{"id":"https://openalex.org/W3007139825","doi":"https://doi.org/10.1109/bigdata47090.2019.9006077","title":"Thermal Imagery Based Instance Segmentation for Energy Audit Applications in Buildings","display_name":"Thermal Imagery Based Instance Segmentation for Energy Audit Applications in Buildings","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007139825","doi":"https://doi.org/10.1109/bigdata47090.2019.9006077","mag":"3007139825"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006077","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006077","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5032387259","display_name":"Youness Arjoune","orcid":"https://orcid.org/0000-0002-8207-0170"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Youness Arjoune","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112556430","display_name":"Sai Peri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sai Peri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055045007","display_name":"Niroop Sugunaraj","orcid":"https://orcid.org/0000-0002-6165-0862"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niroop Sugunaraj","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074606142","display_name":"Debanjan Sadhukhan","orcid":"https://orcid.org/0000-0002-9744-9327"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Debanjan Sadhukhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091105752","display_name":"Michael E. Nord","orcid":"https://orcid.org/0000-0001-5533-5535"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael Nord","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014947593","display_name":"Gautham Krishnamoorthy","orcid":"https://orcid.org/0000-0002-5520-5092"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gautham Krishnamoorthy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031279701","display_name":"David T. Flynn","orcid":"https://orcid.org/0000-0001-7098-9668"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"David Flynn","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5024222105","display_name":"Prakash Ranganathan","orcid":"https://orcid.org/0000-0001-8638-660X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prakash Ranganathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2034,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.57206292,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"208","issue":null,"first_page":"5974","last_page":"5976"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14319","display_name":"Currency Recognition and Detection","score":0.9921000003814697,"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/T14319","display_name":"Currency Recognition and Detection","score":0.9921000003814697,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9861999750137329,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/computer-science","display_name":"Computer science","score":0.6767457127571106},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5750800371170044},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5176451802253723},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.495730996131897},{"id":"https://openalex.org/keywords/audit","display_name":"Audit","score":0.49340739846229553},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4664667844772339},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.09301549196243286}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6767457127571106},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5750800371170044},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5176451802253723},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.495730996131897},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.49340739846229553},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4664667844772339},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.09301549196243286},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006077","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006077","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.9100000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2324761624","https://openalex.org/W2508384486","https://openalex.org/W2535259680","https://openalex.org/W2553945500","https://openalex.org/W2559890493","https://openalex.org/W2613718673","https://openalex.org/W2701094668","https://openalex.org/W2756096025","https://openalex.org/W2767851919","https://openalex.org/W2776636286","https://openalex.org/W2789560854","https://openalex.org/W2963150697","https://openalex.org/W2964095005","https://openalex.org/W2972903622","https://openalex.org/W2977741488","https://openalex.org/W6620707391"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Energy":[0],"audit":[1],"in":[2,30,151,158],"buildings":[3,65],"is":[4],"an":[5],"essential":[6],"task":[7],"for":[8,134],"optimal":[9],"energy":[10],"management":[11],"and":[12,72,90,100,105,129],"operations.":[13],"This":[14],"paper":[15],"focuses":[16],"on":[17],"a":[18,37,50,82,115,135],"machine":[19],"learning":[20],"pipeline":[21],"to":[22,48,59,80],"quantify":[23],"heat":[24,154],"loss":[25],"using":[26],"60,000":[27],"thermal":[28,52,145],"images":[29,33,146],"buildings.":[31],"The":[32,107,139],"are":[34,57,77],"captured":[35],"from":[36,143],"small":[38],"Unmanned":[39],"Aerial":[40],"System":[41],"(sUAS)":[42],"over":[43,123],"the":[44,64,152],"last":[45],"two":[46],"years":[47],"form":[49],"large":[51,83],"data":[53,86],"repository.":[54],"Intense":[55],"efforts":[56],"made":[58],"annotate":[60],"multiple":[61],"sections":[62],"of":[63,121,137],"(e.g.":[66],"windows,":[67],"doors,":[68],"ground,":[69],"facade,":[70],"trees,":[71],"sky).":[73],"Data":[74],"augmentation":[75],"processes":[76],"then":[78,149],"applied":[79],"generate":[81],"comprehensive":[84],"training":[85],"set.":[87],"Object":[88],"detection":[89],"instance":[91],"segmentation":[92],"models":[93],"such":[94],"as":[95],"Mask":[96,112],"R-CNN,":[97,99],"Fast":[98,126],"Faster":[101,130],"R-CNN":[102,113,124,127,131],"were":[103,148],"trained,":[104],"tested.":[106],"preliminary":[108],"results":[109],"indicate":[110],"that":[111],"has":[114],"larger":[116],"mean":[117],"average":[118],"precision":[119],"(mAP)":[120],"(83%)":[122],"(51%),":[125],"(62%),":[128],"(62":[132],"%)":[133],"threshold":[136],"50%.":[138],"surface":[140],"temperature":[141],"values":[142],"these":[144],"(pixel-by-pixel)":[147],"used":[150],"standard":[153],"transfer":[155],"coefficient":[156],"(U-value":[157],"BTU/hr/Sq.ft./F)":[159],"calculations.":[160]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
