{"id":"https://openalex.org/W4416749444","doi":"https://doi.org/10.1109/iros60139.2025.11247694","title":"SheepDA-YOLO: Cross-Domain Adaptive Mean Teacher with Dual-Path Decoupling for Sheep Behavior Recognition","display_name":"SheepDA-YOLO: Cross-Domain Adaptive Mean Teacher with Dual-Path Decoupling for Sheep Behavior Recognition","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416749444","doi":"https://doi.org/10.1109/iros60139.2025.11247694"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11247694","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247694","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050710862","display_name":"Xinjie Chen","orcid":"https://orcid.org/0000-0002-5492-225X"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinjie Chen","raw_affiliation_strings":["Northwest A&#x0026;F University,College of Information Engineering,Yangling,China,712100"],"affiliations":[{"raw_affiliation_string":"Northwest A&#x0026;F University,College of Information Engineering,Yangling,China,712100","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100392967","display_name":"Haotian Zhang","orcid":"https://orcid.org/0000-0003-0478-3869"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haotian Zhang","raw_affiliation_strings":["Northwest A&#x0026;F University,College of Information Engineering,Yangling,China,712100"],"affiliations":[{"raw_affiliation_string":"Northwest A&#x0026;F University,College of Information Engineering,Yangling,China,712100","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100620176","display_name":"Yu Qiao","orcid":"https://orcid.org/0000-0002-2191-3875"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongyuan Qiao","raw_affiliation_strings":["Northwest A&#x0026;F University,College of Information Engineering,Yangling,China,712100"],"affiliations":[{"raw_affiliation_string":"Northwest A&#x0026;F University,College of Information Engineering,Yangling,China,712100","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032622366","display_name":"Meili Wang","orcid":"https://orcid.org/0000-0001-7901-1789"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meili Wang","raw_affiliation_strings":["Northwest A&#x0026;F University,College of Information Engineering,Yangling,China,712100"],"affiliations":[{"raw_affiliation_string":"Northwest A&#x0026;F University,College of Information Engineering,Yangling,China,712100","institution_ids":["https://openalex.org/I89652312"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5050710862"],"corresponding_institution_ids":["https://openalex.org/I89652312"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.43466944,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1833","last_page":"1838"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10838","display_name":"Animal Behavior and Welfare Studies","score":0.22310000658035278,"subfield":{"id":"https://openalex.org/subfields/3404","display_name":"Small Animals"},"field":{"id":"https://openalex.org/fields/34","display_name":"Veterinary"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10838","display_name":"Animal Behavior and Welfare Studies","score":0.22310000658035278,"subfield":{"id":"https://openalex.org/subfields/3404","display_name":"Small Animals"},"field":{"id":"https://openalex.org/fields/34","display_name":"Veterinary"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.14509999752044678,"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"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.1225999966263771,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/decoupling","display_name":"Decoupling (probability)","score":0.517799973487854},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.446399986743927},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4404999911785126},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.43389999866485596},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.38609999418258667},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.31439998745918274},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.3095000088214874},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.30799999833106995}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7067999839782715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.618399977684021},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.517799973487854},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.446399986743927},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4404999911785126},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.43389999866485596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.396699994802475},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.38609999418258667},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31189998984336853},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.290800005197525},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2671999931335449},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11247694","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247694","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2584009249","https://openalex.org/W2617027347","https://openalex.org/W2892389796","https://openalex.org/W2968634921","https://openalex.org/W2972422394","https://openalex.org/W3034937575","https://openalex.org/W3108316907","https://openalex.org/W3163617518","https://openalex.org/W3166409449","https://openalex.org/W3177339245","https://openalex.org/W3193979638","https://openalex.org/W4312993742","https://openalex.org/W4319970173","https://openalex.org/W4386071781","https://openalex.org/W4386319016","https://openalex.org/W4391589924","https://openalex.org/W4394595160"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,11,45,65,75,85,207,211,221],"rapid":[2],"advancement":[3],"of":[4,47,49,51,67,77,106,110,213,224],"smart":[5,225],"farming":[6],"towards":[7],"large-scale":[8,70],"livestock":[9,226],"operations,":[10],"demand":[12],"for":[13,53,116,220],"model":[14],"generalization":[15],"in":[16,35,69,102,187],"cross-pen":[17,214],"behavior":[18],"recognition":[19,177],"has":[20],"significantly":[21,200],"increased.":[22],"Traditional":[23],"deep":[24],"learning":[25],"models":[26,68],"suffer":[27],"from":[28],"substantial":[29],"performance":[30],"degradation":[31],"due":[32],"to":[33,57,97,125,145,174,209],"variations":[34],"illumination":[36,155],"and":[37,94,118,157,164,199],"structure":[38],"across":[39,81],"different":[40],"sheep":[41,71,192],"pens,":[42,193],"often":[43],"necessitating":[44],"re-annotation":[46],"tens":[48],"thousands":[50],"frames":[52],"each":[54],"new":[55],"environment":[56],"mitigate":[58],"domain":[59,128],"shift":[60],"issues.":[61],"This":[62],"severely":[63],"limits":[64],"deployment":[66],"farms.":[72],"To":[73],"achieve":[74],"goal":[76],"\u2019annotate":[78],"once,":[79],"generalize":[80],"pens,\u2019":[82],"we":[83],"propose":[84],"SheepDA-YOLO":[86,183],"framework,":[87],"which":[88,153],"innovatively":[89],"integrates":[90],"contrastive":[91],"image":[92],"translation":[93],"feature":[95,158,162],"decoupling":[96,163],"address":[98],"cross-domain":[99,188],"adaptation":[100],"challenges":[101],"agriculture.":[103],"The":[104,204],"core":[105],"our":[107],"method":[108,124],"consists":[109],"four":[111],"parts:":[112],"generating":[113],"bidirectional":[114],"pseudo-images":[115],"source":[117],"target":[119,191],"domains":[120],"based":[121],"on":[122,190],"CUT":[123],"reduce":[126],"image-level":[127],"discrepancies":[129],"through":[130,160],"mixed":[131],"training":[132],"sets;":[133],"employing":[134],"a":[135,141,170],"Mean":[136],"Teacher":[137],"architecture":[138],"combined":[139],"with":[140],"quadruple":[142],"loss":[143],"function":[144],"ensure":[146],"stable":[147],"knowledge":[148],"transfer;":[149],"proposing":[150],"DP-DMAF":[151],"module,":[152],"suppresses":[154],"interference":[156],"confusion":[159],"dual-path":[161],"separable":[165],"large-kernel":[166],"attention,":[167],"complemented":[168],"by":[169,197],"high-resolution":[171],"detection":[172],"layer":[173],"enhance":[175],"small-target":[176],"accuracy.":[178],"Experimental":[179],"results":[180],"demonstrate":[181],"that":[182],"achieves":[184],"89.7%":[185],"mAP":[186],"testing":[189],"outperforming":[194],"state-of-the-art":[195],"methods":[196],"3.4%":[198],"reducing":[201],"annotation":[202],"costs.":[203],"study":[205],"is":[206],"first":[208],"validate":[210],"feasibility":[212],"adaptation,":[215],"providing":[216],"an":[217],"efficient":[218],"solution":[219],"scalable":[222],"implementation":[223],"farming.":[227]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-28T00:00:00"}
