{"id":"https://openalex.org/W7134049130","doi":"https://doi.org/10.7717/peerj-cs.3523","title":"SemiXAI-Net: XAI-driven semi-supervised object detection <i>via</i> iterative pseudo-label refinement and spatiotemporal consistency","display_name":"SemiXAI-Net: XAI-driven semi-supervised object detection <i>via</i> iterative pseudo-label refinement and spatiotemporal consistency","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7134049130","doi":"https://doi.org/10.7717/peerj-cs.3523"},"language":"en","primary_location":{"id":"doi:10.7717/peerj-cs.3523","is_oa":true,"landing_page_url":"https://doi.org/10.7717/peerj-cs.3523","pdf_url":null,"source":{"id":"https://openalex.org/S4210178049","display_name":"PeerJ Computer Science","issn_l":"2376-5992","issn":["2376-5992"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320104","host_organization_name":"PeerJ, Inc.","host_organization_lineage":["https://openalex.org/P4310320104"],"host_organization_lineage_names":["PeerJ, Inc."],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PeerJ Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.7717/peerj-cs.3523","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071786340","display_name":"Hassan Tahir","orcid":"https://orcid.org/0000-0002-4420-3796"},"institutions":[{"id":"https://openalex.org/I63525965","display_name":"Western Sydney University","ror":"https://ror.org/03t52dk35","country_code":"AU","type":"education","lineage":["https://openalex.org/I63525965"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Hassam Tahir","raw_affiliation_strings":["School of Computing, Engineering and Mathematics, Western Sydney University, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, Engineering and Mathematics, Western Sydney University, Sydney, Australia","institution_ids":["https://openalex.org/I63525965"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076216740","display_name":"Walaa Alayed","orcid":"https://orcid.org/0000-0001-9968-8439"},"institutions":[{"id":"https://openalex.org/I106778892","display_name":"Princess Nourah bint Abdulrahman University","ror":"https://ror.org/05b0cyh02","country_code":"SA","type":"education","lineage":["https://openalex.org/I106778892"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Walaa Alayed","raw_affiliation_strings":["Department of Information Technology, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0001-9968-8439","affiliations":[{"raw_affiliation_string":"Department of Information Technology, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia","institution_ids":["https://openalex.org/I106778892"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128271367","display_name":"Waqar Ul Hassan","orcid":null},"institutions":[{"id":"https://openalex.org/I43879129","display_name":"Government College University, Lahore","ror":"https://ror.org/040gec961","country_code":"PK","type":"education","lineage":["https://openalex.org/I43879129"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Waqar Ul Hassan","raw_affiliation_strings":["Department of Mathematics, Government College University Lahore, Lahore, Pakistan"],"raw_orcid":"https://orcid.org/0009-0000-4929-3316","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Government College University Lahore, Lahore, Pakistan","institution_ids":["https://openalex.org/I43879129"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121018368","display_name":"Truong X. Tran","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Truong X. Tran","raw_affiliation_strings":["School of Science, Engineering and Technology, Penn State Harrisburg, The Pennsylvania State University, PA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science, Engineering and Technology, Penn State Harrisburg, The Pennsylvania State University, PA, United States","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5071786340"],"corresponding_institution_ids":["https://openalex.org/I63525965"],"apc_list":{"value":1395,"currency":"USD","value_usd":1395},"apc_paid":{"value":1395,"currency":"USD","value_usd":1395},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41554015,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"e3523","last_page":"e3523"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.8076000213623047,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.8076000213623047,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.14409999549388885,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.013899999670684338,"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/overfitting","display_name":"Overfitting","score":0.766700029373169},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6424000263214111},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.54830002784729},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5073000192642212},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4666000008583069},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.4593999981880188},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4449999928474426},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.4440999925136566},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4404999911785126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7944999933242798},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.766700029373169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6624000072479248},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6424000263214111},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.54830002784729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5284000039100647},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5073000192642212},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.4593999981880188},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4449999928474426},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4404999911785126},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3937999904155731},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.39079999923706055},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.3643999993801117},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3402999937534332},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2628999948501587}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.7717/peerj-cs.3523","is_oa":true,"landing_page_url":"https://doi.org/10.7717/peerj-cs.3523","pdf_url":null,"source":{"id":"https://openalex.org/S4210178049","display_name":"PeerJ Computer Science","issn_l":"2376-5992","issn":["2376-5992"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320104","host_organization_name":"PeerJ, Inc.","host_organization_lineage":["https://openalex.org/P4310320104"],"host_organization_lineage_names":["PeerJ, Inc."],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PeerJ Computer Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:12c67f91e0c44c979e0c152b8be4a92f","is_oa":true,"landing_page_url":"https://doaj.org/article/12c67f91e0c44c979e0c152b8be4a92f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PeerJ Computer Science, Vol 12, p e3523 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.7717/peerj-cs.3523","is_oa":true,"landing_page_url":"https://doi.org/10.7717/peerj-cs.3523","pdf_url":null,"source":{"id":"https://openalex.org/S4210178049","display_name":"PeerJ Computer Science","issn_l":"2376-5992","issn":["2376-5992"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320104","host_organization_name":"PeerJ, Inc.","host_organization_lineage":["https://openalex.org/P4310320104"],"host_organization_lineage_names":["PeerJ, Inc."],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PeerJ Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6715745329856873}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2295598507","https://openalex.org/W2965545010","https://openalex.org/W2981731882","https://openalex.org/W2985495028","https://openalex.org/W3012481664","https://openalex.org/W3023939003","https://openalex.org/W3034749675","https://openalex.org/W4226441780","https://openalex.org/W4391798186","https://openalex.org/W4394717640","https://openalex.org/W4401750306","https://openalex.org/W4403966990"],"related_works":[],"abstract_inverted_index":{"In":[0,240],"recent":[1],"years,":[2],"object":[3,89],"detection":[4,24,90],"has":[5],"become":[6],"an":[7,236],"indispensable":[8],"component":[9],"in":[10,44,127,294],"various":[11],"critical":[12,295],"applications,":[13,292],"ranging":[14],"from":[15,153],"autonomous":[16,298],"driving":[17],"to":[18,36,62,98,137,218,282],"surveillance":[19],"systems.":[20],"However,":[21],"achieving":[22],"high":[23,302],"accuracy":[25,226,247],"alone":[26],"is":[27,57],"insufficient;":[28],"models":[29,66],"must":[30],"also":[31],"be":[32],"transparent":[33],"and":[34,39,60,116,157,184,199,235,304],"interpretable":[35],"foster":[37],"trust":[38],"ensure":[40],"reliable":[41],"decision-making,":[42],"especially":[43],"safety-critical":[45],"domains.":[46],"Traditional":[47],"supervised":[48],"approaches":[49],"require":[50],"large":[51],"amounts":[52],"of":[53,144,227,230,233,238,248,253,264,273],"labeled":[54,142],"data,":[55,277],"which":[56],"often":[58],"expensive":[59],"time-consuming":[61],"obtain.":[63],"Additionally,":[64,266],"these":[65],"typically":[67],"function":[68],"as":[69],"\u201cblack":[70],"boxes,\u201d":[71],"providing":[72],"limited":[73],"insight":[74],"into":[75],"their":[76],"decision":[77],"processes.":[78],"This":[79],"article":[80],"introduces":[81],"SemiXAI-Net,":[82],"a":[83,103,224,245,270],"semi-supervised":[84,139],"learning":[85,181],"framework":[86,107,163,268],"optimized":[87,175],"for":[88,160,176,290],"tasks":[91],"while":[92],"incorporating":[93],"explainable":[94],"AI":[95],"(XAI)":[96],"techniques":[97],"enhance":[99],"transparency.":[100],"By":[101],"utilizing":[102],"teacher-student":[104],"architecture,":[105],"the":[106,124,190,260,267],"iteratively":[108],"refines":[109],"pseudo-labels,":[110],"effectively":[111],"improving":[112],"both":[113,301],"model":[114,192,305],"performance":[115,303],"interpretability.":[117],"The":[118,162,250],"experimental":[119],"evaluation":[120],"was":[121,147,164],"performed":[122],"using":[123,171],"Common":[125],"Objects":[126],"Context":[128],"(COCO)":[129],"dataset":[130],"(a":[131],"real-world":[132,291],"benchmark)":[133],"alongside":[134],"synthetic":[135,158],"datasets":[136],"simulate":[138],"conditions.":[140],"A":[141],"subset":[143],"COCO":[145],"images":[146,156],"combined":[148],"with":[149,173,215],"unlabeled":[150],"data":[151],"(both":[152],"COCO\u2019s":[154],"unannotated":[155],"sources)":[159],"training.":[161],"implemented":[165],"on":[166,275],"NVIDIA":[167],"Tesla":[168],"V100":[169],"GPUs":[170],"PyTorch,":[172],"hyperparameters":[174],"stability":[177],"(batch":[178],"size:":[179],"32,":[180],"rate:":[182],"0.001)":[183],"efficiency.":[185],"To":[186],"enable":[187],"real-time":[188],"interpretability,":[189],"student":[191],"integrated":[193],"Gradient-weighted":[194],"Class":[195],"Activation":[196],"Mapping":[197],"(Grad-CAM)":[198],"Local":[200],"Interpretable":[201],"Model-Agnostic":[202],"Explanations":[203],"(LIME)":[204],"techniques,":[205],"generating":[206],"visual":[207],"explanations":[208],"during":[209],"inference.":[210],"Training":[211],"spanned":[212],"10":[213],"epochs":[214],"early":[216],"stopping":[217],"mitigate":[219],"overfitting":[220],"risks.":[221],"SemiXAI-Net":[222,254],"achieves":[223],"superior":[225],"93%,":[228],"precision":[229],"94%,":[231,257],"recall":[232],"95%,":[234],"F1-score":[237],"94.5%.":[239],"comparison,":[241],"traditional":[242,261],"methods":[243],"reach":[244],"maximum":[246],"87%.":[249],"explainability":[251],"score":[252,263],"stands":[255],"at":[256],"significantly":[258],"surpassing":[259],"method\u2019s":[262],"71%.":[265],"demonstrates":[269],"generalization":[271],"ability":[272],"94%":[274],"unseen":[276],"making":[278],"it":[279],"highly":[280],"adaptable":[281],"new":[283],"environments.":[284],"These":[285],"results":[286],"highlight":[287],"SemiXAI-Net\u2019s":[288],"potential":[289],"particularly":[293],"domains":[296],"like":[297],"driving,":[299],"where":[300],"transparency":[306],"are":[307],"essential.":[308]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2026-03-07T00:00:00"}