{"id":"https://openalex.org/W4415952374","doi":"https://doi.org/10.3390/sym17111886","title":"Research on Multi-Dimensional Detection Method for Black Smoke Emission of Diesel Vehicles Based on Deep Learning","display_name":"Research on Multi-Dimensional Detection Method for Black Smoke Emission of Diesel Vehicles Based on Deep Learning","publication_year":2025,"publication_date":"2025-11-06","ids":{"openalex":"https://openalex.org/W4415952374","doi":"https://doi.org/10.3390/sym17111886"},"language":"en","primary_location":{"id":"doi:10.3390/sym17111886","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17111886","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/sym17111886","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100451242","display_name":"Bing Li","orcid":"https://orcid.org/0000-0002-1258-976X"},"institutions":[{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bing Li","raw_affiliation_strings":["College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China"],"affiliations":[{"raw_affiliation_string":"College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China","institution_ids":["https://openalex.org/I47689461"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053112608","display_name":"Xin Xu","orcid":"https://orcid.org/0000-0003-3238-745X"},"institutions":[{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Xu","raw_affiliation_strings":["College of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China"],"affiliations":[{"raw_affiliation_string":"College of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China","institution_ids":["https://openalex.org/I47689461"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091622954","display_name":"Meng Zhang","orcid":"https://orcid.org/0000-0002-8894-2507"},"institutions":[{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Zhang","raw_affiliation_strings":["College of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China"],"affiliations":[{"raw_affiliation_string":"College of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China","institution_ids":["https://openalex.org/I47689461"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100451242"],"corresponding_institution_ids":["https://openalex.org/I47689461"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39128901,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"11","first_page":"1886","last_page":"1886"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9815000295639038,"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":0.9815000295639038,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.002099999925121665,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6958000063896179},{"id":"https://openalex.org/keywords/smoke","display_name":"Smoke","score":0.6251999735832214},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6090999841690063},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5685999989509583},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5619000196456909},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.5103999972343445},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47780001163482666},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.454800009727478},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.43540000915527344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.727400004863739},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6958000063896179},{"id":"https://openalex.org/C58874564","wikidata":"https://www.wikidata.org/wiki/Q130768","display_name":"Smoke","level":2,"score":0.6251999735832214},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6090999841690063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5716000199317932},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5685999989509583},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5619000196456909},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.5103999972343445},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47780001163482666},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.454800009727478},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.43540000915527344},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4325000047683716},{"id":"https://openalex.org/C138171918","wikidata":"https://www.wikidata.org/wiki/Q38423","display_name":"Diesel fuel","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.33230000734329224},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C22507642","wikidata":"https://www.wikidata.org/wiki/Q1069369","display_name":"Hazardous waste","level":2,"score":0.3208000063896179},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3052000105381012},{"id":"https://openalex.org/C2780804531","wikidata":"https://www.wikidata.org/wiki/Q174174","display_name":"Diesel engine","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.28360000252723694},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.2660999894142151},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.25529998540878296},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.25119999051094055},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/sym17111886","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17111886","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/sym17111886","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17111886","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4796852975","display_name":null,"funder_award_id":"2017YFC0803901-2","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8753556766","display_name":null,"funder_award_id":"E2017001","funder_id":"https://openalex.org/F4320323085","funder_display_name":"Natural Science Foundation of Heilongjiang Province"}],"funders":[{"id":"https://openalex.org/F4320323085","display_name":"Natural Science Foundation of Heilongjiang Province","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2081140177","https://openalex.org/W2193145675","https://openalex.org/W2252355370","https://openalex.org/W2344001629","https://openalex.org/W2565639579","https://openalex.org/W2751420734","https://openalex.org/W2756554574","https://openalex.org/W2768959465","https://openalex.org/W2807862495","https://openalex.org/W2893520743","https://openalex.org/W2894901185","https://openalex.org/W2914763211","https://openalex.org/W2922724427","https://openalex.org/W2954800225","https://openalex.org/W2956265248","https://openalex.org/W2962766617","https://openalex.org/W2963857746","https://openalex.org/W2964169840","https://openalex.org/W2997747012","https://openalex.org/W3034971973","https://openalex.org/W3170256812","https://openalex.org/W3180134609","https://openalex.org/W3192555868","https://openalex.org/W4200053652","https://openalex.org/W4220719423","https://openalex.org/W4309091338","https://openalex.org/W4372342717","https://openalex.org/W4396906627","https://openalex.org/W4403770406","https://openalex.org/W4411977814","https://openalex.org/W4412460023","https://openalex.org/W4412746658","https://openalex.org/W4413958587"],"related_works":[],"abstract_inverted_index":{"Black":[0],"smoke":[1,47,56,139,190],"emitted":[2],"from":[3],"diesel":[4],"vehicles":[5],"contains":[6],"substantial":[7],"amounts":[8],"of":[9,17,45,59,118,126,173,188,204],"hazardous":[10],"substances.":[11],"With":[12],"the":[13,30,41,55,106,124,134,144,160,170,184,192,202,223,251],"increasing":[14],"annual":[15],"levels":[16],"such":[18],"emissions,":[19],"there":[20],"is":[21,37,52],"growing":[22],"concern":[23],"over":[24],"their":[25],"detrimental":[26],"effects":[27],"on":[28,215],"both":[29],"environment":[31],"and":[32,43,81,237],"human":[33],"health.":[34],"Therefore,":[35],"it":[36],"imperative":[38],"to":[39,53,75,136,164,178,206,244,256],"strengthen":[40],"supervision":[42],"control":[44],"black":[46,70,138,189],"emissions.":[48],"An":[49],"effective":[50],"approach":[51],"analyze":[54],"emission":[57],"status":[58],"vehicles.":[60],"Conventional":[61],"object":[62],"detection":[63,94,152],"models":[64],"often":[65],"exhibit":[66],"limitations":[67],"in":[68,230,235,241],"detecting":[69],"smoke,":[71],"including":[72],"challenges":[73],"related":[74],"multi-scale":[76,98],"target":[77],"sizes,":[78],"complex":[79,257],"backgrounds,":[80],"insufficient":[82],"localization":[83,180],"accuracy.":[84],"To":[85,141],"address":[86],"these":[87],"issues,":[88],"this":[89],"study":[90],"proposes":[91],"a":[92,97,111,147,216,227,232,238],"multi-dimensional":[93],"algorithm.":[95],"First,":[96],"feature":[99,150,166],"extraction":[100,125],"method":[101],"was":[102,154,196],"introduced":[103],"by":[104,183],"replacing":[105],"conventional":[107],"C2F":[108],"module":[109],"with":[110],"mechanism":[112],"that":[113,222,250],"employs":[114],"parallel":[115],"convolutional":[116],"kernels":[117],"varying":[119],"sizes.":[120],"This":[121,156,198],"design":[122],"enables":[123],"features":[127],"at":[128],"different":[129,174],"receptive":[130],"fields,":[131],"significantly":[132],"improving":[133],"capability":[135],"capture":[137],"patterns.":[140],"further":[142],"enhance":[143],"network\u2019s":[145],"performance,":[146],"four-layer":[148],"adaptive":[149],"fusion":[151,161],"head":[153],"proposed.":[155],"component":[157],"dynamically":[158],"adjusts":[159],"weights":[162],"assigned":[163],"each":[165],"layer,":[167],"thereby":[168],"leveraging":[169],"unique":[171],"advantages":[172],"hierarchical":[175],"representations.":[176],"Additionally,":[177],"improve":[179],"accuracy":[181],"affected":[182],"highly":[185],"irregular":[186],"shapes":[187],"edges,":[191],"Inner-IoU":[193],"loss":[194,199],"function":[195],"incorporated.":[197],"effectively":[200],"alleviates":[201],"oversensitivity":[203],"CIoU":[205],"bounding":[207],"box":[208],"regression":[209],"near":[210],"image":[211],"boundaries.":[212],"Experiments":[213],"conducted":[214],"custom":[217],"dataset,":[218],"named":[219],"Smoke-X,":[220],"demonstrated":[221],"proposed":[224],"algorithm":[225],"achieves":[226],"4.8%":[228],"increase":[229],"precision,":[231],"5.9%":[233],"improvement":[234],"recall,":[236],"5.6%":[239],"gain":[240],"mAP50,":[242],"compared":[243],"baseline":[245],"methods.":[246],"These":[247],"improvements":[248],"indicate":[249],"model":[252],"exhibits":[253],"stronger":[254],"adaptability":[255],"environments,":[258],"suggesting":[259],"considerable":[260],"practical":[261],"value":[262],"for":[263],"real-world":[264],"applications.":[265]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-06T00:00:00"}
