{"id":"https://openalex.org/W4318147831","doi":"https://doi.org/10.1109/bigdata55660.2022.10020282","title":"Road Damage Detection using YOLO with Image Tiling about Multi-source Images","display_name":"Road Damage Detection using YOLO with Image Tiling about Multi-source Images","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147831","doi":"https://doi.org/10.1109/bigdata55660.2022.10020282"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020282","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5072873928","display_name":"Dongjun Jeong","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Dongjun Jeong","raw_affiliation_strings":["AI Lab Cell,Gangnam-gu, Seoul,Republic of Korea","AI Lab Cell, Gangnam-gu, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"AI Lab Cell,Gangnam-gu, Seoul,Republic of Korea","institution_ids":[]},{"raw_affiliation_string":"AI Lab Cell, Gangnam-gu, Seoul, Republic of Korea","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040358994","display_name":"Jua Kim","orcid":"https://orcid.org/0000-0002-3949-9019"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jua Kim","raw_affiliation_strings":["22, Yeongdong-daero,Gangnam-gu, Seoul,Republic of Korea","22, Yeongdong-daero, Gangnam-gu, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"22, Yeongdong-daero,Gangnam-gu, Seoul,Republic of Korea","institution_ids":[]},{"raw_affiliation_string":"22, Yeongdong-daero, Gangnam-gu, Seoul, Republic of Korea","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5072873928"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":10.85,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.99323077,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"6401","last_page":"6406"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9991000294685364,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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-vision","display_name":"Computer vision","score":0.6825301051139832},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6437016725540161},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6283795833587646},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4812747836112976},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.320173978805542}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6825301051139832},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6437016725540161},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6283795833587646},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4812747836112976},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.320173978805542}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020282","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2015861736","https://openalex.org/W2117539524","https://openalex.org/W2511065100","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2748643398","https://openalex.org/W2913930668","https://openalex.org/W2963037989","https://openalex.org/W2963857746","https://openalex.org/W3018757597","https://openalex.org/W3034059856","https://openalex.org/W3042011474","https://openalex.org/W3099452838","https://openalex.org/W3124942917","https://openalex.org/W3137017695","https://openalex.org/W3161660388","https://openalex.org/W4293584584","https://openalex.org/W4298289240","https://openalex.org/W4304689590","https://openalex.org/W4320024091","https://openalex.org/W6637568146","https://openalex.org/W6750227808","https://openalex.org/W6777046832","https://openalex.org/W6913011054"],"related_works":["https://openalex.org/W2005185696","https://openalex.org/W2161229648","https://openalex.org/W2235753890","https://openalex.org/W2993674027","https://openalex.org/W2130228941","https://openalex.org/W2092957489","https://openalex.org/W2366116130","https://openalex.org/W2314419244","https://openalex.org/W2132132164","https://openalex.org/W2889893736"],"abstract_inverted_index":{"The":[0],"importance":[1],"of":[2,20,129,135],"road":[3,26,62,69,92,118],"damage":[4,27,70,93,119],"detection":[5,71],"work":[6],"is":[7],"continuously":[8],"increasing,":[9],"and":[10,22,43,53,59,104,131],"various":[11,47],"methods":[12],"are":[13],"being":[14],"developed":[15],"to":[16,86,109],"reduce":[17],"the":[18,25,37,68,82,89,111,116],"cost":[19],"time":[21],"economy.":[23],"Therefore,":[24],"dataset":[28],"was":[29],"collected":[30],"from":[31],"six":[32],"countries,":[33],"such":[34,49],"as":[35,50],"China,":[36],"Czech":[38],"Republic,":[39],"India,":[40],"Japan,":[41],"Norway,":[42],"United":[44],"States,":[45],"by":[46],"sources":[48],"drones,":[51],"cars,":[52],"motorbikes":[54],"for":[55,114],"making":[56],"a":[57,75],"robust":[58],"powerful":[60],"automatic":[61],"state":[63],"monitoring":[64],"system.":[65],"We":[66,80],"solved":[67],"task":[72],"using":[73],"YOLO,":[74],"deep":[76],"learning":[77],"based":[78],"technology.":[79],"adopted":[81],"image":[83],"tiling":[84],"technique":[85],"properly":[87],"use":[88,110],"high":[90],"resolution":[91,102],"images":[94,103],"captured":[95],"in":[96],"Norway":[97],"with":[98],"other":[99],"similar":[100],"size":[101],"trained":[105],"twelve":[106],"YOLOv5x":[107],"models":[108],"ensemble":[112],"method":[113],"detecting":[115],"four":[117],"types.":[120],"Finally,":[121],"our":[122],"solution":[123],"obtained":[124],"an":[125,132],"average":[126],"F1":[127],"score":[128],"0.6744":[130],"inference":[133],"speed":[134],"1":[136],"FPS.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-24T08:02:53.985720","created_date":"2025-10-10T00:00:00"}
