{"id":"https://openalex.org/W2899242765","doi":"https://doi.org/10.1109/tip.2018.2878966","title":"DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection","display_name":"DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection","publication_year":2018,"publication_date":"2018-10-31","ids":{"openalex":"https://openalex.org/W2899242765","doi":"https://doi.org/10.1109/tip.2018.2878966","mag":"2899242765","pmid":"https://pubmed.ncbi.nlm.nih.gov/30387731"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2018.2878966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2878966","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5070093927","display_name":"Qin Zou","orcid":"https://orcid.org/0000-0001-7955-0782"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qin Zou","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-7955-0782","affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100459168","display_name":"Zheng Zhang","orcid":"https://orcid.org/0000-0003-1470-6998"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Zhang","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404067","display_name":"Qingquan Li","orcid":"https://orcid.org/0000-0002-2438-6046"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingquan Li","raw_affiliation_strings":["Shenzhen Key Laboratory of Spatial Smart Sensing and Service, Shenzhen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Key Laboratory of Spatial Smart Sensing and Service, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020400305","display_name":"Xianbiao Qi","orcid":"https://orcid.org/0000-0002-8493-1966"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianbiao Qi","raw_affiliation_strings":["Shenzhen Research Institute of Big Data, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data, Shenzhen, China","institution_ids":["https://openalex.org/I4210099586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100391116","display_name":"Qian Wang","orcid":"https://orcid.org/0000-0002-8967-8525"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Wang","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-8967-8525","affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082259804","display_name":"Song Wang","orcid":"https://orcid.org/0000-0003-4152-5295"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Song Wang","raw_affiliation_strings":["Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA","institution_ids":["https://openalex.org/I155781252"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":39.5323,"has_fulltext":false,"cited_by_count":1051,"citation_normalized_percentile":{"value":0.99975549,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"28","issue":"3","first_page":"1498","last_page":"1512"},"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":0.9926000237464905,"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/T11850","display_name":"Concrete Corrosion and Durability","score":0.9787999987602234,"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":0.81415855884552},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7336708903312683},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7090873718261719},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6410081386566162},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6184418201446533},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.6002393960952759},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.598473072052002},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5866507291793823},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5139138698577881},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.4889446496963501},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4880756139755249},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.47039875388145447},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.46475306153297424},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35499125719070435},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10667896270751953},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08810403943061829}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.81415855884552},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7336708903312683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7090873718261719},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6410081386566162},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6184418201446533},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.6002393960952759},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.598473072052002},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5866507291793823},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5139138698577881},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.4889446496963501},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4880756139755249},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.47039875388145447},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.46475306153297424},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35499125719070435},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10667896270751953},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08810403943061829},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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:10.1109/tip.2018.2878966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2878966","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:30387731","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/30387731","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","display_name":"No poverty","score":0.4000000059604645}],"awards":[{"id":"https://openalex.org/G1107534717","display_name":null,"funder_award_id":"91546106","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1428221054","display_name":null,"funder_award_id":"61301277","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1612684229","display_name":null,"funder_award_id":"2018CFB482","funder_id":"https://openalex.org/F4320322186","funder_display_name":"Natural Science Foundation of Hubei Province"},{"id":"https://openalex.org/G4387305886","display_name":null,"funder_award_id":"61872277","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5685664964","display_name":null,"funder_award_id":"2016YFB0502203","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322186","display_name":"Natural Science Foundation of Hubei Province","ror":null},{"id":"https://openalex.org/F4320324116","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W142335935","https://openalex.org/W345900524","https://openalex.org/W845365781","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1832101600","https://openalex.org/W1842610785","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1910619957","https://openalex.org/W1930528368","https://openalex.org/W1967123296","https://openalex.org/W1969305618","https://openalex.org/W1969393996","https://openalex.org/W1974605586","https://openalex.org/W1976047850","https://openalex.org/W1995130521","https://openalex.org/W2033294017","https://openalex.org/W2052516389","https://openalex.org/W2079054397","https://openalex.org/W2090026077","https://openalex.org/W2110158442","https://openalex.org/W2113726946","https://openalex.org/W2117193984","https://openalex.org/W2122184585","https://openalex.org/W2128880484","https://openalex.org/W2130785652","https://openalex.org/W2144801789","https://openalex.org/W2145023731","https://openalex.org/W2150769593","https://openalex.org/W2151049637","https://openalex.org/W2155893237","https://openalex.org/W2160411867","https://openalex.org/W2163605009","https://openalex.org/W2276393162","https://openalex.org/W2300687442","https://openalex.org/W2372645802","https://openalex.org/W2407692387","https://openalex.org/W2483076098","https://openalex.org/W2511065100","https://openalex.org/W2523358814","https://openalex.org/W2553813712","https://openalex.org/W2560622558","https://openalex.org/W2591692311","https://openalex.org/W2594490116","https://openalex.org/W2612491774","https://openalex.org/W2962872526","https://openalex.org/W2963723365","https://openalex.org/W2963881378","https://openalex.org/W4241071816","https://openalex.org/W6638650905","https://openalex.org/W6639799379","https://openalex.org/W6639824700","https://openalex.org/W6677607857","https://openalex.org/W6684191040","https://openalex.org/W6709127878"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W2354322770","https://openalex.org/W4237547500","https://openalex.org/W1570848052","https://openalex.org/W2373192430","https://openalex.org/W4239268388","https://openalex.org/W4243305035","https://openalex.org/W1537496349","https://openalex.org/W2379407973","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Cracks":[0],"are":[1,6,75,86,96],"typical":[2],"line":[3,81],"structures":[4],"that":[5,150],"of":[7,110],"interest":[8],"in":[9,88,98,119,124,161],"many":[10,15],"computer-vision":[11],"applications.":[12],"In":[13,38,63],"practice,":[14],"cracks,":[16,19],"e.g.,":[17],"pavement":[18],"show":[20],"poor":[21],"continuity":[22],"and":[23,92,112,123,140,163],"low":[24],"contrast,":[25],"which":[26],"brings":[27],"great":[28],"challenges":[29],"to":[30,78],"image-based":[31],"crack":[32,54,61,138],"detection":[33,55],"by":[34,56],"using":[35],"low-level":[36],"features.":[37],"this":[39,64],"paper,":[40],"we":[41],"propose":[42],"DeepCrack":[43,104,134,151],"-":[44],"an":[45],"end-to-end":[46],"trainable":[47],"deep":[48,67],"convolutional":[49,68,73,116],"neural":[50],"network":[51,122,127],"for":[52,60],"automatic":[53],"learning":[57],"high-level":[58],"features":[59,69,117],"representation.":[62],"method,":[65],"multi-scale":[66],"learned":[70],"at":[71,128],"hierarchical":[72],"stages":[74],"fused":[76],"together":[77],"capture":[79],"the":[80,107,115,120,125,129,157,165],"structures.":[82],"More":[83],"detailed":[84],"representations":[85,95],"made":[87,97],"larger-scale":[89],"feature":[90,100],"maps":[91],"more":[93],"holistic":[94],"smaller-scale":[99],"maps.":[101],"We":[102,132],"build":[103],"net":[105,135],"on":[106,136,143,156],"encoder-decoder":[108],"architecture":[109],"SegNet,":[111],"pairwisely":[113],"fuse":[114],"generated":[118],"encoder":[121],"decoder":[126],"same":[130],"scale.":[131],"train":[133],"one":[137],"dataset":[139],"evaluate":[141],"it":[142],"three":[144,158],"others.":[145],"The":[146],"experimental":[147],"results":[148],"demonstrate":[149],"achieves":[152],"F-Measure":[153],"over":[154],"0.87":[155],"challenging":[159],"datasets":[160],"average":[162],"outperforms":[164],"current":[166],"state-of-the-art":[167],"methods.":[168]},"counts_by_year":[{"year":2026,"cited_by_count":115},{"year":2025,"cited_by_count":213},{"year":2024,"cited_by_count":192},{"year":2023,"cited_by_count":194},{"year":2022,"cited_by_count":137},{"year":2021,"cited_by_count":101},{"year":2020,"cited_by_count":63},{"year":2019,"cited_by_count":32},{"year":2018,"cited_by_count":4}],"updated_date":"2026-07-25T09:21:30.201066","created_date":"2025-10-10T00:00:00"}
