{"id":"https://openalex.org/W4416249485","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228410","title":"DMSnet: Dual multi-scale background stripping nested network for single-frame infrared small target detection","display_name":"DMSnet: Dual multi-scale background stripping nested network for single-frame infrared small target detection","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249485","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228410"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228410","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5003964217","display_name":"Zhiwei Hu","orcid":"https://orcid.org/0000-0003-0324-2227"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zichang Hu","raw_affiliation_strings":["Wuhan University of Science and Technology,Department of Computer Science and Technology,Wuhan,China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Science and Technology,Department of Computer Science and Technology,Wuhan,China","institution_ids":["https://openalex.org/I43922553"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067275102","display_name":"Xuhao Guo","orcid":"https://orcid.org/0000-0002-2436-7961"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xuhao Guo","raw_affiliation_strings":["University of Melbourne,Faculty of Engineering and Information Technology,Melbourne,Australia"],"affiliations":[{"raw_affiliation_string":"University of Melbourne,Faculty of Engineering and Information Technology,Melbourne,Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014472255","display_name":"He Deng","orcid":"https://orcid.org/0000-0002-4402-4923"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"He Deng","raw_affiliation_strings":["Wuhan University of Science and Technology,Department of Computer Science and Technology,Wuhan,China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Science and Technology,Department of Computer Science and Technology,Wuhan,China","institution_ids":["https://openalex.org/I43922553"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5003964217"],"corresponding_institution_ids":["https://openalex.org/I43922553"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46814704,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9763000011444092,"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":0.9763000011444092,"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/T14158","display_name":"Optical Systems and Laser Technology","score":0.004900000058114529,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T12994","display_name":"Infrared Thermography in Medicine","score":0.002400000113993883,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.664900004863739},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6621000170707703},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5455999970436096},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.48429998755455017},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45559999346733093},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4465000033378601},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.414900004863739},{"id":"https://openalex.org/keywords/feature-detection","display_name":"Feature detection (computer vision)","score":0.3846000134944916},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3700000047683716}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7243000268936157},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.664900004863739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6621000170707703},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6556000113487244},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5455999970436096},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.48429998755455017},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4465000033378601},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.414900004863739},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.3846000134944916},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3734999895095825},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3700000047683716},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3637999892234802},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.36070001125335693},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C158355884","wikidata":"https://www.wikidata.org/wiki/Q11388","display_name":"Infrared","level":2,"score":0.3208000063896179},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.28850001096725464},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.2833000123500824},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26579999923706055},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25940001010894775},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228410","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":31,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1978993121","https://openalex.org/W1992873714","https://openalex.org/W2041560658","https://openalex.org/W2080197848","https://openalex.org/W2118967151","https://openalex.org/W2344310038","https://openalex.org/W2544366167","https://openalex.org/W2565639579","https://openalex.org/W2604768956","https://openalex.org/W2745102798","https://openalex.org/W2799166040","https://openalex.org/W2896123015","https://openalex.org/W2912919760","https://openalex.org/W2963420686","https://openalex.org/W2963890956","https://openalex.org/W2991758591","https://openalex.org/W2996290406","https://openalex.org/W3010079414","https://openalex.org/W3015788359","https://openalex.org/W3039443125","https://openalex.org/W3088317060","https://openalex.org/W3097913527","https://openalex.org/W3118249006","https://openalex.org/W3118934234","https://openalex.org/W3171950886","https://openalex.org/W4285137940","https://openalex.org/W4288064706","https://openalex.org/W4294310853","https://openalex.org/W4317794940"],"related_works":[],"abstract_inverted_index":{"The":[0],"research":[1],"of":[2,24,58,92,101,149,177,198,203,211],"single-frame":[3],"infrared":[4,103,218],"small":[5,35,59,104,150,215],"target":[6,25,36,105],"detection":[7,78],"has":[8],"made":[9],"some":[10,124],"achievements,":[11],"but":[12],"still":[13,48],"needs":[14],"to":[15,89,173],"be":[16],"improved.":[17],"Traditional":[18],"methods":[19,200],"rely":[20],"on":[21,185],"the":[22,29,39,51,56,62,77,93,98,102,108,117,130,147,159,209],"assumption":[23],"background":[26,53,72,122],"difference,":[27],"and":[28,55,113,116,123,161,179,187,206],"effect":[30],"is":[31,43,47,79,171],"poor":[32],"in":[33,61,201,213,217],"multi-scale":[34,71,169],"detection.":[37],"Although":[38],"deep":[40,63],"learning":[41],"method":[42],"more":[44],"promising,":[45],"it":[46],"affected":[49],"by":[50,96],"complex":[52,221],"noise":[54],"loss":[57],"targets":[60,151,216],"network.":[64,75],"Therefore,":[65],"we":[66],"propose":[67],"DMSnet,":[68],"a":[69,82,140,153,195],"dual":[70],"stripping":[73],"nested":[74,141],"Firstly,":[76],"transformed":[80],"into":[81,129],"low":[83],"rank":[84],"sparse":[85],"matrix":[86],"optimization":[87],"problem":[88],"strip":[90],"part":[91],"adverse":[94],"information":[95],"using":[97],"non-local":[99],"self-similarity":[100],"image.":[106],"Then":[107],"original":[109],"image":[110,119,181],"with":[111,120,158,220],"complete":[112],"rich":[114],"details":[115],"improved":[118],"stripped":[121],"non-target":[125],"elements":[126],"are":[127],"input":[128],"double":[131],"branch":[132],"structure":[133],"for":[134,165],"feature":[135,166,182],"extraction.":[136],"Feature":[137],"extraction":[138],"uses":[139],"u-net":[142],"structure,":[143],"which":[144],"can":[145],"retain":[146],"representation":[148],"at":[152],"deeper":[154],"level.":[155],"It":[156],"combines":[157],"spatial":[160],"channel":[162],"attention":[163],"module":[164],"enhancement.":[167],"Finally,":[168],"fusion":[170],"used":[172],"make":[174],"full":[175],"use":[176],"high-level":[178],"low-level":[180],"information.":[183],"Experiments":[184],"NUDT-SIRST":[186],"SIRST":[188],"datasets":[189],"show":[190],"that":[191],"DMSnet":[192,212],"stably":[193],"surpasses":[194],"large":[196],"number":[197],"existing":[199],"terms":[202],"IoU,":[204],"PD":[205],"FA,":[207],"indicating":[208],"effectiveness":[210],"detecting":[214],"images":[219],"backgrounds.":[222]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}