{"id":"https://openalex.org/W3137588588","doi":"https://doi.org/10.1109/bigdata50022.2020.9378027","title":"Road Damage Detection and Classification with Detectron2 and Faster R-CNN","display_name":"Road Damage Detection and Classification with Detectron2 and Faster R-CNN","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3137588588","doi":"https://doi.org/10.1109/bigdata50022.2020.9378027","mag":"3137588588"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5063674569","display_name":"Vung Pham","orcid":"https://orcid.org/0000-0001-9702-8904"},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vung Pham","raw_affiliation_strings":["Computer Science Department, Texas Tech University, Lubbock, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Texas Tech University, Lubbock, USA","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101620869","display_name":"Chau Pham","orcid":"https://orcid.org/0000-0001-6137-9737"},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chau Pham","raw_affiliation_strings":["Computer Science Department, Texas Tech University, Lubbock, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Texas Tech University, Lubbock, USA","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032280607","display_name":"Tommy Dang","orcid":"https://orcid.org/0000-0001-8322-0014"},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tommy Dang","raw_affiliation_strings":["Computer Science Department, Texas Tech University, Lubbock, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Texas Tech University, Lubbock, USA","institution_ids":["https://openalex.org/I12315562"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I12315562"],"apc_list":null,"apc_paid":null,"fwci":49.3831,"has_fulltext":false,"cited_by_count":164,"citation_normalized_percentile":{"value":0.99983648,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"5592","last_page":"5601"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T11609","display_name":"Geophysical Methods and Applications","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T10036","display_name":"Advanced Neural Network Applications","score":0.9872000217437744,"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":0.813251256942749},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.6425919532775879},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6095234751701355},{"id":"https://openalex.org/keywords/damages","display_name":"Damages","score":0.5139474272727966},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4852197468280792},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4533979892730713},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.45175209641456604},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.4245198667049408},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3879346549510956}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.813251256942749},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.6425919532775879},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6095234751701355},{"id":"https://openalex.org/C2777381055","wikidata":"https://www.wikidata.org/wiki/Q308922","display_name":"Damages","level":2,"score":0.5139474272727966},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4852197468280792},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4533979892730713},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.45175209641456604},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.4245198667049408},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3879346549510956},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W2070604790","https://openalex.org/W2099471712","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2570343428","https://openalex.org/W2613718673","https://openalex.org/W2787765665","https://openalex.org/W2796347433","https://openalex.org/W2804204591","https://openalex.org/W2889985731","https://openalex.org/W2912049211","https://openalex.org/W2913071089","https://openalex.org/W2913348995","https://openalex.org/W2913930668","https://openalex.org/W2962760235","https://openalex.org/W2962793481","https://openalex.org/W2963037989","https://openalex.org/W2963351448","https://openalex.org/W2986375760","https://openalex.org/W3013211776","https://openalex.org/W3018757597","https://openalex.org/W3034059856","https://openalex.org/W3034971973","https://openalex.org/W3035160371","https://openalex.org/W3038710868","https://openalex.org/W3039295546","https://openalex.org/W3081878810","https://openalex.org/W3099319035","https://openalex.org/W3099452838","https://openalex.org/W3106250896","https://openalex.org/W4293584584","https://openalex.org/W4294643831","https://openalex.org/W4320013936","https://openalex.org/W6620707391","https://openalex.org/W6628973269","https://openalex.org/W6631782140","https://openalex.org/W6637373629","https://openalex.org/W6639102338","https://openalex.org/W6745560452","https://openalex.org/W6748134320","https://openalex.org/W6750227808","https://openalex.org/W6777046832","https://openalex.org/W6782530356","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4205240985","https://openalex.org/W2314597598","https://openalex.org/W1527183021","https://openalex.org/W3125296675","https://openalex.org/W4235320026","https://openalex.org/W3124239800","https://openalex.org/W2365977737","https://openalex.org/W1577024311","https://openalex.org/W4387399630","https://openalex.org/W2170856278"],"abstract_inverted_index":{"The":[0,90],"road":[1,10,27],"is":[2,12,29,105],"vital":[3],"for":[4,14,46,98,129,176],"many":[5],"aspects":[6],"of":[7,18,26,54,125,135],"life,":[8],"and":[9,32,35,43,61,107,127,132,162],"maintenance":[11],"crucial":[13],"human":[15],"safety.":[16],"One":[17],"the":[19,41,70,80,94,130,136,140,146,155,159,173],"critical":[20],"tasks":[21],"to":[22,30,110,113,171],"allow":[23],"timely":[24],"repair":[25],"damages":[28],"quickly":[31],"efficiently":[33],"detect":[34],"classify":[36],"them.":[37],"This":[38,119],"work":[39],"details":[40],"strategies":[42,170],"experiments":[44],"evaluated":[45],"these":[47,67],"tasks.":[48],"Specifically,":[49],"we":[50,152,167],"evaluate":[51,154],"Detectron2's":[52,102],"implementation":[53],"Faster":[55,99],"R-CNN":[56,100],"using":[57,69],"different":[58,114],"base":[59,96],"models":[60],"configurations.":[62],"We":[63],"also":[64,153,168],"experiment":[65],"with":[66,101],"approaches":[68],"Global":[71],"Road":[72],"Damage":[73],"Detection":[74],"Challenge":[75,88],"2020,":[76],"A":[77],"Track":[78],"in":[79,116,122],"IEEE":[81],"Big":[82,85],"Data":[83,86],"2020":[84],"Cup":[87],"dataset.":[89,178],"results":[91,121,157],"show":[92,142],"that":[93],"X101-FPN":[95],"model":[97],"default":[103],"configurations":[104],"efficient":[106],"general":[108],"enough":[109],"be":[111],"transferable":[112],"countries":[115],"this":[117,177],"challenge.":[118],"approach":[120],"F1":[123,147],"scores":[124,148],"51.0%":[126],"51.4%":[128],"test1":[131],"test2":[133],"sets":[134],"challenge,":[137],"respectively.":[138],"Though":[139],"visualizations":[141],"good":[143],"prediction":[144,156],"results,":[145],"are":[149],"low.":[150],"Therefore,":[151],"against":[158],"existing":[160],"annotations":[161],"discover":[163],"some":[164],"discrepancies.":[165],"Thus,":[166],"suggest":[169],"improve":[172],"labeling":[174],"process":[175]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":37},{"year":2024,"cited_by_count":40},{"year":2023,"cited_by_count":40},{"year":2022,"cited_by_count":25},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}