{"id":"https://openalex.org/W7131113102","doi":"https://doi.org/10.1109/iccvw69036.2025.00232","title":"TY-RIST: Tactical YOLO Tricks for Real-Time Infrared Small Target Detection","display_name":"TY-RIST: Tactical YOLO Tricks for Real-Time Infrared Small Target Detection","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7131113102","doi":"https://doi.org/10.1109/iccvw69036.2025.00232"},"language":null,"primary_location":{"id":"doi:10.1109/iccvw69036.2025.00232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","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/A5126716986","display_name":"Abdulkarim Atrash","orcid":null},"institutions":[{"id":"https://openalex.org/I201799495","display_name":"Middle East Technical University","ror":"https://ror.org/014weej12","country_code":"TR","type":"education","lineage":["https://openalex.org/I201799495"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Abdulkarim Atrash","raw_affiliation_strings":["Middle East Technical University,Turkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Middle East Technical University,Turkiye","institution_ids":["https://openalex.org/I201799495"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Seyda Ertekin","orcid":null},"institutions":[{"id":"https://openalex.org/I201799495","display_name":"Middle East Technical University","ror":"https://ror.org/014weej12","country_code":"TR","type":"education","lineage":["https://openalex.org/I201799495"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Seyda Ertekin","raw_affiliation_strings":["Middle East Technical University,Turkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Middle East Technical University,Turkiye","institution_ids":["https://openalex.org/I201799495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017077810","display_name":"\u00d6m\u00fcr U\u011fur","orcid":"https://orcid.org/0000-0001-9348-7775"},"institutions":[{"id":"https://openalex.org/I201799495","display_name":"Middle East Technical University","ror":"https://ror.org/014weej12","country_code":"TR","type":"education","lineage":["https://openalex.org/I201799495"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"\u00d6m\u00fcr U\u011fur","raw_affiliation_strings":["Middle East Technical University,Turkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Middle East Technical University,Turkiye","institution_ids":["https://openalex.org/I201799495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053010867","display_name":"Omar Moured","orcid":"https://orcid.org/0000-0003-4227-8417"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Omar Moured","raw_affiliation_strings":["Karlsruhe Institute of Technology,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology,Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100628847","display_name":"Yufan Chen","orcid":"https://orcid.org/0000-0001-7989-3283"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Yufan Chen","raw_affiliation_strings":["Karlsruhe Institute of Technology,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology,Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126705081","display_name":"Jiaming Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jiaming Zhang","raw_affiliation_strings":["Karlsruhe Institute of Technology,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology,Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.907,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.9556248,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2222","last_page":"2231"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.6085000038146973,"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":0.6085000038146973,"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/T11637","display_name":"Advanced Semiconductor Detectors and Materials","score":0.16519999504089355,"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.028599999845027924,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.664900004863739},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.44510000944137573},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42419999837875366},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.39660000801086426},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.38280001282691956},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.37860000133514404},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.3783999979496002}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7177000045776367},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6741999983787537},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.664900004863739},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.487199991941452},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.44510000944137573},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42419999837875366},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.39660000801086426},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.38280001282691956},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3783999979496002},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3686000108718872},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.35839998722076416},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C2779726219","wikidata":"https://www.wikidata.org/wiki/Q7685884","display_name":"Target acquisition","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C2987098735","wikidata":"https://www.wikidata.org/wiki/Q3808900","display_name":"Recall rate","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.258899986743927}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvw69036.2025.00232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","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":47,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1988122332","https://openalex.org/W2035568192","https://openalex.org/W2544366167","https://openalex.org/W2594239676","https://openalex.org/W2752782242","https://openalex.org/W3010079414","https://openalex.org/W3039443125","https://openalex.org/W3118249006","https://openalex.org/W3124866053","https://openalex.org/W3171950886","https://openalex.org/W3177052299","https://openalex.org/W3187636574","https://openalex.org/W3194790201","https://openalex.org/W3207628311","https://openalex.org/W4220827371","https://openalex.org/W4285406065","https://openalex.org/W4285610218","https://openalex.org/W4289752563","https://openalex.org/W4313065862","https://openalex.org/W4313506322","https://openalex.org/W4313855945","https://openalex.org/W4317794940","https://openalex.org/W4319265540","https://openalex.org/W4323262682","https://openalex.org/W4372260328","https://openalex.org/W4388756758","https://openalex.org/W4389430742","https://openalex.org/W4390603723","https://openalex.org/W4391020229","https://openalex.org/W4391943446","https://openalex.org/W4394597260","https://openalex.org/W4394698940","https://openalex.org/W4398255795","https://openalex.org/W4399598133","https://openalex.org/W4400438626","https://openalex.org/W4402733568","https://openalex.org/W4402979889","https://openalex.org/W4403413348","https://openalex.org/W4406331864","https://openalex.org/W4406525418","https://openalex.org/W4406739753","https://openalex.org/W4406999451","https://openalex.org/W4408992192","https://openalex.org/W4409366328","https://openalex.org/W4411175553","https://openalex.org/W4412444079"],"related_works":[],"abstract_inverted_index":{"While":[0],"critical":[1],"for":[2],"defense":[3],"and":[4,35,69,86,108,124,152],"surveillance,":[5],"infrared":[6],"small":[7],"target":[8,18],"detection":[9,62],"(IRSTD)":[10],"remains":[11],"a":[12,52,60,71,136,142],"challenging":[13],"task":[14],"due":[15],"to:":[16],"(1)":[17,51],"loss":[19],"from":[20,32],"minimal":[21],"features,":[22],"(2)":[23,59],"false":[24],"alarms":[25],"in":[26],"cluttered":[27],"environments,":[28],"(3)":[29,64],"missed":[30],"detections":[31],"low":[33],"saliency,":[34],"(4)":[36,70],"high":[37],"computational":[38,77],"costs.":[39],"To":[40],"address":[41],"these,":[42],"we":[43,91],"propose":[44],"TY-RIST,":[45],"an":[46],"optimized":[47],"YOLOv12n":[48],"architecture":[49],"featuring:":[50],"stride-aware":[53],"backbone":[54],"with":[55],"fine-grained":[56],"receptive":[57],"fields,":[58],"high-resolution":[61],"head,":[63],"cascaded":[65],"coordinate":[66],"attention":[67],"blocks,":[68],"branch":[72],"pruning":[73],"strategy":[74],"that":[75],"reduces":[76],"cost":[78],"up":[79,131],"to":[80,99,132],"~25.5%":[81],"while":[82,129],"marginally":[83],"enhancing":[84],"performance":[85],"enabling":[87],"real-time":[88],"inference.":[89],"Additionally,":[90],"incorporate":[92],"the":[93],"Normalized":[94],"Gaussian":[95],"Wasserstein":[96],"Distance":[97],"(NWD)":[98],"improve":[100],"regression":[101],"stability.":[102],"Extensive":[103],"experiments":[104],"on":[105,135,141],"four":[106],"benchmarks":[107],"across":[109],"20":[110],"different":[111],"models":[112],"demonstrate":[113],"state-of-the-art":[114],"performance,":[115],"boosting":[116],"mAP@50":[117],"by":[118,122,126],"+":[119,127],"7.9%,":[120],"Precision":[121],"+3%,":[123],"Recall":[125],"10.2%,":[128],"running":[130],"~123":[133],"FPS":[134],"single":[137],"GPU.":[138],"Cross-dataset":[139],"validation":[140],"fifth":[143],"dataset":[144],"further":[145],"confirms":[146],"strong":[147],"generalization":[148],"capability.":[149],"Further":[150],"results":[151],"details":[153],"are":[154],"published":[155],"at":[156],"www.github.com/moured/TY-RIST.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-26T00:00:00"}