{"id":"https://openalex.org/W7128701039","doi":"https://doi.org/10.48550/arxiv.2602.10592","title":"Enhancing YOLOv11n for Reliable Child Detection in Noisy Surveillance Footage","display_name":"Enhancing YOLOv11n for Reliable Child Detection in Noisy Surveillance Footage","publication_year":2026,"publication_date":"2026-02-11","ids":{"openalex":"https://openalex.org/W7128701039","doi":"https://doi.org/10.48550/arxiv.2602.10592"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.10592","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125753977","display_name":"Khanh Linh Tran","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tran, Khanh Linh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108765590","display_name":"Minh Nguyen Dang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dang, Minh Nguyen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125741531","display_name":"Thien Nguyen Trong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Trong, Thien Nguyen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125773139","display_name":"Hung Nguyen Quoc","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quoc, Hung Nguyen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125775394","display_name":"Linh Nguyen Kieu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kieu, Linh Nguyen","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5125753977"],"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/T11448","display_name":"Face recognition and analysis","score":0.2854999899864197,"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"}},"topics":[{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.2854999899864197,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.2451999932527542,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.12409999966621399,"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/inference","display_name":"Inference","score":0.5608999729156494},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5501999855041504},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4722000062465668},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.45829999446868896},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.359499990940094},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3465000092983246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6777999997138977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5745999813079834},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5608999729156494},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5501999855041504},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4864000082015991},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4722000062465668},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.45829999446868896},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3716000020503998},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.36239999532699585},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3465000092983246},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3325999975204468},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3158999979496002},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C2776190703","wikidata":"https://www.wikidata.org/wiki/Q488148","display_name":"Slicing","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C2780624872","wikidata":"https://www.wikidata.org/wiki/Q852453","display_name":"Motion detection","level":3,"score":0.25850000977516174}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.10592","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.10592","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.10592","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.10592","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5702599883079529,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,16,36,66,120,138,149],"practical":[4],"and":[5,24,54,83,92,99,117,148,170,187,207],"lightweight":[6],"solution":[7],"for":[8,196],"enhancing":[9],"child":[10,22,73],"detection":[11,41],"in":[12,19,59],"low-quality":[13],"surveillance":[14,201],"footage,":[15],"critical":[17],"component":[18],"real-world":[20],"missing":[21],"alert":[23],"daycare":[25],"monitoring":[26],"systems.":[27],"Building":[28],"upon":[29],"the":[30,125,131,178,208],"efficient":[31],"YOLOv11n":[32],"architecture,":[33],"we":[34,103],"propose":[35],"deployment-ready":[37],"pipeline":[38,180],"that":[39,70],"improves":[40],"under":[42],"challenging":[43],"conditions":[44],"including":[45,89],"occlusion,":[46],"small":[47,98],"object":[48],"size,":[49],"low":[50],"resolution,":[51],"motion":[52],"blur,":[53],"poor":[55],"lighting":[56,90],"commonly":[57],"found":[58],"existing":[60],"CCTV":[61],"infrastructures.":[62],"Our":[63],"approach":[64],"introduces":[65],"domain-specific":[67],"augmentation":[68],"strategy":[69],"synthesizes":[71],"realistic":[72],"placements":[74],"using":[75],"spatial":[76],"perturbations":[77],"such":[78],"as":[79],"partial":[80],"visibility,":[81],"truncation,":[82],"overlaps,":[84],"combined":[85],"with":[86,183],"photometric":[87],"degradations":[88],"variation":[91],"noise.":[93],"To":[94],"improve":[95],"recall":[96],"of":[97,124,146,162,167],"partially":[100],"occluded":[101],"instances,":[102],"integrate":[104],"Slicing":[105],"Aided":[106],"Hyper":[107],"Inference":[108],"(SAHI)":[109],"at":[110,142],"inference":[111],"time.":[112],"All":[113],"components":[114],"are":[115,215],"trained":[116],"evaluated":[118],"on":[119],"filtered,":[121],"child-only":[122],"subset":[123],"Roboflow":[126],"Daycare":[127],"dataset.":[128],"Compared":[129],"to":[130,159,212],"baseline":[132],"YOLOv11n,":[133],"our":[134],"enhanced":[135],"system":[136],"achieves":[137],"mean":[139,150],"Average":[140,151],"Precision":[141,152],"0.5":[143,158],"IoU":[144,155],"(mAP@0.5)":[145],"0.967":[147],"averaged":[153],"over":[154],"thresholds":[156],"from":[157],"0.95":[160],"(mAP@0.5:0.95)":[161],"0.783,":[163],"yielding":[164],"absolute":[165],"improvements":[166],"0.7":[168],"percent":[169],"2.3":[171],"percent,":[172],"respectively,":[173],"without":[174],"architectural":[175],"changes.":[176],"Importantly,":[177],"entire":[179],"maintains":[181],"compatibility":[182],"low-power":[184],"edge":[185],"devices":[186],"supports":[188],"real-time":[189],"performance,":[190],"making":[191],"it":[192,214],"particularly":[193],"well":[194],"suited":[195],"low-cost":[197],"or":[198],"resource-constrained":[199],"industrial":[200],"deployments.":[202],"The":[203],"example":[204],"augmented":[205],"dataset":[206],"source":[209],"code":[210],"used":[211],"generate":[213],"available":[216],"at:":[217],"https://github.com/html-ptit/Data-Augmentation-YOLOv11n-child-detection":[218]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-13T00:00:00"}