{"id":"https://openalex.org/W4416252256","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227605","title":"A Weighting Loss Approach for Transformer-Based Object Detection","display_name":"A Weighting Loss Approach for Transformer-Based Object Detection","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416252256","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227605"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227605","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227605","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/A5094359357","display_name":"Matthaios Dimitrios Tzimas","orcid":"https://orcid.org/0009-0002-8989-8330"},"institutions":[{"id":"https://openalex.org/I21370196","display_name":"Aristotle University of Thessaloniki","ror":"https://ror.org/02j61yw88","country_code":"GR","type":"education","lineage":["https://openalex.org/I21370196"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Matthaios D. Tzimas","raw_affiliation_strings":["Aristotle University of Thessaloniki,Department of Informatics,Greece"],"affiliations":[{"raw_affiliation_string":"Aristotle University of Thessaloniki,Department of Informatics,Greece","institution_ids":["https://openalex.org/I21370196"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082427939","display_name":"Vasileios Mygdalis","orcid":"https://orcid.org/0000-0001-5473-5262"},"institutions":[{"id":"https://openalex.org/I21370196","display_name":"Aristotle University of Thessaloniki","ror":"https://ror.org/02j61yw88","country_code":"GR","type":"education","lineage":["https://openalex.org/I21370196"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Vasileios Mygdalis","raw_affiliation_strings":["Aristotle University of Thessaloniki,Department of Informatics,Greece"],"affiliations":[{"raw_affiliation_string":"Aristotle University of Thessaloniki,Department of Informatics,Greece","institution_ids":["https://openalex.org/I21370196"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061130224","display_name":"Ioannis Pitas","orcid":"https://orcid.org/0009-0006-7555-8641"},"institutions":[{"id":"https://openalex.org/I21370196","display_name":"Aristotle University of Thessaloniki","ror":"https://ror.org/02j61yw88","country_code":"GR","type":"education","lineage":["https://openalex.org/I21370196"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Ioannis Pitas","raw_affiliation_strings":["Aristotle University of Thessaloniki,Department of Informatics,Greece"],"affiliations":[{"raw_affiliation_string":"Aristotle University of Thessaloniki,Department of Informatics,Greece","institution_ids":["https://openalex.org/I21370196"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5094359357"],"corresponding_institution_ids":["https://openalex.org/I21370196"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40898137,"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":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.0026000000070780516,"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/T11019","display_name":"Image Enhancement Techniques","score":0.0017999999690800905,"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/object-detection","display_name":"Object detection","score":0.7914999723434448},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5741000175476074},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.5134999752044678},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.4864000082015991},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.46869999170303345},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.46709999442100525},{"id":"https://openalex.org/keywords/fire-detection","display_name":"Fire detection","score":0.4666000008583069},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4242999851703644},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4002000093460083},{"id":"https://openalex.org/keywords/a-weighting","display_name":"A-weighting","score":0.3917999863624573}],"concepts":[{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7914999723434448},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6844000220298767},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6074000000953674},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5741000175476074},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.5134999752044678},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.4864000082015991},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.46709999442100525},{"id":"https://openalex.org/C2780836893","wikidata":"https://www.wikidata.org/wiki/Q19922674","display_name":"Fire detection","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4242999851703644},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C70136482","wikidata":"https://www.wikidata.org/wiki/Q13583781","display_name":"A-weighting","level":3,"score":0.3917999863624573},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3325999975204468},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31949999928474426},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.31139999628067017},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C3019361169","wikidata":"https://www.wikidata.org/wiki/Q2609467","display_name":"Detection threshold","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.2903999984264374},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2890999913215637},{"id":"https://openalex.org/C293773","wikidata":"https://www.wikidata.org/wiki/Q7608015","display_name":"Step detection","level":3,"score":0.2883000075817108},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C71681937","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object-class detection","level":5,"score":0.26109999418258667},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C2776088427","wikidata":"https://www.wikidata.org/wiki/Q378859","display_name":"Pattern detection","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.25870001316070557},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.2583000063896179},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227605","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227605","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":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2570343428","https://openalex.org/W2733381601","https://openalex.org/W2962766617","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W3096609285","https://openalex.org/W3138516171","https://openalex.org/W3198895189","https://openalex.org/W3208645658","https://openalex.org/W4283716939","https://openalex.org/W4292299509","https://openalex.org/W4303644807","https://openalex.org/W4312312588","https://openalex.org/W4312785838","https://openalex.org/W4319297957","https://openalex.org/W4362703401","https://openalex.org/W4382467962","https://openalex.org/W4385245566","https://openalex.org/W4385342336","https://openalex.org/W4386076325","https://openalex.org/W4390873840","https://openalex.org/W4393868968","https://openalex.org/W4402351415"],"related_works":[],"abstract_inverted_index":{"This":[0,38,141],"paper":[1],"introduces":[2],"a":[3,28],"training":[4,25],"loss":[5,106],"function":[6],"tailored":[7],"for":[8,32,151],"object":[9,91],"detection":[10,44,48,63,82,92,121,153,167],"in":[11,19,47],"transformer-based":[12,149],"architectures.":[13,114],"Our":[14,115],"approach":[15],"addresses":[16],"the":[17,33,57,70,101,104,118,146,159],"imbalance":[18],"ground-truth":[20],"bounding":[21,51],"box":[22],"sizes":[23,55],"during":[24],"by":[26],"implementing":[27],"coordinate-based":[29],"error-weighting":[30],"mechanism":[31],"L<inf":[34],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[35],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</inf>":[36],"loss.":[37],"modification":[39],"stabilizes":[40],"optimization":[41],"and":[42,84,164],"enhances":[43,145],"performance,":[45],"particularly":[46],"problems":[49],"requiring":[50],"boxes":[52],"of":[53,103,123,148,161],"varying":[54],"within":[56],"same":[58],"image,":[59],"such":[60,94],"as":[61,95],"fire/smoke":[62,81],"applications.":[64],"By":[65],"integrating":[66],"this":[67],"method":[68,128],"into":[69,111],"Real-Time":[71],"Detection":[72],"Transformer":[73],"(RT-DETR),":[74],"we":[75,108],"conduct":[76],"extensive":[77],"experiments":[78,116],"across":[79,129],"three":[80,131],"datasets":[83,132],"compare":[85],"our":[86,127],"findings":[87],"against":[88],"leading":[89],"real-time":[90,152],"algorithms,":[93],"YOLO":[96],"models.":[97],"To":[98],"further":[99],"validate":[100],"generalizability":[102],"proposed":[105],"function,":[107],"incorporate":[109],"it":[110],"various":[112],"DETR-based":[113],"demonstrate":[117],"superior":[119],"fire":[120,166],"accuracy":[122],"RT-DETR":[124],"trained":[125],"with":[126],"all":[130],"while":[133],"ensuring":[134],"its":[135],"effectiveness":[136],"on":[137],"more":[138,162],"complex":[139],"datasets.":[140],"study":[142],"not":[143],"only":[144],"capabilities":[147],"architectures":[150],"tasks":[154],"but":[155],"also":[156],"contributes":[157],"to":[158],"development":[160],"efficient":[163],"reliable":[165],"systems.":[168]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}
