{"id":"https://openalex.org/W7135079624","doi":"https://doi.org/10.1109/access.2026.3672935","title":"Infrared Small Target Detection Algorithm Based on PDN-YOLOv11","display_name":"Infrared Small Target Detection Algorithm Based on PDN-YOLOv11","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7135079624","doi":"https://doi.org/10.1109/access.2026.3672935"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3672935","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3672935","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3672935","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051790496","display_name":"Hongmei Li","orcid":"https://orcid.org/0000-0002-9858-7748"},"institutions":[{"id":"https://openalex.org/I174104030","display_name":"Taiyuan Normal University","ror":"https://ror.org/051k00p03","country_code":"CN","type":"education","lineage":["https://openalex.org/I174104030"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongmei Li","raw_affiliation_strings":["College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China"],"raw_orcid":"https://orcid.org/0000-0002-9858-7748","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China","institution_ids":["https://openalex.org/I174104030"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128842896","display_name":"Anqi Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I174104030","display_name":"Taiyuan Normal University","ror":"https://ror.org/051k00p03","country_code":"CN","type":"education","lineage":["https://openalex.org/I174104030"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Anqi Liu","raw_affiliation_strings":["College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China","institution_ids":["https://openalex.org/I174104030"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030922708","display_name":"Luxia Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I174104030","display_name":"Taiyuan Normal University","ror":"https://ror.org/051k00p03","country_code":"CN","type":"education","lineage":["https://openalex.org/I174104030"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luxia Yang","raw_affiliation_strings":["College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China","institution_ids":["https://openalex.org/I174104030"]}]},{"author_position":"last","author":{"id":null,"display_name":"Hongrui Zhang","orcid":"https://orcid.org/0009-0007-7438-4819"},"institutions":[{"id":"https://openalex.org/I174104030","display_name":"Taiyuan Normal University","ror":"https://ror.org/051k00p03","country_code":"CN","type":"education","lineage":["https://openalex.org/I174104030"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongrui Zhang","raw_affiliation_strings":["College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China"],"raw_orcid":"https://orcid.org/0009-0007-7438-4819","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China","institution_ids":["https://openalex.org/I174104030"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I174104030"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40620532,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"39580","last_page":"39590"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.883899986743927,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.883899986743927,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.06780000030994415,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.007400000002235174,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.7854999899864197},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6144000291824341},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5925999879837036},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5674999952316284},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5281999707221985},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5241000056266785},{"id":"https://openalex.org/keywords/information-loss","display_name":"Information loss","score":0.41659998893737793}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.7854999899864197},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7512999773025513},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6144000291824341},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5925999879837036},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5674999952316284},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5418999791145325},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5281999707221985},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5241000056266785},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5115000009536743},{"id":"https://openalex.org/C2988416141","wikidata":"https://www.wikidata.org/wiki/Q6031139","display_name":"Information loss","level":2,"score":0.41659998893737793},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3815000057220459},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3124000132083893},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.2685000002384186}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3672935","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3672935","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:164d76c43a3a4635b6622be2fa35c490","is_oa":true,"landing_page_url":"https://doaj.org/article/164d76c43a3a4635b6622be2fa35c490","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":"IEEE Access, Vol 14, Pp 39580-39590 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3672935","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3672935","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320329796","display_name":"Taiyuan Normal University","ror":"https://ror.org/051k00p03"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2102605133","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963351448","https://openalex.org/W2964241181","https://openalex.org/W3034551897","https://openalex.org/W3034552520","https://openalex.org/W3039443125","https://openalex.org/W3088317060","https://openalex.org/W3118249006","https://openalex.org/W3171950886","https://openalex.org/W4226043641","https://openalex.org/W4250482878","https://openalex.org/W4289752563","https://openalex.org/W4313065862","https://openalex.org/W4319265540","https://openalex.org/W4386071462","https://openalex.org/W4386076325","https://openalex.org/W4399300294","https://openalex.org/W4402979889","https://openalex.org/W4403022807","https://openalex.org/W4403770406","https://openalex.org/W4406138192","https://openalex.org/W4409560167","https://openalex.org/W7124202231"],"related_works":[],"abstract_inverted_index":{"Infrared":[0],"small":[1,8,128,167],"targets":[2],"in":[3,20,186,190,218,227],"complex":[4],"backgrounds":[5],"suffer":[6],"from":[7],"size,":[9],"sparse":[10],"features,":[11],"and":[12,63,93,102,148,188,214,231,236,247],"strong":[13],"noise,":[14],"leading":[15],"to":[16,56,98,120,136,140,160,175,241],"a":[17,41,51,78,117,208],"pronounced":[18],"decline":[19],"detection":[21,33,111,146,212],"accuracy.":[22],"To":[23],"address":[24],"this":[25],"problem,":[26],"we":[27],"propose":[28],"PDN-YOLOv11":[29,181],"(PPA_DSCU_NWD-YOLOv11),":[30],"an":[31],"improved":[32],"algorithm":[34],"based":[35],"on":[36,192,233],"the":[37,67,104,110,122,131,151,166,176,193,199,219,222,234,242],"YOLOv11n":[38],"model.":[39],"First,":[40],"Parallelized":[42],"Patch-Aware":[43],"Attention":[44],"(PPA)":[45],"module":[46],"is":[47,84,113,134,158],"introduced,":[48],"which":[49],"employs":[50],"multi-branch":[52],"feature":[53,58,100,106],"extraction":[54],"strategy":[55],"capture":[57,123],"information":[59,72],"at":[60],"different":[61],"scales":[62],"hierarchies,":[64],"thereby":[65,143],"ensuring":[66],"effective":[68],"retention":[69],"of":[70,124,184,201,229],"critical":[71],"after":[73],"multiple":[74],"downsampling":[75],"operations.":[76],"Then,":[77],"Dual-path":[79],"Spatial-channel":[80],"Co-reconstruction":[81],"Unit":[82],"(DSCU)":[83],"constructed.":[85],"It":[86],"integrates":[87],"residual-enhanced":[88],"Spatial":[89],"Reconstruction":[90,95],"Units":[91,96],"(SRU-R)":[92],"Channel":[94],"(CRU-R)":[97],"reduce":[99],"redundancy":[101],"enhance":[103],"model's":[105],"representation":[107],"capacity.":[108],"Furthermore,":[109,217],"head":[112],"optimized":[114],"by":[115,165,204],"adding":[116],"P2":[118],"layer":[119,133],"strengthen":[121],"fine":[125],"details":[126],"for":[127],"targets,":[129],"while":[130,196],"P5":[132],"removed":[135],"suppress":[137],"excessive":[138],"attention":[139],"large-scale":[141],"backgrounds,":[142],"improving":[144],"both":[145],"accuracy":[147,213],"efficiency.":[149,216],"Finally,":[150],"Normalized":[152],"Wasserstein":[153],"Distance":[154],"(NWD)":[155],"loss":[156],"function":[157],"adopted":[159],"mitigate":[161],"localization":[162],"biases":[163],"caused":[164],"target":[168],"size.":[169],"Experimental":[170],"results":[171],"demonstrate":[172],"that":[173],"compared":[174,240],"original":[177],"YOLOv11":[178,243],"model,":[179,244],"our":[180],"achieves":[182,225],"increases":[183,226],"1.2%":[185],"mAP@0.5":[187,228],"6.3%":[189],"mAP@0.5:0.95":[191],"NUDT-SIRST":[194],"dataset,":[195],"simultaneously":[197],"reducing":[198],"number":[200],"model":[202,224],"parameters":[203],"22.9%.":[205],"This":[206],"indicates":[207],"favorable":[209],"balance":[210],"between":[211],"inference":[215],"generalization":[220],"experiments,":[221],"proposed":[223],"1.1%":[230],"1.6%":[232],"IRSTD-1K":[235],"NUAA-SIRST":[237],"datasets,":[238],"respectively,":[239],"reaching":[245],"94.1%":[246],"84.7%.":[248]},"counts_by_year":[],"updated_date":"2026-03-20T20:47:17.329874","created_date":"2026-03-13T00:00:00"}
