{"id":"https://openalex.org/W4415398536","doi":"https://doi.org/10.1109/spml66318.2025.11199805","title":"InRFA-YOLOv11-CD: A Contraband Detection Method Based on InceptionNeXt-RFA Interaction for Security Inspection","display_name":"InRFA-YOLOv11-CD: A Contraband Detection Method Based on InceptionNeXt-RFA Interaction for Security Inspection","publication_year":2025,"publication_date":"2025-07-15","ids":{"openalex":"https://openalex.org/W4415398536","doi":"https://doi.org/10.1109/spml66318.2025.11199805"},"language":null,"primary_location":{"id":"doi:10.1109/spml66318.2025.11199805","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spml66318.2025.11199805","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 8th International Conference on Signal Processing and Machine Learning (SPML)","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/A5120083921","display_name":"Fanyi Kong","orcid":null},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanyi Kong","raw_affiliation_strings":["School of Electrical and Control Engineering, Shenyang Jianzhu University,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Control Engineering, Shenyang Jianzhu University,Shenyang,China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100764049","display_name":"Dongming Liu","orcid":"https://orcid.org/0000-0001-6382-3168"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongming Liu","raw_affiliation_strings":["School of Electrical and Control Engineering, Shenyang Jianzhu University,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Control Engineering, Shenyang Jianzhu University,Shenyang,China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100755084","display_name":"Hui Cao","orcid":"https://orcid.org/0000-0002-2151-4903"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Cao","raw_affiliation_strings":["School of Electrical and Control Engineering, Shenyang Jianzhu University,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Control Engineering, Shenyang Jianzhu University,Shenyang,China","institution_ids":["https://openalex.org/I83714178"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I83714178"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24628124,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"130","last_page":"135"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9750000238418579,"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"}},"topics":[{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9750000238418579,"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/robustness","display_name":"Robustness (evolution)","score":0.736299991607666},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.586899995803833},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.47110000252723694},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4244999885559082},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.42100000381469727},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.41620001196861267},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.40459999442100525}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.736299991607666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.661300003528595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6064000129699707},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.586899995803833},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4975000023841858},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.47110000252723694},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.42100000381469727},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.41620001196861267},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.40459999442100525},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.39800000190734863},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3546000123023987},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.3296999931335449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27639999985694885},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.27250000834465027},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C178148461","wikidata":"https://www.wikidata.org/wiki/Q1632136","display_name":"Security controls","level":3,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/spml66318.2025.11199805","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spml66318.2025.11199805","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 8th International Conference on Signal Processing and Machine Learning (SPML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7027439274","display_name":null,"funder_award_id":"2024-BSLH-258","funder_id":"https://openalex.org/F4320323086","funder_display_name":"Natural Science Foundation of Liaoning Province"}],"funders":[{"id":"https://openalex.org/F4320323086","display_name":"Natural Science Foundation of Liaoning Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"study,":[2],"we":[3],"propose":[4],"InRFA-YOLOv11-CD,":[5],"an":[6],"advanced":[7],"deep":[8],"learning":[9],"model":[10,28,130],"designed":[11],"to":[12,43],"enhance":[13],"X-ray":[14],"contraband":[15,144],"detection":[16,57],"in":[17,100,131],"security":[18,133],"inspection":[19],"scenarios.":[20],"Building":[21],"on":[22,68],"the":[23,30,40,126,129],"foundation":[24],"of":[25,83,87,92,128],"YOLOv11,":[26],"our":[27],"incorporates":[29],"InRFA":[31],"block,":[32],"which":[33],"integrates":[34],"Receptive-Field":[35],"Attention":[36],"Convolution":[37],"(RFAConv)":[38],"and":[39,48,62,90,117],"InceptionNeXt":[41],"block":[42],"improve":[44],"multi-scale":[45],"feature":[46],"extraction":[47],"dynamic":[49],"receptive":[50],"field":[51],"adaptation.":[52],"This":[53],"innovation":[54],"significantly":[55],"boosts":[56],"performance":[58],"for":[59,142],"small,":[60],"occluded,":[61],"complex":[63,132],"objects.":[64],"Experimental":[65],"evaluations":[66],"conducted":[67],"a":[69,81,139],"dataset":[70],"comprising":[71],"10,000":[72],"images":[73],"demonstrate":[74],"that":[75],"InRFA-YOLOv11-CD":[76],"outperforms":[77],"state-of-the-art":[78],"models,":[79],"achieving":[80],"precision":[82],"92.7":[84],"%,":[85,89],"recall":[86],"91.3":[88],"mAP50:95":[91],"65.7":[93],"%.":[94],"Notably,":[95],"significant":[96],"improvements":[97],"are":[98],"observed":[99],"detecting":[101],"lighters":[102],"<tex":[103,108,113,119],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[104,109,114,120],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$(+4.0":[105],"\\%)$</tex>,":[106,111,116],"knives":[107],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$(+2.6":[110],"scissors":[112],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$(+9.0":[115],"screwdrivers":[118],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$(+2.9":[121],"\\%)$</tex>.":[122],"These":[123],"results":[124],"affirm":[125],"robustness":[127],"screening":[134],"environments,":[135],"positioning":[136],"it":[137],"as":[138],"promising":[140],"solution":[141],"real-world":[143],"detection.":[145]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-22T00:00:00"}