{"id":"https://openalex.org/W4385732611","doi":"https://doi.org/10.1109/tits.2023.3300312","title":"ECSNet: An Accelerated Real-Time Image Segmentation CNN Architecture for Pavement Crack Detection","display_name":"ECSNet: An Accelerated Real-Time Image Segmentation CNN Architecture for Pavement Crack Detection","publication_year":2023,"publication_date":"2023-08-10","ids":{"openalex":"https://openalex.org/W4385732611","doi":"https://doi.org/10.1109/tits.2023.3300312"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2023.3300312","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2023.3300312","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-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/A5003966240","display_name":"Tianjie Zhang","orcid":"https://orcid.org/0000-0002-6697-0777"},"institutions":[{"id":"https://openalex.org/I120156002","display_name":"Boise State University","ror":"https://ror.org/02e3zdp86","country_code":"US","type":"education","lineage":["https://openalex.org/I120156002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianjie Zhang","raw_affiliation_strings":["Department of Computer Science, Boise State University, Boise, ID, USA"],"raw_orcid":"https://orcid.org/0000-0002-6697-0777","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Boise State University, Boise, ID, USA","institution_ids":["https://openalex.org/I120156002"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038300996","display_name":"Donglei Wang","orcid":"https://orcid.org/0000-0002-5888-7439"},"institutions":[{"id":"https://openalex.org/I120156002","display_name":"Boise State University","ror":"https://ror.org/02e3zdp86","country_code":"US","type":"education","lineage":["https://openalex.org/I120156002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Donglei Wang","raw_affiliation_strings":["Department of Civil Engineering, Boise State University, Boise, ID, USA"],"raw_orcid":"https://orcid.org/0000-0002-5888-7439","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Boise State University, Boise, ID, USA","institution_ids":["https://openalex.org/I120156002"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068651974","display_name":"Yang Lu","orcid":"https://orcid.org/0000-0003-2330-4237"},"institutions":[{"id":"https://openalex.org/I120156002","display_name":"Boise State University","ror":"https://ror.org/02e3zdp86","country_code":"US","type":"education","lineage":["https://openalex.org/I120156002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Lu","raw_affiliation_strings":["Department of Civil Engineering, Boise State University, Boise, ID, USA"],"raw_orcid":"https://orcid.org/0000-0003-2330-4237","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Boise State University, Boise, ID, USA","institution_ids":["https://openalex.org/I120156002"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I120156002"],"apc_list":null,"apc_paid":null,"fwci":9.1302,"has_fulltext":false,"cited_by_count":79,"citation_normalized_percentile":{"value":0.99163006,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"24","issue":"12","first_page":"15105","last_page":"15112"},"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":0.9986000061035156,"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":0.9825000166893005,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6091654300689697},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5967859029769897},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5876401662826538},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5779799818992615},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5456323623657227},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5082792639732361},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3416835367679596},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09166651964187622}],"concepts":[{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6091654300689697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5967859029769897},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5876401662826538},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5779799818992615},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5456323623657227},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5082792639732361},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3416835367679596},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09166651964187622},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2023.3300312","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2023.3300312","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2045332846","https://openalex.org/W2194775991","https://openalex.org/W2630837129","https://openalex.org/W2742801844","https://openalex.org/W2912350898","https://openalex.org/W2941356554","https://openalex.org/W2963881378","https://openalex.org/W2970225046","https://openalex.org/W2982083293","https://openalex.org/W3005933934","https://openalex.org/W3009712583","https://openalex.org/W3021470593","https://openalex.org/W3087277009","https://openalex.org/W3184120658","https://openalex.org/W3194006183","https://openalex.org/W3196806685","https://openalex.org/W4206296868","https://openalex.org/W4211062512","https://openalex.org/W4213361372","https://openalex.org/W4220780913","https://openalex.org/W4221117053","https://openalex.org/W4224237033","https://openalex.org/W4225984668","https://openalex.org/W4281254198","https://openalex.org/W4281975579","https://openalex.org/W4285105302","https://openalex.org/W4293406525","https://openalex.org/W4297775537","https://openalex.org/W4311569659","https://openalex.org/W4319996377","https://openalex.org/W6717372056","https://openalex.org/W6737664043","https://openalex.org/W6739696289","https://openalex.org/W6742250094"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W2588198209","https://openalex.org/W1909006023","https://openalex.org/W4205824991","https://openalex.org/W4389858081","https://openalex.org/W3200723557","https://openalex.org/W4312713546","https://openalex.org/W2362195430","https://openalex.org/W2347494122","https://openalex.org/W1522196789"],"abstract_inverted_index":{"The":[0,137],"ability":[1],"to":[2,63,148],"perform":[3],"pixel-wise":[4],"segmentation":[5,90],"on":[6,29],"pavement":[7,55,86],"cracks":[8],"in":[9,13,53,142],"real-time":[10,54,85],"is":[11,161,202],"paramount":[12],"road":[14],"service":[15],"condition":[16],"assessment":[17],"and":[18,32,40,47,89,104,108,119,126,145,158,175,193,204,215],"maintenance":[19],"decision-making":[20],"practices.":[21],"Recent":[22],"deep":[23,72],"learning":[24,73],"detection":[25,30,57,88],"models":[26,49,151,200],"are":[27,59],"focused":[28],"accuracy":[31,52,127,144,214],"require":[33],"a":[34,70,210],"large":[35],"number":[36],"of":[37,129,173,181],"computing":[38],"sources":[39],"long":[41],"run":[42],"times.":[43],"However,":[44],"highly":[45],"efficient":[46],"accelerated":[48,84],"with":[50,169],"acceptable":[51],"crack":[56,87,115],"tasks":[58],"required":[60],"but":[61],"hard":[62],"achieve.":[64],"In":[65],"this":[66],"work,":[67],"we":[68],"present":[69],"customized":[71],"model":[74,132,185],"architecture":[75,113],"named":[76],"Efficient":[77],"Crack":[78],"Segmentation":[79],"Neural":[80],"Network":[81],"(ECSNet)":[82],"for":[83,114],"without":[91],"compromising":[92],"performance.":[93],"We":[94,123],"introduce":[95],"some":[96],"novel":[97],"parts,":[98],"including":[99,152],"small":[100],"kernel":[101],"convolutional":[102,109],"layers":[103],"parallel":[105],"max":[106],"pooling":[107],"operation,":[110],"into":[111],"the":[112,134,166,187,199],"information":[116],"quickly":[117],"extraction":[118],"model\u2019s":[120],"parameter":[121],"reduction.":[122],"test":[124],"latency":[125],"trade-offs":[128],"our":[130,184],"proposed":[131],"using":[133],"DeepCrack":[135],"Dataset.":[136],"results":[138],"demonstrate":[139],"strong":[140],"performance":[141],"both":[143],"efficiency":[146,216],"compared":[147],"other":[149],"state-of-the-art":[150],"DeepLabV3,":[153],"FCN,":[154],"LRASPP,":[155],"Enet,":[156],"Unet":[157],"DeepCrack.":[159],"It":[160,208],"promising":[162],"that":[163],"ECSNet":[164],"obtains":[165],"second":[167],"place":[168],"an":[170,176],"F1":[171],"score":[172],"(84.45%)":[174],"Intersection":[177],"over":[178],"Union":[179],"(IoU)":[180],"73.08%.":[182],"Furthermore,":[183],"gains":[186],"largest":[188],"Frames":[189],"Per":[190],"Second":[191],"(FPS)":[192],"lowest":[194],"training":[195],"time":[196],"among":[197],"all":[198],"which":[201],"73.29":[203],"5011":[205],"seconds,":[206],"respectively.":[207],"maintains":[209],"good":[211],"balance":[212],"between":[213],"metrics.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":33},{"year":2024,"cited_by_count":31},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-25T09:21:30.201066","created_date":"2025-10-10T00:00:00"}