{"id":"https://openalex.org/W3097354404","doi":"https://doi.org/10.1109/access.2020.3031683","title":"Applying Convolutional Neural Networks to Detect Natural Gas Leaks in Wellhead Images","display_name":"Applying Convolutional Neural Networks to Detect Natural Gas Leaks in Wellhead Images","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3097354404","doi":"https://doi.org/10.1109/access.2020.3031683","mag":"3097354404"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3031683","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3031683","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09226415.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09226415.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037338233","display_name":"Roberlanio Oliveira Melo","orcid":"https://orcid.org/0000-0001-9831-016X"},"institutions":[{"id":"https://openalex.org/I62885914","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35","country_code":"BR","type":"education","lineage":["https://openalex.org/I62885914"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Roberlanio Oliveira Melo","raw_affiliation_strings":["Federal University of Amazonas, Manaus, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Amazonas, Manaus, Brazil","institution_ids":["https://openalex.org/I62885914"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057891403","display_name":"M. G. F. Costa","orcid":"https://orcid.org/0000-0002-6839-1402"},"institutions":[{"id":"https://openalex.org/I62885914","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35","country_code":"BR","type":"education","lineage":["https://openalex.org/I62885914"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"M. G. F. Costa","raw_affiliation_strings":["Federal University of Amazonas, Manaus, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Amazonas, Manaus, Brazil","institution_ids":["https://openalex.org/I62885914"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004818827","display_name":"C\u00edcero Ferreira Fernandes Costa Filho","orcid":"https://orcid.org/0000-0003-3325-5715"},"institutions":[{"id":"https://openalex.org/I62885914","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35","country_code":"BR","type":"education","lineage":["https://openalex.org/I62885914"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Cicero F. F. Costa Filho","raw_affiliation_strings":["Federal University of Amazonas, Manaus, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Amazonas, Manaus, Brazil","institution_ids":["https://openalex.org/I62885914"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5037338233"],"corresponding_institution_ids":["https://openalex.org/I62885914"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.7163,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.83374171,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"191775","last_page":"191784"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9958999752998352,"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":0.9958999752998352,"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/T12316","display_name":"Oil Spill Detection and Mitigation","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9771999716758728,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/wellhead","display_name":"Wellhead","score":0.8555053472518921},{"id":"https://openalex.org/keywords/natural-gas","display_name":"Natural gas","score":0.7430017590522766},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6407085061073303},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6237679719924927},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4906252920627594},{"id":"https://openalex.org/keywords/gas-leak","display_name":"Gas leak","score":0.46928179264068604},{"id":"https://openalex.org/keywords/petroleum-engineering","display_name":"Petroleum engineering","score":0.4198296070098877},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39441415667533875},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37590402364730835},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.342118501663208},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.24373841285705566},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16145974397659302},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.10841614007949829},{"id":"https://openalex.org/keywords/waste-management","display_name":"Waste management","score":0.09321409463882446}],"concepts":[{"id":"https://openalex.org/C2780424376","wikidata":"https://www.wikidata.org/wiki/Q7981274","display_name":"Wellhead","level":2,"score":0.8555053472518921},{"id":"https://openalex.org/C59427239","wikidata":"https://www.wikidata.org/wiki/Q40858","display_name":"Natural gas","level":2,"score":0.7430017590522766},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6407085061073303},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6237679719924927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4906252920627594},{"id":"https://openalex.org/C2777318586","wikidata":"https://www.wikidata.org/wiki/Q5526351","display_name":"Gas leak","level":2,"score":0.46928179264068604},{"id":"https://openalex.org/C78762247","wikidata":"https://www.wikidata.org/wiki/Q1273174","display_name":"Petroleum engineering","level":1,"score":0.4198296070098877},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39441415667533875},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37590402364730835},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.342118501663208},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.24373841285705566},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16145974397659302},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.10841614007949829},{"id":"https://openalex.org/C548081761","wikidata":"https://www.wikidata.org/wiki/Q180388","display_name":"Waste management","level":1,"score":0.09321409463882446},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3031683","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3031683","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09226415.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:02072d6a36f44a809ba4b7e6f952349b","is_oa":true,"landing_page_url":"https://doaj.org/article/02072d6a36f44a809ba4b7e6f952349b","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 191775-191784 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3031683","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3031683","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09226415.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5199999809265137}],"awards":[],"funders":[{"id":"https://openalex.org/F4320318869","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35"},{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3097354404.pdf","grobid_xml":"https://content.openalex.org/works/W3097354404.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W44815768","https://openalex.org/W162258389","https://openalex.org/W188659239","https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1943054406","https://openalex.org/W1985204936","https://openalex.org/W1993260141","https://openalex.org/W1999571566","https://openalex.org/W2008525336","https://openalex.org/W2082487146","https://openalex.org/W2095705004","https://openalex.org/W2097117768","https://openalex.org/W2149933564","https://openalex.org/W2163605009","https://openalex.org/W2170505850","https://openalex.org/W2194775991","https://openalex.org/W2333700841","https://openalex.org/W2500751094","https://openalex.org/W2591291312","https://openalex.org/W2605495192","https://openalex.org/W2618530766","https://openalex.org/W2763623093","https://openalex.org/W2788633781","https://openalex.org/W2894395197","https://openalex.org/W2896556344","https://openalex.org/W2898605815","https://openalex.org/W2901982721","https://openalex.org/W2905014148","https://openalex.org/W2943036004","https://openalex.org/W2944758627","https://openalex.org/W2945780539","https://openalex.org/W2952203743","https://openalex.org/W2962835968","https://openalex.org/W2962858109","https://openalex.org/W2963163009","https://openalex.org/W2963446712","https://openalex.org/W2964121744","https://openalex.org/W2979202956","https://openalex.org/W2983575492","https://openalex.org/W2988600867","https://openalex.org/W3005533248","https://openalex.org/W3100661288","https://openalex.org/W3121813536","https://openalex.org/W3135557936","https://openalex.org/W4299518610","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6674330103","https://openalex.org/W6682132143","https://openalex.org/W6684191040","https://openalex.org/W6702712380"],"related_works":["https://openalex.org/W3157999811","https://openalex.org/W4296538526","https://openalex.org/W4385608239","https://openalex.org/W2375924075","https://openalex.org/W4285151523","https://openalex.org/W2372238730","https://openalex.org/W3187611183","https://openalex.org/W3118435219","https://openalex.org/W2373061735","https://openalex.org/W4386958473"],"abstract_inverted_index":{"Detecting":[0],"natural":[1,25,50,97,120,143,168],"gas":[2,26,51,98,121,144,169],"leaks":[3,122,170],"is":[4],"one":[5],"of":[6,49,67,88,91,185,191],"the":[7,12,34,44,56,65,84,128,134,178],"most":[8],"important":[9],"measures":[10],"in":[11,43,100,107,123,171],"oil":[13,124],"industry":[14],"for":[15,23,30,58,64,118],"preventing":[16],"accidents.":[17],"The":[18],"literature":[19,45],"provides":[20],"different":[21],"techniques":[22,75],"detecting":[24,119],"leaks.":[27,145],"However,":[28],"except":[29],"previous":[31,71],"studies":[32,72],"by":[33],"authors":[35],"on":[36,46],"this":[37,111,147],"topic,":[38],"there":[39],"remains":[40],"a":[41,78,96,115],"gap":[42],"leak":[47],"detection":[48],"using":[52],"digital":[53],"images,":[54],"without":[55,167],"need":[57],"sensors":[59],"or":[60,86,166],"special":[61],"cameras":[62],"calibrated":[63],"spectrum":[66],"methane":[68],"molecules.":[69],"These":[70],"used":[73],"image-processing":[74],"associated":[76],"with":[77,133,165],"novelty":[79],"filter":[80],"classifier":[81],"to":[82,141,156,163],"detect":[83],"presence":[85],"absence":[87],"visible":[89],"cloud":[90],"hydrocarbon":[92],"vapors,":[93],"that":[94,126,177],"is,":[95],"plume":[99],"Closed":[101],"Circuit":[102],"Television":[103],"(CCTV)":[104],"frames":[105],"installed":[106],"onshore":[108,172],"wellheads.":[109,173],"In":[110,146],"article,":[112],"we":[113],"present":[114],"new":[116,148],"method":[117],"facilities":[125],"enhances":[127],"results":[129,175],"obtained":[130],"previously,":[131],"along":[132],"Gradient-weighted":[135],"Class":[136],"Activation":[137],"Mapping":[138],"Algorithm":[139],"(Grad-CAM)":[140],"identify":[142],"method,":[149],"convolutional":[150],"neural":[151],"networks":[152],"(CNN)":[153],"are":[154],"applied":[155],"classify":[157],"images":[158],"(CCTV":[159],"frames)":[160],"as":[161],"belonging":[162],"classes":[164],"Experimental":[174],"showed":[176],"best":[179],"performance":[180],"model":[181],"presented":[182],"an":[183],"accuracy":[184],"99.78%":[186],"and":[187],"false":[188],"negative":[189],"rate":[190],"0.00%.":[192]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2025-10-10T00:00:00"}
