,215,226],"precision":[141],"0.988":[142],"recall":[144],"0.965.":[145],"On":[146],"unseen":[148],"test":[150],"per-species":[153],"confidence":[154],"ranged":[155],"0.96":[157],"0.99":[159],"across":[160],"0.17%":[166],"false-negative":[167],"rate":[168],"concentrated":[169],"in":[170,208],"difficult":[171],"night-time,":[172],"distant,":[173],"occluded":[175],"images.":[176],"These":[177],"metrics":[178],"are":[179],"same":[184],"pool":[185],"cameras":[189],"as":[190],"training,":[191],"so":[192],"performance":[193],"entirely":[195],"new":[196],"left":[199],"future":[201],"work.":[202],"We":[203],"weights":[207],"ONNX":[209],"format":[210],"under":[211],"non-commercial":[213],"licence,":[214],"local":[216],"desktop":[217],"real-time":[219],"camera":[220],"support,":[221],"aimed":[222],"explicitly":[223],"ecologists":[225],"no":[227],"machine-learning":[228],"experience.":[229],"This":[230],"deliberate":[234],"counterweight":[235],"paid":[239],"models":[241],"developed":[244],"last":[247],"decade.":[248]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-11T00:00:00"}
