{"id":"https://openalex.org/W4416250518","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229375","title":"A Study on Road Damage Detection Methods Based on YOLOv11","display_name":"A Study on Road Damage Detection Methods Based on YOLOv11","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250518","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229375"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229375","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":"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/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":true,"raw_author_name":"Yin Chen","raw_affiliation_strings":["Xinjiang University,School of Software,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Software,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","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"],"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":2,"corresponding_author_ids":["https://openalex.org/A5062762966"],"corresponding_institution_ids":["https://openalex.org/I96908189"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37331677,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.6504999995231628,"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":0.6504999995231628,"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":0.2012999951839447,"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":0.03830000013113022,"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/context","display_name":"Context (archaeology)","score":0.6169000267982483},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5404999852180481},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5160999894142151},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5076000094413757},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4796000123023987},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4643999934196472},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4499000012874603}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7197999954223633},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6169000267982483},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5404999852180481},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5160999894142151},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5076000094413757},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4796000123023987},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47450000047683716},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4643999934196472},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4499000012874603},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4453999996185303},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39399999380111694},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.35280001163482666},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2840999960899353},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.2685999870300293}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229375","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":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2102605133","https://openalex.org/W2193145675","https://openalex.org/W2565639579","https://openalex.org/W2949096641","https://openalex.org/W2963037989","https://openalex.org/W2963420686","https://openalex.org/W3034552520","https://openalex.org/W3035049382","https://openalex.org/W3177052299","https://openalex.org/W4383502363","https://openalex.org/W4386858688","https://openalex.org/W4391382562","https://openalex.org/W4391992602","https://openalex.org/W4400073289","https://openalex.org/W4400098420","https://openalex.org/W4401597853","https://openalex.org/W4402510633","https://openalex.org/W4402510982","https://openalex.org/W4402754006","https://openalex.org/W4404740660","https://openalex.org/W4405755728"],"related_works":[],"abstract_inverted_index":{"Road":[0],"damage":[1,45,157,212],"poses":[2],"a":[3,61,186,198],"significant":[4,205],"threat":[5],"to":[6,70,100,105,141,151],"traffic":[7],"safety.":[8],"However,":[9],"current":[10],"object":[11],"detection":[12,46,79],"models":[13],"often":[14],"underperform":[15],"when":[16],"faced":[17],"with":[18,60,97],"complex":[19],"backgrounds":[20],"and":[21,36,132,145,154,161],"diverse":[22,153],"types":[23],"of":[24,80,208],"damage.":[25,85,120],"To":[26],"address":[27],"these":[28],"limitations,":[29],"this":[30],"paper":[31],"enhances":[32],"the":[33,38,52,68,78,87,102,113,122,129,133,139,163,173,181,193,204],"YOLOv11n":[34,174],"algorithm":[35,140,175],"introduces":[37],"LRR-YOLO":[39,49,171,184,209],"algorithm,":[40],"specifically":[41],"designed":[42],"for":[43],"road":[44,84,119,156,211],"tasks.":[47],"The":[48],"backbone":[50],"integrates":[51],"ERFCBAMConv":[53],"module,":[54],"which":[55,92],"combines":[56,93],"multi-scale":[57,118],"feature":[58],"extraction":[59],"dynamic":[62],"attention":[63],"mechanism.":[64],"This":[65,110,136],"modification":[66],"enables":[67,138],"model":[69],"prioritize":[71],"key":[72],"features":[73],"more":[74],"effectively,":[75],"thereby":[76],"improving":[77,162],"fine":[81],"details":[82],"in":[83,116,158,189,210],"Additionally,":[86],"LSK":[88],"module":[89,124],"is":[90,125],"incorporated,":[91],"depthwise":[94],"separable":[95],"convolution":[96],"spatial":[98],"context":[99],"enhance":[101],"model\u2019s":[103,164],"ability":[104,150],"capture":[106,142],"global":[107,144],"contextual":[108],"information.":[109],"enhancement":[111],"improves":[112],"algorithm\u2019s":[114],"effectiveness":[115],"detecting":[117],"Moreover,":[121],"C3K2-RO":[123],"developed":[126],"by":[127],"integrating":[128],"RepViT":[130],"Block":[131],"ODConv":[134],"module.":[135],"combination":[137],"both":[143],"local":[146],"features,":[147],"enhancing":[148],"its":[149],"identify":[152],"irregular":[155],"challenging":[159],"environments":[160],"generalization":[165],"capability.":[166],"Experimental":[167],"results":[168],"demonstrate":[169],"that":[170],"outperforms":[172],"across":[176],"multiple":[177],"datasets.":[178],"Specifically,":[179],"on":[180,192],"RDD2022":[182],"dataset,":[183,195],"achieves":[185],"5.2%":[187],"improvement":[188],"mAP@50,":[190],"while":[191],"GRDDC2020":[194],"it":[196],"shows":[197],"4.5%":[199],"increase.":[200],"These":[201],"findings":[202],"underscore":[203],"performance":[206],"gains":[207],"detection.":[213]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}