{"id":"https://openalex.org/W7140288943","doi":"https://doi.org/10.3390/a19040247","title":"A Real-Time Detection Approach for Bridge Crack","display_name":"A Real-Time Detection Approach for Bridge Crack","publication_year":2026,"publication_date":"2026-03-25","ids":{"openalex":"https://openalex.org/W7140288943","doi":"https://doi.org/10.3390/a19040247"},"language":"en","primary_location":{"id":"doi:10.3390/a19040247","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a19040247","pdf_url":"https://www.mdpi.com/1999-4893/19/4/247/pdf?version=1774425327","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"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":"Algorithms","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1999-4893/19/4/247/pdf?version=1774425327","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130549747","display_name":"Tingjuan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I3133134087","display_name":"Lanzhou Jiaotong University","ror":"https://ror.org/03144pv92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133134087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingjuan Wang","raw_affiliation_strings":["School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China"],"raw_orcid":"https://orcid.org/0009-0007-1044-6961","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China","institution_ids":["https://openalex.org/I3133134087"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130587823","display_name":"Jiuyuan Huo","orcid":null},"institutions":[{"id":"https://openalex.org/I3133134087","display_name":"Lanzhou Jiaotong University","ror":"https://ror.org/03144pv92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133134087"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiuyuan Huo","raw_affiliation_strings":["School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China"],"raw_orcid":"https://orcid.org/0000-0003-2395-4133","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China","institution_ids":["https://openalex.org/I3133134087"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5130594169","display_name":"Xinping Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinping Wu","raw_affiliation_strings":["China Telecom Wanwei Information Technology Co., Ltd., Lanzhou 730030, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Telecom Wanwei Information Technology Co., Ltd., Lanzhou 730030, China","institution_ids":["https://openalex.org/I4210136246"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5130587823"],"corresponding_institution_ids":["https://openalex.org/I3133134087"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3050031,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"19","issue":"4","first_page":"247","last_page":"247"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9587000012397766,"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":0.9587000012397766,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.029899999499320984,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0010999999940395355,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6140000224113464},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.6033999919891357},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5931000113487244},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5694000124931335},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5138999819755554},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4968000054359436},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.44600000977516174},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.3723999857902527}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6639999747276306},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6140000224113464},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.6033999919891357},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5931000113487244},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5694000124931335},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5138999819755554},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4968000054359436},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.385699987411499},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36010000109672546},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.33059999346733093},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30390000343322754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C188721877","wikidata":"https://www.wikidata.org/wiki/Q103510","display_name":"Bar (unit)","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/a19040247","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a19040247","pdf_url":"https://www.mdpi.com/1999-4893/19/4/247/pdf?version=1774425327","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"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":"Algorithms","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e6e166c449b140b498823cf420c3a73b","is_oa":true,"landing_page_url":"https://doaj.org/article/e6e166c449b140b498823cf420c3a73b","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Algorithms, Vol 19, Iss 4, p 247 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/a19040247","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a19040247","pdf_url":"https://www.mdpi.com/1999-4893/19/4/247/pdf?version=1774425327","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"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":"Algorithms","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8533449467","display_name":null,"funder_award_id":"62262038","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7140288943.pdf","grobid_xml":"https://content.openalex.org/works/W7140288943.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W96161441","https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2193145675","https://openalex.org/W2799323087","https://openalex.org/W2799391334","https://openalex.org/W2913119512","https://openalex.org/W2943686015","https://openalex.org/W2963125010","https://openalex.org/W2963150697","https://openalex.org/W2983866628","https://openalex.org/W3000609350","https://openalex.org/W3106763147","https://openalex.org/W3110311189","https://openalex.org/W3112798201","https://openalex.org/W3168326173","https://openalex.org/W3170866501","https://openalex.org/W3184003041","https://openalex.org/W3185768234","https://openalex.org/W4200150467","https://openalex.org/W4223453160","https://openalex.org/W4281780401","https://openalex.org/W4296038296","https://openalex.org/W4310506223","https://openalex.org/W4360609555","https://openalex.org/W4385604813","https://openalex.org/W4386076325","https://openalex.org/W4386934703","https://openalex.org/W4387418672","https://openalex.org/W4387503356","https://openalex.org/W4388747436","https://openalex.org/W4403770406","https://openalex.org/W4414038557","https://openalex.org/W7118669824"],"related_works":[],"abstract_inverted_index":{"To":[0],"meet":[1],"the":[2,30,35,48,67,98,128,138,152,154],"requirement":[3],"of":[4,38,117,124],"real-time":[5,189],"bridge":[6,147],"crack":[7,39,148,190],"detection,":[8],"this":[9],"paper":[10],"proposes":[11],"a":[12,43,52,145,188],"lightweight":[13],"detection":[14,49,84,132,191],"model":[15,140,160,176],"based":[16],"on":[17,144],"YOLOv7-tiny.":[18,103],"First,":[19],"an":[20,91],"edge-preserved":[21],"image":[22,31],"enhancement":[23],"method":[24,105],"is":[25,55,88,94,156,178,184],"proposed.":[26],"It":[27,65,110],"effectively":[28],"enhances":[29],"contrast":[32],"and":[33,62,71,86,159,164,171,182],"preserves":[34],"structural":[36],"features":[37,126],"edges.":[40],"This":[41,57,104,120],"provides":[42],"high-quality":[44],"data":[45],"foundation":[46],"for":[47,115,187],"network.":[50],"Second,":[51],"LWCSP":[53],"module":[54,58],"introduced.":[56],"integrates":[59],"hybrid":[60],"convolution":[61],"shuffle":[63],"operations.":[64],"reduces":[66],"model\u2019s":[68],"parameter":[69,162],"count":[70],"computation.":[72],"Simultaneously,":[73],"it":[74],"maintains":[75],"strong":[76,142],"feature":[77],"representation":[78],"capability.":[79],"A":[80],"good":[81],"balance":[82],"between":[83],"performance":[85,143],"efficiency":[87],"achieved.":[89],"Finally,":[90],"improved":[92],"SWise-IoU":[93],"proposed":[95,139],"to":[96,151],"optimize":[97],"bounding":[99],"box":[100],"regression":[101],"in":[102],"dynamically":[106],"evaluates":[107],"sample":[108,125],"quality.":[109],"enables":[111],"differentiated":[112],"gradient":[113],"adjustment":[114],"samples":[116],"different":[118],"qualities.":[119],"promotes":[121],"sufficient":[122],"learning":[123],"by":[127,168],"model,":[129],"thereby":[130],"improving":[131],"accuracy.":[133],"Experimental":[134],"results":[135],"show":[136],"that":[137],"delivers":[141],"public":[146],"dataset.":[149],"Compared":[150],"baseline,":[153],"mAP@0.5":[155,183],"12.1":[157],"higher,":[158],"size,":[161],"count,":[163],"FLOPs":[165],"are":[166],"reduced":[167],"7.3%,":[169],"8.03%,":[170],"10%,":[172],"respectively.":[173],"The":[174],"final":[175],"size":[177],"only":[179],"11.4":[180],"MB,":[181],"86.1%,":[185],"suitable":[186],"task.":[192]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-03-26T00:00:00"}
