{"id":"https://openalex.org/W4224131244","doi":"https://doi.org/10.3390/rs14081877","title":"A Fast Inference Vision Transformer for Automatic Pavement Image Classification and Its Visual Interpretation Method","display_name":"A Fast Inference Vision Transformer for Automatic Pavement Image Classification and Its Visual Interpretation Method","publication_year":2022,"publication_date":"2022-04-13","ids":{"openalex":"https://openalex.org/W4224131244","doi":"https://doi.org/10.3390/rs14081877"},"language":"en","primary_location":{"id":"doi:10.3390/rs14081877","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14081877","pdf_url":"https://www.mdpi.com/2072-4292/14/8/1877/pdf?version=1649864115","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/14/8/1877/pdf?version=1649864115","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065524007","display_name":"Yihan Chen","orcid":"https://orcid.org/0000-0002-8011-1290"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihan Chen","raw_affiliation_strings":["Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China","National Demonstration Center for Experimental Road and Traffic Engineering Education, Southeast University, Nanjing 211189, China"],"affiliations":[{"raw_affiliation_string":"Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"National Demonstration Center for Experimental Road and Traffic Engineering Education, Southeast University, Nanjing 211189, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071883235","display_name":"Xingyu Gu","orcid":"https://orcid.org/0000-0002-8561-7223"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xingyu Gu","raw_affiliation_strings":["Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China","National Demonstration Center for Experimental Road and Traffic Engineering Education, Southeast University, Nanjing 211189, China"],"affiliations":[{"raw_affiliation_string":"Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"National Demonstration Center for Experimental Road and Traffic Engineering Education, Southeast University, Nanjing 211189, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100412006","display_name":"Zhen Liu","orcid":"https://orcid.org/0000-0001-8012-7682"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Liu","raw_affiliation_strings":["Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China","National Demonstration Center for Experimental Road and Traffic Engineering Education, Southeast University, Nanjing 211189, China"],"affiliations":[{"raw_affiliation_string":"Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"National Demonstration Center for Experimental Road and Traffic Engineering Education, Southeast University, Nanjing 211189, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040956935","display_name":"Liang Jia","orcid":"https://orcid.org/0000-0001-9493-8742"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Liang","raw_affiliation_strings":["Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China","National Demonstration Center for Experimental Road and Traffic Engineering Education, Southeast University, Nanjing 211189, China"],"affiliations":[{"raw_affiliation_string":"Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"National Demonstration Center for Experimental Road and Traffic Engineering Education, Southeast University, Nanjing 211189, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5071883235"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":6.6847,"has_fulltext":true,"cited_by_count":62,"citation_normalized_percentile":{"value":0.97805692,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"14","issue":"8","first_page":"1877","last_page":"1877"},"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":0.9957000017166138,"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":0.9677000045776367,"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/computer-science","display_name":"Computer science","score":0.7372394800186157},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7071965932846069},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5939170718193054},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5807468295097351},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.5702990889549255},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5502550005912781},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5264571309089661},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48335832357406616},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4616808295249939},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.406143456697464},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09236496686935425},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.08068794012069702}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7372394800186157},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7071965932846069},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5939170718193054},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5807468295097351},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.5702990889549255},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5502550005912781},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5264571309089661},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48335832357406616},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4616808295249939},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.406143456697464},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09236496686935425},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.08068794012069702},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs14081877","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14081877","pdf_url":"https://www.mdpi.com/2072-4292/14/8/1877/pdf?version=1649864115","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:00192c5e0e4a4a3db5661f0e397471ab","is_oa":true,"landing_page_url":"https://doaj.org/article/00192c5e0e4a4a3db5661f0e397471ab","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 14, Iss 8, p 1877 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/8/1877/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14081877","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs14081877","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14081877","pdf_url":"https://www.mdpi.com/2072-4292/14/8/1877/pdf?version=1649864115","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4224131244.pdf","grobid_xml":"https://content.openalex.org/works/W4224131244.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1677182931","https://openalex.org/W1836465849","https://openalex.org/W2108598243","https://openalex.org/W2147800946","https://openalex.org/W2157331557","https://openalex.org/W2163605009","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2511065100","https://openalex.org/W2528491735","https://openalex.org/W2735386636","https://openalex.org/W2748643398","https://openalex.org/W2814406141","https://openalex.org/W2886369963","https://openalex.org/W2887597701","https://openalex.org/W2912530595","https://openalex.org/W2950768109","https://openalex.org/W2962858109","https://openalex.org/W2963446712","https://openalex.org/W2964308596","https://openalex.org/W2982083293","https://openalex.org/W3035422918","https://openalex.org/W3112350297","https://openalex.org/W3114333203","https://openalex.org/W3116489684","https://openalex.org/W3125559191","https://openalex.org/W3128592650","https://openalex.org/W3134503721","https://openalex.org/W3139399265","https://openalex.org/W3151130473","https://openalex.org/W3160826328","https://openalex.org/W3199740707","https://openalex.org/W3201890785","https://openalex.org/W3217728966","https://openalex.org/W4200535558","https://openalex.org/W4214588794","https://openalex.org/W4220764081","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W2990636717","https://openalex.org/W4206178588","https://openalex.org/W3094491777","https://openalex.org/W3214715529","https://openalex.org/W4287635093"],"abstract_inverted_index":{"Traditional":[0],"automatic":[1,52],"pavement":[2,54,90,182,198,291],"distress":[3],"detection":[4],"methods":[5],"using":[6,73],"convolutional":[7,60],"neural":[8],"networks":[9],"(CNNs)":[10],"require":[11],"a":[12,43,162,255],"great":[13],"deal":[14],"of":[15,26,34,59,89,103,117,137,140,148,224,231,282,288],"time":[16],"and":[17,21,68,77,93,146,150,175,184,191,229,239,260,270,279],"resources":[18],"for":[19,51,158],"computing":[20],"are":[22],"poor":[23],"in":[24,37,178,194,215,276],"terms":[25],"interpretability.":[27],"Therefore,":[28],"inspired":[29],"by":[30],"the":[31,74,83,104,128,138,141,165,179,195,202,207,225,242,267,286,289],"successful":[32],"application":[33],"Transformer":[35,45,144],"architecture":[36],"natural":[38],"language":[39],"processing":[40],"(NLP)":[41],"tasks,":[42],"novel":[44],"method":[46,167,228,244,257],"called":[47],"LeViT":[48,57,132],"was":[49,107,263],"introduced":[50],"asphalt":[53,181,197],"image":[55,91,292],"classification.":[56],"consists":[58],"layers,":[61],"transformer":[62],"stages":[63],"where":[64],"Multi-layer":[65],"Perception":[66],"(MLP)":[67],"multi-head":[69],"self-attention":[70],"blocks":[71],"alternate":[72],"residual":[75],"connection,":[76],"two":[78],"classifier":[79],"heads.":[80],"To":[81],"conduct":[82],"proposed":[84,105,166,243,264,290],"methods,":[85,131],"three":[86],"different":[87],"sources":[88],"datasets":[92],"pre-trained":[94],"weights":[95],"based":[96,121],"on":[97,122],"ImageNet":[98],"were":[99,119],"attained.":[100],"The":[101],"performance":[102,204,248],"model":[106],"compared":[108],"with":[109,161,249],"six":[110],"state-of-the-art":[111],"(SOTA)":[112],"deep":[113],"learning":[114,124],"models.":[115,210],"All":[116],"them":[118],"trained":[120],"transfer":[123],"strategy.":[125],"Compared":[126],"to":[127,265],"tested":[129,208],"SOTA":[130,209],"has":[133,273],"less":[134],"than":[135],"1/8":[136],"parameters":[139],"original":[142,226],"Vision":[143],"(ViT)":[145],"1/2":[147],"ResNet":[149],"InceptionNet.":[151],"Experimental":[152],"results":[153,269],"show":[154],"that":[155],"after":[156],"training":[157],"100":[159],"epochs":[160],"16":[163],"batch-size,":[164],"acquired":[168],"91.56%":[169,173],"accuracy,":[170,186],"91.72%":[171],"precision,":[172,188],"recall,":[174,190],"91.45%":[176],"F1-score":[177,193],"Chinese":[180],"dataset":[183],"99.17%":[185,189,192],"99.19%":[187],"German":[196],"dataset,":[199],"which":[200,220,284],"is":[201,221],"best":[203],"among":[205],"all":[206],"Moreover,":[211],"it":[212],"shows":[213],"superiority":[214],"inference":[216],"speed":[217],"(86":[218],"ms/step),":[219],"approximately":[222],"25%":[223],"ViT":[227],"80%":[230],"some":[232],"prevailing":[233],"CNN-based":[234],"models,":[235],"including":[236],"DenseNet,":[237],"VGG,":[238],"ResNet.":[240],"Overall,":[241],"can":[245],"achieve":[246],"competitive":[247],"fewer":[250],"computation":[251],"costs.":[252],"In":[253],"addition,":[254],"visualization":[256],"combining":[258],"Grad-CAM":[259],"Attention":[261],"Rollout":[262],"analyze":[266],"classification":[268,293],"explore":[271],"what":[272],"been":[274],"learned":[275],"every":[277],"MLP":[278],"attention":[280],"block":[281],"LeViT,":[283],"improved":[285],"interpretability":[287],"model.":[294]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":27},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":2}],"updated_date":"2026-04-11T08:14:18.477133","created_date":"2025-10-10T00:00:00"}