{"id":"https://openalex.org/W3104430712","doi":"https://doi.org/10.1109/bigdata50022.2020.9377751","title":"An Efficient and Scalable Deep Learning Approach for Road Damage Detection","display_name":"An Efficient and Scalable Deep Learning Approach for Road Damage Detection","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3104430712","doi":"https://doi.org/10.1109/bigdata50022.2020.9377751","mag":"3104430712"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9377751","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9377751","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":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2011.09577","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002696579","display_name":"Sadra Naddaf-Sh","orcid":"https://orcid.org/0000-0003-1879-901X"},"institutions":[{"id":"https://openalex.org/I177898655","display_name":"Lamar University","ror":"https://ror.org/008ms5s18","country_code":"US","type":"education","lineage":["https://openalex.org/I177898655"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sadra Naddaf-Sh","raw_affiliation_strings":["Phillip M. Drayer Electrical Engineering Department, Lamar University, Beaumont, Texas, USA","Lamar University,Phillip M. Drayer Electrical Engineering Department,Beaumont,Texas,USA"],"affiliations":[{"raw_affiliation_string":"Phillip M. Drayer Electrical Engineering Department, Lamar University, Beaumont, Texas, USA","institution_ids":["https://openalex.org/I177898655"]},{"raw_affiliation_string":"Lamar University,Phillip M. Drayer Electrical Engineering Department,Beaumont,Texas,USA","institution_ids":["https://openalex.org/I177898655"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109760566","display_name":"M-Mahdi Naddaf-Sh","orcid":null},"institutions":[{"id":"https://openalex.org/I177898655","display_name":"Lamar University","ror":"https://ror.org/008ms5s18","country_code":"US","type":"education","lineage":["https://openalex.org/I177898655"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M-Mahdi Naddaf-Sh","raw_affiliation_strings":["Phillip M. Drayer Electrical Engineering Department, Lamar University, Beaumont, Texas, USA","Lamar University,Phillip M. Drayer Electrical Engineering Department,Beaumont,Texas,USA"],"affiliations":[{"raw_affiliation_string":"Phillip M. Drayer Electrical Engineering Department, Lamar University, Beaumont, Texas, USA","institution_ids":["https://openalex.org/I177898655"]},{"raw_affiliation_string":"Lamar University,Phillip M. Drayer Electrical Engineering Department,Beaumont,Texas,USA","institution_ids":["https://openalex.org/I177898655"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070985991","display_name":"Amir R. Kashani","orcid":"https://orcid.org/0000-0002-9929-0671"},"institutions":[{"id":"https://openalex.org/I4210111540","display_name":"Stanley Black & Decker (United States)","ror":"https://ror.org/01zrkhb76","country_code":"US","type":"company","lineage":["https://openalex.org/I4210111540"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amir R. Kashani","raw_affiliation_strings":["Artificial Intelligence Lab, Stanley Oil & Gas, Stanley Black & Decker, Washington DC, USA","Stanley Oil & Gas, Stanley Black & Decker,Artificial Intelligence Lab,Washington DC,USA"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Lab, Stanley Oil & Gas, Stanley Black & Decker, Washington DC, USA","institution_ids":["https://openalex.org/I4210111540"]},{"raw_affiliation_string":"Stanley Oil & Gas, Stanley Black & Decker,Artificial Intelligence Lab,Washington DC,USA","institution_ids":["https://openalex.org/I4210111540"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040854181","display_name":"Hassan Zargarzadeh","orcid":"https://orcid.org/0000-0003-0332-808X"},"institutions":[{"id":"https://openalex.org/I177898655","display_name":"Lamar University","ror":"https://ror.org/008ms5s18","country_code":"US","type":"education","lineage":["https://openalex.org/I177898655"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hassan Zargarzadeh","raw_affiliation_strings":["Phillip M. Drayer Electrical Engineering Department, Lamar University, Beaumont, Texas, USA","Lamar University,Phillip M. Drayer Electrical Engineering Department,Beaumont,Texas,USA"],"affiliations":[{"raw_affiliation_string":"Phillip M. Drayer Electrical Engineering Department, Lamar University, Beaumont, Texas, USA","institution_ids":["https://openalex.org/I177898655"]},{"raw_affiliation_string":"Lamar University,Phillip M. Drayer Electrical Engineering Department,Beaumont,Texas,USA","institution_ids":["https://openalex.org/I177898655"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5002696579"],"corresponding_institution_ids":["https://openalex.org/I177898655"],"apc_list":null,"apc_paid":null,"fwci":0.5817,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.66912647,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5602","last_page":"5608"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":1.0,"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":1.0,"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/T10264","display_name":"Asphalt Pavement Performance Evaluation","score":0.992900013923645,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.968500018119812,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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":0.6076026558876038},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5739148855209351},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5499016642570496},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.45840245485305786},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4553968012332916},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4546029269695282},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.43850716948509216},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.4376411437988281},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.307253897190094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6076026558876038},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5739148855209351},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5499016642570496},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.45840245485305786},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4553968012332916},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4546029269695282},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.43850716948509216},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.4376411437988281},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.307253897190094},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9377751","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9377751","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"},{"id":"pmh:oai:arXiv.org:2011.09577","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2011.09577","pdf_url":"https://arxiv.org/pdf/2011.09577","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3104430712","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2011.09577","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2011.09577","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2011.09577","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2011.09577","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2011.09577","pdf_url":"https://arxiv.org/pdf/2011.09577","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W87845390","https://openalex.org/W649219053","https://openalex.org/W1861492603","https://openalex.org/W2031489346","https://openalex.org/W2090926585","https://openalex.org/W2338908902","https://openalex.org/W2346804089","https://openalex.org/W2494379831","https://openalex.org/W2496312916","https://openalex.org/W2511065100","https://openalex.org/W2518108298","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2613718673","https://openalex.org/W2787765665","https://openalex.org/W2900287800","https://openalex.org/W2902195199","https://openalex.org/W2912049211","https://openalex.org/W2949736877","https://openalex.org/W2954428350","https://openalex.org/W2955425717","https://openalex.org/W2963112338","https://openalex.org/W2963351448","https://openalex.org/W2975407436","https://openalex.org/W2982646900","https://openalex.org/W2998997213","https://openalex.org/W3029443186","https://openalex.org/W3034971973","https://openalex.org/W3035682985","https://openalex.org/W3043995050","https://openalex.org/W3081878810","https://openalex.org/W3091461723","https://openalex.org/W3097922209","https://openalex.org/W3099452838","https://openalex.org/W6603520187","https://openalex.org/W6620707391","https://openalex.org/W6621353900","https://openalex.org/W6639102338","https://openalex.org/W6703652217","https://openalex.org/W6726497184","https://openalex.org/W6745245109","https://openalex.org/W6748134320","https://openalex.org/W6762718338","https://openalex.org/W6764945797","https://openalex.org/W6769974798","https://openalex.org/W6782530356","https://openalex.org/W6785321981"],"related_works":["https://openalex.org/W3154859778","https://openalex.org/W3000110218","https://openalex.org/W751258390","https://openalex.org/W2791177596","https://openalex.org/W2089024449","https://openalex.org/W3178156134","https://openalex.org/W1963782251","https://openalex.org/W2755731774","https://openalex.org/W2807119915","https://openalex.org/W2954647760","https://openalex.org/W3014686228","https://openalex.org/W3147968667","https://openalex.org/W2572493078","https://openalex.org/W3035719708","https://openalex.org/W3154478532","https://openalex.org/W3087366093","https://openalex.org/W2793993897","https://openalex.org/W2954225798","https://openalex.org/W3000087545","https://openalex.org/W1969741130"],"abstract_inverted_index":{"Pavement":[0],"condition":[1],"evaluation":[2],"is":[3,108,125,158,166],"essential":[4],"to":[5,17,23,59,79,135],"time":[6,140],"the":[7,30,61,67,81,147,150],"preventative":[8],"or":[9],"rehabilitative":[10],"actions":[11],"and":[12,26,32,47,66,102,115,137,155],"control":[13],"distress":[14,83,96],"propagation.":[15],"Failing":[16],"conduct":[18],"timely":[19,56],"evaluations":[20],"can":[21,39,52],"lead":[22],"severe":[24],"structural":[25],"financial":[27],"loss":[28],"of":[29,43,64,90,94,113,149],"infrastructure":[31],"complete":[33],"reconstructions.":[34],"Automated":[35],"computer-aided":[36],"surveying":[37,77],"measures":[38],"provide":[40],"a":[41,74,91,111],"database":[42,51,88],"road":[44,57],"damage":[45],"patterns":[46],"their":[48],"locations.":[49],"This":[50,71],"be":[53],"utilized":[54],"for":[55,121],"re-pairs":[58],"gain":[60],"minimum":[62],"cost":[63],"maintenance":[65],"asphalt's":[68],"maximum":[69],"durability.":[70],"paper":[72],"introduces":[73],"deep":[75],"learning-based":[76],"scheme":[78],"analyze":[80],"image-based":[82],"data":[84],"in":[85,130],"real-time.":[86],"A":[87],"consisting":[89],"diverse":[92],"population":[93],"crack":[95,123],"types":[97],"such":[98],"as":[99],"longitudinal,":[100],"transverse,":[101],"alligator":[103],"cracks,":[104],"photographed":[105],"using":[106],"mobile-device":[107],"used.":[109],"Then,":[110],"family":[112],"efficient":[114],"scalable":[116],"models":[117],"that":[118],"are":[119,153],"tuned":[120],"pavement":[122],"detection":[124],"trained.":[126],"Proposed":[127],"models,":[128],"resulted":[129],"F1-scores,":[131],"ranging":[132],"from":[133,141],"52%":[134],"56%,":[136],"average":[138],"inference":[139],"178-10":[142],"images":[143],"per":[144],"second.":[145],"Finally,":[146],"performance":[148],"object":[151],"detectors":[152],"examined,":[154],"error":[156],"analysis":[157],"reported":[159],"against":[160],"various":[161],"images.":[162],"The":[163],"source":[164],"code":[165],"available":[167],"at":[168],"https://github.com/mahdi65/roadDamageDetection2020.":[169]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}