{"id":"https://openalex.org/W4416249968","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228986","title":"Study of YOLOv8-Based Techniques for Detecting Road Surface Damage","display_name":"Study of YOLOv8-Based Techniques for Detecting Road Surface Damage","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249968","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228986"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228986","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228986","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5062762966","display_name":"Chen Yin","orcid":"https://orcid.org/0000-0003-1723-7728"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yin Chen","raw_affiliation_strings":["Xinjiang University,School of Software,Urumqi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Software,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020873493","display_name":"Jiong Yu","orcid":"https://orcid.org/0000-0002-9181-6720"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiong Yu","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology,Urumqi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100404309","display_name":"Shu Li","orcid":"https://orcid.org/0000-0001-8225-5426"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu Li","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology,Urumqi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I96908189"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.6269000172615051,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6269000172615051,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.31060001254081726,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.008100000210106373,"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/pyramid","display_name":"Pyramid (geometry)","score":0.5740000009536743},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.527999997138977},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5263000130653381},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.511900007724762},{"id":"https://openalex.org/keywords/road-surface","display_name":"Road surface","score":0.491100013256073},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.46799999475479126},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4205000102519989},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4083000123500824}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6230000257492065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5781000256538391},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5740000009536743},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.527999997138977},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5263000130653381},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.511900007724762},{"id":"https://openalex.org/C2780042925","wikidata":"https://www.wikidata.org/wiki/Q1049667","display_name":"Road surface","level":2,"score":0.491100013256073},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.46799999475479126},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.426800012588501},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4205000102519989},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4083000123500824},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.36579999327659607},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2572999894618988},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228986","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228986","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325419","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2102605133","https://openalex.org/W2193145675","https://openalex.org/W2570343428","https://openalex.org/W2949096641","https://openalex.org/W2963037989","https://openalex.org/W2963420686","https://openalex.org/W3034552520","https://openalex.org/W3035049382","https://openalex.org/W3039693589","https://openalex.org/W4289752563","https://openalex.org/W4293584584","https://openalex.org/W4296038296","https://openalex.org/W4386076325","https://openalex.org/W4391382562","https://openalex.org/W4391768953","https://openalex.org/W4400073289","https://openalex.org/W4400098420","https://openalex.org/W4400728523","https://openalex.org/W4402510982"],"related_works":[],"abstract_inverted_index":{"Road":[0],"damage":[1,49,68,106,143,162,179,226],"not":[2],"only":[3],"reduces":[4,165],"driving":[5],"comfort":[6],"but":[7],"also":[8],"poses":[9],"significant":[10,161,220],"risks":[11],"to":[12,16,85,89,129,141,160,175],"traffic":[13],"safety.":[14],"Due":[15],"the":[17,21,55,82,95,100,138,157,166,189,196,199,209,219],"wide":[18],"variation":[19],"in":[20,103,126,180,224],"size,":[22],"shape,":[23],"and":[24,33,47,60,164,171],"texture":[25],"of":[26,168,202,222],"road":[27,67,178,225],"damage,":[28],"traditional":[29],"manual":[30],"detection":[31,36,69,227],"methods":[32],"existing":[34],"object":[35],"algorithms":[37],"often":[38],"perform":[39],"inadequately,":[40],"particularly":[41],"when":[42],"confronted":[43],"with":[44],"complex":[45,181],"backgrounds":[46],"diverse":[48,145],"types.":[50],"To":[51],"address":[52],"these":[53],"problems,":[54],"YOLOv8n":[56,190],"algorithm":[57,63,191],"was":[58,70,124],"optimized":[59],"an":[61],"LRS-YOLO":[62,187,203,223],"designed":[64,125],"specifically":[65],"for":[66],"proposed.":[71],"The":[72,148],"Receptive-Field":[73],"Attention":[74,153],"Convolution":[75,121],"(RFAConv)":[76],"module":[77,123,155],"is":[78,108],"first":[79],"integrated":[80],"into":[81],"backbone":[83],"network":[84],"boost":[86],"its":[87,173],"responsiveness":[88],"subtle":[90],"local":[91],"damage.":[92],"By":[93],"using":[94],"adaptive":[96],"weight":[97],"generation":[98],"method,":[99],"model\u2019s":[101,139,158],"effectiveness":[102],"recognizing":[104],"small":[105],"patterns":[107],"significantly":[109],"increased.":[110],"Second,":[111],"a":[112],"Spatial":[113,118],"Pyramid":[114,119],"Pooling":[115],"Fully":[116],"Connected":[117],"Partial":[120],"(SPPFCSPPC)":[122],"this":[127,135],"study":[128],"provide":[130],"richer":[131],"multi-scale":[132],"feature":[133],"representations,":[134],"consequently":[136],"strengthens":[137],"ability":[140,174],"recognize":[142],"across":[144],"scales.":[146],"Finally,":[147],"proposed":[149],"Large":[150],"Kernel":[151],"Channel":[152],"(LKCA)":[154],"increases":[156],"attention":[159],"regions":[163],"effect":[167],"irrelevant":[169],"areas,":[170],"improve":[172],"accurately":[176],"identify":[177],"backgrounds.":[182],"Experimental":[183],"results":[184,217],"demonstrate":[185],"that":[186],"outperforms":[188],"on":[192,208],"multiple":[193],"datasets.":[194],"On":[195],"RDD2022":[197],"dataset,":[198,211],"mAP@50":[200],"score":[201],"increased":[204],"by":[205,214],"7.2%,":[206],"while":[207],"UAV-PDD2023":[210],"it":[212],"improved":[213],"2.8%.":[215],"These":[216],"underscore":[218],"advantages":[221],"tasks.":[228]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-14T00:00:00"}