{"id":"https://openalex.org/W3086857729","doi":"https://doi.org/10.23919/fusion45008.2020.9190400","title":"Two-Step Surface Damage Detection Scheme using Convolutional Neural Network and Artificial Neural Network","display_name":"Two-Step Surface Damage Detection Scheme using Convolutional Neural Network and Artificial Neural Network","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3086857729","doi":"https://doi.org/10.23919/fusion45008.2020.9190400","mag":"3086857729"},"language":"en","primary_location":{"id":"doi:10.23919/fusion45008.2020.9190400","is_oa":false,"landing_page_url":"https://doi.org/10.23919/fusion45008.2020.9190400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2003.10760","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053567805","display_name":"Alice Yi Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I192619145","display_name":"University of the Witwatersrand","ror":"https://ror.org/03rp50x72","country_code":"ZA","type":"education","lineage":["https://openalex.org/I192619145"]}],"countries":["ZA"],"is_corresponding":true,"raw_author_name":"Alice Yi Yang","raw_affiliation_strings":["School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa","institution_ids":["https://openalex.org/I192619145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002268768","display_name":"Ling Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I192619145","display_name":"University of the Witwatersrand","ror":"https://ror.org/03rp50x72","country_code":"ZA","type":"education","lineage":["https://openalex.org/I192619145"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Ling Cheng","raw_affiliation_strings":["School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa","institution_ids":["https://openalex.org/I192619145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5053567805"],"corresponding_institution_ids":["https://openalex.org/I192619145"],"apc_list":null,"apc_paid":null,"fwci":0.7272,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.69515895,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9919999837875366,"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/T11850","display_name":"Concrete Corrosion and Durability","score":0.9796000123023987,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8328649997711182},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8083357810974121},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7550150156021118},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.7059261798858643},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.613315224647522},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6066181659698486},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5604288578033447},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5200809836387634},{"id":"https://openalex.org/keywords/canny-edge-detector","display_name":"Canny edge detector","score":0.507358729839325},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.4598871171474457},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4535475969314575},{"id":"https://openalex.org/keywords/false-positives-and-false-negatives","display_name":"False positives and false negatives","score":0.41701966524124146},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4108811616897583},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34310483932495117},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2815900444984436}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8328649997711182},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8083357810974121},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7550150156021118},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.7059261798858643},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.613315224647522},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6066181659698486},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5604288578033447},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5200809836387634},{"id":"https://openalex.org/C14705441","wikidata":"https://www.wikidata.org/wiki/Q597183","display_name":"Canny edge detector","level":5,"score":0.507358729839325},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.4598871171474457},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4535475969314575},{"id":"https://openalex.org/C112789634","wikidata":"https://www.wikidata.org/wiki/Q18207010","display_name":"False positives and false negatives","level":3,"score":0.41701966524124146},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4108811616897583},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34310483932495117},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2815900444984436},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/fusion45008.2020.9190400","is_oa":false,"landing_page_url":"https://doi.org/10.23919/fusion45008.2020.9190400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2003.10760","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.10760","pdf_url":"https://arxiv.org/pdf/2003.10760","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2003.10760","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.10760","pdf_url":"https://arxiv.org/pdf/2003.10760","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":"Sustainable cities and communities","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1709548961","https://openalex.org/W2016312379","https://openalex.org/W2044097773","https://openalex.org/W2089468765","https://openalex.org/W2291383002","https://openalex.org/W2295124130","https://openalex.org/W2316653434","https://openalex.org/W2318006660","https://openalex.org/W2766039569","https://openalex.org/W2896894644","https://openalex.org/W2897872796","https://openalex.org/W2916316891","https://openalex.org/W2941796489","https://openalex.org/W2949194345","https://openalex.org/W3008300569","https://openalex.org/W4236737715"],"related_works":["https://openalex.org/W1557094818","https://openalex.org/W2183246718","https://openalex.org/W1973412793","https://openalex.org/W2099261052","https://openalex.org/W4292605373","https://openalex.org/W2951146195","https://openalex.org/W4226316650","https://openalex.org/W3123215897","https://openalex.org/W2153600354","https://openalex.org/W3169126738"],"abstract_inverted_index":{"Surface":[0],"damage":[1,8,23,55],"on":[2],"concrete":[3],"is":[4,51,56,64,69,149,187],"important":[5],"as":[6,66,82],"the":[7,11,15,53,59,62,85,89,98,107,112,116,134,142,147,153,158,171,185,190,214],"can":[9],"affect":[10],"structural":[12],"integrity":[13],"of":[14,91,128,146,165,176],"structure.":[16],"This":[17,68,87],"paper":[18],"proposes":[19],"a":[20],"two-step":[21,215],"surface":[22,54,179],"detection":[24,207],"scheme":[25,216],"using":[26,122],"Convolutional":[27],"Neural":[28,33],"Network":[29,34],"(CNN)":[30],"and":[31,46,170,211],"Artificial":[32],"(ANN).":[35],"The":[36,48,73,100,118,144,160,181,204],"CNN":[37,161,186,210],"classifies":[38],"given":[39],"input":[40],"images":[41,92],"into":[42,141],"two":[43],"categories:":[44],"positive":[45,49,83,154,192],"negative.":[47,67],"category":[50],"where":[52],"present":[57],"within":[58,115,157],"image,":[60],"otherwise":[61],"image":[63,76,168],"classified":[65,81],"an":[70,163,174],"image-based":[71],"classification.":[72],"ANN":[74,101,148,172,212],"accepts":[75],"inputs":[77,140],"that":[78,93],"have":[79],"been":[80],"by":[84,97],"ANN.":[86,99,143],"reduces":[88],"number":[90],"are":[94,109,120,131,139,197,202,217],"further":[95],"processed":[96],"performs":[102],"feature-based":[103],"classification,":[104],"in":[105,184,213],"which":[106],"features":[108,130,138],"extracted":[110,132],"from":[111,133],"detected":[113,121,135],"edges":[114,119,156],"image.":[117,159],"Canny":[123],"edge":[124],"detection.":[125,180],"A":[126],"total":[127],"19":[129],"edges.":[136],"These":[137],"purpose":[145],"to":[150,189],"highlight":[151],"only":[152],"damaged":[155],"achieves":[162,173],"accuracy":[164,175,183],"80.7%":[166],"for":[167,178,208],"classification":[169],"98.1%":[177],"decreased":[182],"due":[188],"false":[191,195,200,205],"detection,":[193],"however":[194],"positives":[196],"tolerated":[198],"whereas":[199],"negatives":[201],"not.":[203],"negative":[206],"both":[209],"0%.":[218]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2020-09-21T00:00:00"}
