{"id":"https://openalex.org/W4402916108","doi":"https://doi.org/10.1109/icip51287.2024.10647443","title":"A Context-Oriented Multi-Scale Neural Network for Fire Segmentation","display_name":"A Context-Oriented Multi-Scale Neural Network for Fire Segmentation","publication_year":2024,"publication_date":"2024-09-27","ids":{"openalex":"https://openalex.org/W4402916108","doi":"https://doi.org/10.1109/icip51287.2024.10647443"},"language":"en","primary_location":{"id":"doi:10.1109/icip51287.2024.10647443","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip51287.2024.10647443","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","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/A5051547647","display_name":"Tony Zhang","orcid":"https://orcid.org/0009-0009-0288-8270"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tony Zhang","raw_affiliation_strings":["University of Michigan,Electrical and Computer Engineering Department,Ann Arbor,MI,USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan,Electrical and Computer Engineering Department,Ann Arbor,MI,USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068224304","display_name":"Robert P. Dick","orcid":"https://orcid.org/0000-0001-5428-9530"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert P. Dick","raw_affiliation_strings":["University of Michigan,Electrical and Computer Engineering Department,Ann Arbor,MI,USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan,Electrical and Computer Engineering Department,Ann Arbor,MI,USA","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051547647"],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":0.7945,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71725157,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1343","last_page":"1349"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9988999962806702,"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":0.9988999962806702,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9039999842643738,"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/computer-science","display_name":"Computer science","score":0.7158803939819336},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6426335573196411},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6146115660667419},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5490777492523193},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4875440299510956},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4340006709098816},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.18781733512878418},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.08210378885269165},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07850480079650879}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7158803939819336},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6426335573196411},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6146115660667419},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5490777492523193},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4875440299510956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4340006709098816},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.18781733512878418},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.08210378885269165},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07850480079650879},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip51287.2024.10647443","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip51287.2024.10647443","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1518544806","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1996037078","https://openalex.org/W2089926733","https://openalex.org/W2099386276","https://openalex.org/W2111586857","https://openalex.org/W2142333006","https://openalex.org/W2194775991","https://openalex.org/W2412782625","https://openalex.org/W2479109623","https://openalex.org/W2560023338","https://openalex.org/W2611171851","https://openalex.org/W2733381601","https://openalex.org/W2751420734","https://openalex.org/W2780222614","https://openalex.org/W2807862495","https://openalex.org/W2895340641","https://openalex.org/W2955058313","https://openalex.org/W2956265248","https://openalex.org/W2963727650","https://openalex.org/W2981689412","https://openalex.org/W3038510778","https://openalex.org/W3047385447","https://openalex.org/W3100742769","https://openalex.org/W3119287538","https://openalex.org/W3175849542","https://openalex.org/W3196904463","https://openalex.org/W3202035629","https://openalex.org/W4312443924","https://openalex.org/W6746796840","https://openalex.org/W6754879843"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W2391251536","https://openalex.org/W2501551404","https://openalex.org/W4385583601","https://openalex.org/W4298131179","https://openalex.org/W2113201962","https://openalex.org/W2362198218","https://openalex.org/W1982750869","https://openalex.org/W2019521278"],"abstract_inverted_index":{"Existing":[0],"image-based":[1],"fire":[2,38,101,125],"segmentation":[3],"techniques":[4],"use":[5],"convolutional":[6],"neural":[7,55],"networks":[8],"to":[9,59,93],"handle":[10],"complicated":[11],"scenes.":[12],"Such":[13],"approaches":[14],"perform":[15],"poorly":[16],"when":[17,23],"flame":[18],"sizes":[19],"vary":[20],"greatly":[21],"and":[22,65,102],"backgrounds":[24],"are":[25],"complex.":[26],"In":[27],"this":[28],"paper,":[29],"we":[30],"describe":[31,69],"a":[32,42,70,113],"novel":[33],"Context-Oriented":[34,71],"Multi-Scale":[35],"Network":[36],"for":[37],"segmentation.":[39],"We":[40,67],"construct":[41],"multi-scale":[43],"aggregation":[44],"module":[45],"that":[46,96,108],"combines":[47],"semantic":[48],"information":[49],"at":[50],"different":[51,63],"levels":[52],"in":[53,57,87,91],"the":[54,75,79,88],"network":[56,80],"order":[58,92],"recognize":[60],"fires":[61],"with":[62],"shapes":[64],"sizes.":[66],"also":[68],"Module,":[72],"which":[73],"increases":[74],"receptive":[76],"field":[77],"of":[78,84],"by":[81],"utilizing":[82],"relationships":[83],"all":[85],"pixels":[86],"feature":[89],"map":[90],"obtain":[94],"features":[95],"more":[97],"effectively":[98],"discriminate":[99],"between":[100],"non-fire":[103],"pixels.":[104],"Experimental":[105],"results":[106],"demonstrate":[107],"our":[109],"proposed":[110],"model":[111],"has":[112],"$2.7":[114],"\\%$":[115],"higher":[116],"mean":[117],"Intersection":[118],"over":[119],"Union":[120],"(mIoU)":[121],"accuracy":[122],"than":[123],"previous":[124],"detection":[126],"methods.":[127]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}