{"id":"https://openalex.org/W4229439183","doi":"https://doi.org/10.3233/jcm-226112","title":"An improved SSD method for infrared target detection based on convolutional neural network","display_name":"An improved SSD method for infrared target detection based on convolutional neural network","publication_year":2022,"publication_date":"2022-05-10","ids":{"openalex":"https://openalex.org/W4229439183","doi":"https://doi.org/10.3233/jcm-226112"},"language":"en","primary_location":{"id":"doi:10.3233/jcm-226112","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jcm-226112","pdf_url":null,"source":{"id":"https://openalex.org/S2765058733","display_name":"Journal of Computational Methods in Sciences and Engineering","issn_l":"1472-7978","issn":["1472-7978","1875-8983"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Methods in Sciences and Engineering","raw_type":"journal-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/A5100412387","display_name":"Gang Liu","orcid":"https://orcid.org/0000-0001-6676-6621"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gang Liu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101184662","display_name":"Zixuan Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zixuan Cao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100358498","display_name":"Sen Liu","orcid":"https://orcid.org/0000-0003-3778-8973"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sen Liu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035560190","display_name":"Bin Song","orcid":"https://orcid.org/0000-0002-8096-3370"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bin Song","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100413269","display_name":"Zhonghua Liu","orcid":"https://orcid.org/0000-0001-9689-7753"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhonghua Liu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100412387"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2318,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83059548,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"22","issue":"4","first_page":"1393","last_page":"1408"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9987999796867371,"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/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.9916999936103821,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8001818060874939},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7050981521606445},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.682560920715332},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5904473662376404},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.535020112991333},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5313212275505066},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5011022090911865},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.494625061750412},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.46312862634658813},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4380887746810913},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4375171959400177},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.4287768602371216},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4266073405742645},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.390097975730896},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.30104511976242065},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07512277364730835}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8001818060874939},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7050981521606445},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.682560920715332},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5904473662376404},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.535020112991333},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5313212275505066},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5011022090911865},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.494625061750412},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.46312862634658813},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4380887746810913},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4375171959400177},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.4287768602371216},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4266073405742645},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.390097975730896},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30104511976242065},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07512277364730835},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jcm-226112","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jcm-226112","pdf_url":null,"source":{"id":"https://openalex.org/S2765058733","display_name":"Journal of Computational Methods in Sciences and Engineering","issn_l":"1472-7978","issn":["1472-7978","1875-8983"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Methods in Sciences and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1998399571","https://openalex.org/W2137105523","https://openalex.org/W2193145675","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2753588254","https://openalex.org/W2798355657","https://openalex.org/W2884561390","https://openalex.org/W2899607431","https://openalex.org/W2919115771","https://openalex.org/W2957221598","https://openalex.org/W2962766617","https://openalex.org/W2963037989","https://openalex.org/W2963058975","https://openalex.org/W2963091558","https://openalex.org/W2963857746","https://openalex.org/W2964444661","https://openalex.org/W2988916019","https://openalex.org/W2995412840","https://openalex.org/W3034552520","https://openalex.org/W3106250896","https://openalex.org/W3151922143","https://openalex.org/W4253695868","https://openalex.org/W6765643516","https://openalex.org/W6768952390"],"related_works":["https://openalex.org/W2348909947","https://openalex.org/W4292672442","https://openalex.org/W2362101859","https://openalex.org/W2941610985","https://openalex.org/W4249847449","https://openalex.org/W2791431590","https://openalex.org/W1978900583","https://openalex.org/W2350688482","https://openalex.org/W2053575972","https://openalex.org/W4323256314"],"abstract_inverted_index":{"Target":[0],"detection":[1,50,79,92,253,314],"is":[2,66,88,108,153,170,185,245],"the":[3,27,40,44,56,72,95,117,125,160,167,174,178,188,199,225,229,232,241,249,252,256,272,294,300,316],"basis":[4],"for":[5,267,285],"automatic":[6],"target":[7,32,49,78,113,128,242],"recognition":[8,20],"system":[9],"of":[10,31,58,74,120,127,141,162,182,235,251,259],"infrared":[11,75,296],"imaging":[12,76],"guidance":[13,77],"to":[14,70,111,166,240,247,255],"complete":[15],"subsequent":[16],"tasks":[17],"such":[18,327],"as":[19,328],"and":[21,34,61,177,195,198,206,216,221,228,284,306,346],"tracking.":[22],"Existing":[23],"systems":[24],"have":[25],"not":[26,109,123],"autonomous":[28,59],"learning":[29,54,60],"ability":[30,57],"feature,":[33],"it":[35,290],"will":[36],"be":[37],"powerless":[38],"once":[39],"task":[41],"environment":[42],"exceeds":[43],"pre-planned":[45],"condition.":[46],"The":[47,262],"single-stage":[48,91],"based":[51,172],"on":[52,173,281,319],"deep":[53],"has":[55,104,192,203],"high":[62,204],"computational":[63],"efficiency,":[64],"which":[65,107,191,202],"an":[67],"effective":[68],"way":[69],"solve":[71],"problem":[73],"in":[80,102,139],"complex":[81],"environment.":[82],"SSD":[83,103,121,236],"(Single":[84],"Shot":[85,337,343,350],"MultiBox":[86],"Detector)":[87,345],"a":[89],"classical":[90],"model,":[93],"however,":[94],"convolution":[96],"layer":[97,165,190,201],"with":[98],"strong":[99,196],"semantic":[100],"information":[101],"low":[105,193],"resolution,":[106],"conducive":[110],"small":[112,260,310],"detection.":[114],"In":[115],"addition,":[116],"location":[118,233],"loss":[119,234],"does":[122],"consider":[124],"impact":[126],"scale":[129,243],"change.":[130],"Therefore,":[131],"this":[132],"paper":[133],"puts":[134],"forward":[135],"two":[136],"improvement":[137],"ideas":[138],"view":[140],"SSD:":[142],"(1)":[143],"Starting":[144,210],"from":[145,211],"FPN":[146],"(Feature":[147],"Pyramid":[148],"Network),":[149],"feature":[150,164,179,189,200],"channel\u2019s":[151],"importance":[152],"distinguished":[154],"through":[155],"efficient":[156],"channel":[157],"attention":[158],"mechanism,":[159],"contribution":[161],"each":[163],"fusion":[168,181],"output":[169],"described":[171],"learnable":[175],"weight,":[176],"weighted":[180],"bidirectional":[183],"multi-scale":[184],"realized":[186],"between":[187,224],"resolution":[194,205],"semantics":[197],"weak":[207],"semantics.":[208],"(2)":[209],"IoU":[212],"(Intersection":[213],"over":[214],"Union)":[215],"considering":[217],"non":[218],"overlapping":[219],"parts":[220],"geometric":[222],"relationship":[223],"predicted":[226],"box":[227],"ground-truth":[230],"box,":[231],"that":[237],"remains":[238],"invariable":[239],"change":[244],"constructed":[246],"improve":[248],"sensitivity":[250],"model":[254],"locating":[257],"error":[258],"target.":[261],"experimental":[263,320],"results":[264,321],"show":[265],"that,":[266],"300":[268,270],"\u00d7":[269,287],"input,":[271,289],"presented":[273,317],"method":[274,302,318],"achieves":[275,303],"84.7%":[276],"mAP":[277,305],"(mean":[278],"Average":[279],"Precision)":[280],"VOC2007":[282],"test":[283],"512":[286,288],"reaches":[291],"86.6%.":[292],"On":[293],"self-built":[295],"aircraft":[297],"data":[298],"set,":[299],"proposed":[301],"81.1%":[304],"can":[307],"detect":[308],"more":[309],"targets.":[311],"Without":[312],"affecting":[313],"speed,":[315],"outperforms":[322],"some":[323],"comparable":[324],"state-of-the-art":[325],"models":[326],"YOLOv3":[329],"(You":[330],"Only":[331],"Look":[332],"Once),":[333],"DSSD":[334],"(Deconvolutional":[335],"Single":[336,342,349],"Multibox":[338,344,351],"Detector),":[339],"RSSD":[340],"(Rainbow":[341],"FSSD":[347],"(Fusion":[348],"Detector).":[352]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
