{"id":"https://openalex.org/W7117256108","doi":"https://doi.org/10.1109/tip.2025.3646455","title":"Multi-Stage Group Interaction and Cross-Domain Fusion Network for Real-Time Smoke Segmentation","display_name":"Multi-Stage Group Interaction and Cross-Domain Fusion Network for Real-Time Smoke Segmentation","publication_year":2025,"publication_date":"2025-12-25","ids":{"openalex":"https://openalex.org/W7117256108","doi":"https://doi.org/10.1109/tip.2025.3646455","pmid":"https://pubmed.ncbi.nlm.nih.gov/41447492"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2025.3646455","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2025.3646455","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5087022827","display_name":"K. L. Li","orcid":null},"institutions":[{"id":"https://openalex.org/I21945476","display_name":"Shanghai Normal University","ror":"https://ror.org/01cxqmw89","country_code":"CN","type":"education","lineage":["https://openalex.org/I21945476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kang Li","raw_affiliation_strings":["College of Mathematics and Science, Shanghai Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"College of Mathematics and Science, Shanghai Normal University, Shanghai, China","institution_ids":["https://openalex.org/I21945476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121293582","display_name":"Feiniu Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I21945476","display_name":"Shanghai Normal University","ror":"https://ror.org/01cxqmw89","country_code":"CN","type":"education","lineage":["https://openalex.org/I21945476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feiniu Yuan","raw_affiliation_strings":["College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China","institution_ids":["https://openalex.org/I21945476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068663623","display_name":"C M Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I21945476","display_name":"Shanghai Normal University","ror":"https://ror.org/01cxqmw89","country_code":"CN","type":"education","lineage":["https://openalex.org/I21945476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunmei Wang","raw_affiliation_strings":["College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China","institution_ids":["https://openalex.org/I21945476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046173201","display_name":"Chunli Meng","orcid":"https://orcid.org/0000-0002-9909-7492"},"institutions":[{"id":"https://openalex.org/I21945476","display_name":"Shanghai Normal University","ror":"https://ror.org/01cxqmw89","country_code":"CN","type":"education","lineage":["https://openalex.org/I21945476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunli Meng","raw_affiliation_strings":["College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China","institution_ids":["https://openalex.org/I21945476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5087022827"],"corresponding_institution_ids":["https://openalex.org/I21945476"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6550371,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"35","issue":null,"first_page":"124","last_page":"135"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9927999973297119,"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":0.9927999973297119,"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/T11019","display_name":"Image Enhancement Techniques","score":0.0017000000225380063,"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/T11317","display_name":"Fire dynamics and safety research","score":0.000699999975040555,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.638700008392334},{"id":"https://openalex.org/keywords/smoke","display_name":"Smoke","score":0.5748999714851379},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4796000123023987},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4507000148296356},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.40149998664855957},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37720000743865967},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.3474999964237213},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3384000062942505},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.31839999556541443}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7129999995231628},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.638700008392334},{"id":"https://openalex.org/C58874564","wikidata":"https://www.wikidata.org/wiki/Q130768","display_name":"Smoke","level":2,"score":0.5748999714851379},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49540001153945923},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4796000123023987},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4507000148296356},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.40149998664855957},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.375},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.3474999964237213},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3176000118255615},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.2815999984741211},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C95491727","wikidata":"https://www.wikidata.org/wiki/Q992968","display_name":"Mobile telephony","level":3,"score":0.267300009727478},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2630000114440918},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C106516650","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm design","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2025.3646455","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2025.3646455","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:41447492","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41447492","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.5711872577667236,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2040386797","https://openalex.org/W2084391620","https://openalex.org/W2344001629","https://openalex.org/W2741866663","https://openalex.org/W2767352048","https://openalex.org/W2794499044","https://openalex.org/W2797373405","https://openalex.org/W2798405286","https://openalex.org/W2886934227","https://openalex.org/W2892068485","https://openalex.org/W2906161342","https://openalex.org/W2944039629","https://openalex.org/W2951202157","https://openalex.org/W2962858109","https://openalex.org/W2963155258","https://openalex.org/W2963163009","https://openalex.org/W2963446712","https://openalex.org/W2980784832","https://openalex.org/W2998449272","https://openalex.org/W3105393511","https://openalex.org/W3119132889","https://openalex.org/W3158505286","https://openalex.org/W3164816468","https://openalex.org/W3196904463","https://openalex.org/W3197715576","https://openalex.org/W3199537137","https://openalex.org/W4205231989","https://openalex.org/W4210537330","https://openalex.org/W4282838841","https://openalex.org/W4289752563","https://openalex.org/W4297094920","https://openalex.org/W4313527340","https://openalex.org/W4313550878","https://openalex.org/W4316660778","https://openalex.org/W4376607931","https://openalex.org/W4378628329","https://openalex.org/W4386076267","https://openalex.org/W4386385278","https://openalex.org/W4388821320","https://openalex.org/W4390872447","https://openalex.org/W4391547724","https://openalex.org/W4393159984","https://openalex.org/W4394859977","https://openalex.org/W4400012626","https://openalex.org/W4402979335"],"related_works":[],"abstract_inverted_index":{"Lightweight":[0],"smoke":[1,21,55,65,86,97,126,146],"image":[2],"segmentation":[3,22],"is":[4],"essential":[5],"for":[6,34,53,82],"fire":[7],"warning":[8],"systems,":[9],"particularly":[10],"on":[11,194,211],"mobile":[12,35],"devices.":[13],"In":[14],"recent":[15],"years,":[16],"although":[17],"numerous":[18],"high-precision,":[19],"large-scale":[20],"models":[23],"have":[24],"been":[25],"developed,":[26],"there":[27],"are":[28],"few":[29],"lightweight":[30,85],"solutions":[31],"specifically":[32],"designed":[33],"applications.":[36],"Therefore,":[37],"we":[38,67,101,128,149],"propose":[39],"a":[40,69,84,103,151,159,190,204,208],"Multi-stage":[41,104],"Group":[42,105,152,160],"Interaction":[43,71,106],"and":[44,78,117,158,168,189,207,222],"Cross-domain":[45,70],"Fusion":[46,161],"Network":[47],"(MGICFN)":[48],"with":[49],"low":[50],"computational":[51],"complexity":[52],"real-time":[54],"segmentation.":[56],"To":[57,88,120],"improve":[58],"the":[59,90,112,122,166,195,212],"model's":[60],"ability":[61],"to":[62,75,143,164],"effectively":[63],"analyze":[64],"features,":[66],"incorporate":[68],"Attention":[72,155],"Module":[73,107,133,156,162],"(CIAM)":[74],"merge":[76],"spatial":[77],"frequency":[79],"domain":[80],"features":[81],"creating":[83],"encoder.":[87],"alleviate":[89],"loss":[91],"of":[92,125],"critical":[93],"information":[94,113,124],"from":[95],"small":[96],"objects":[98],"during":[99],"downsampling,":[100],"design":[102],"(MGIM).":[108],"The":[109],"MGIM":[110],"calibrates":[111],"discrepancies":[114],"between":[115],"high":[116],"low-dimensional":[118],"features.":[119,147],"enhance":[121],"boundary":[123],"targets,":[127],"introduce":[129],"an":[130,177,182,201],"Edge":[131],"Enhancement":[132],"(EEM),":[134],"which":[135],"utilizes":[136],"predicted":[137],"target":[138],"boundaries":[139],"as":[140],"advanced":[141],"guidance":[142],"refine":[144],"lower-level":[145],"Furthermore,":[148],"implement":[150],"Convolutional":[153],"Block":[154],"(GCBAM)":[157],"(GFM)":[163],"connect":[165],"encoder":[167],"decoder":[169],"efficiently.":[170],"Experimental":[171],"results":[172],"demonstrate":[173],"that":[174],"MGICFN":[175,217],"achieves":[176,200],"88.70%":[178],"Dice":[179],"coefficient":[180],"(Dice),":[181],"81.16%":[183],"mean":[184],"Intersection":[185],"over":[186],"Union":[187],"(mIoU),":[188],"91.93%":[191],"accuracy":[192],"(Acc)":[193],"SFS3K":[196],"dataset.":[197,215],"It":[198],"also":[199],"87.30%":[202],"Dice,":[203],"78.68%":[205],"mIoU,":[206],"92.95%":[209],"Acc":[210],"SYN70K":[213],"test":[214],"Our":[216],"model":[218],"has":[219],"0.73M":[220],"parameters":[221],"requires":[223],"0.3G":[224],"FLOPs.":[225]},"counts_by_year":[],"updated_date":"2026-02-23T20:09:44.859080","created_date":"2025-12-25T00:00:00"}
