{"id":"https://openalex.org/W4406458809","doi":"https://doi.org/10.1109/bigdata62323.2024.10825391","title":"Optimizing Road Damage Detection with YOLOv10: A Resource-Efficient Approach Utilizing Augmentation, Data Sampling, and Hyperparameter Tuning","display_name":"Optimizing Road Damage Detection with YOLOv10: A Resource-Efficient Approach Utilizing Augmentation, Data Sampling, and Hyperparameter Tuning","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406458809","doi":"https://doi.org/10.1109/bigdata62323.2024.10825391"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5044802071","display_name":"Ashkan Behzadian","orcid":"https://orcid.org/0000-0002-1053-9953"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ashkan Behzadian","raw_affiliation_strings":["University of Missouri,Department of Civil and Environmental Engineering,Columbia,MO,USA"],"affiliations":[{"raw_affiliation_string":"University of Missouri,Department of Civil and Environmental Engineering,Columbia,MO,USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043031204","display_name":"Tanner Muturi","orcid":"https://orcid.org/0000-0001-8388-9092"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tanner Muturi","raw_affiliation_strings":["University of Missouri,Department of Civil and Environmental Engineering,Columbia,MO,USA"],"affiliations":[{"raw_affiliation_string":"University of Missouri,Department of Civil and Environmental Engineering,Columbia,MO,USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059846131","display_name":"Neema Jakisa Owor","orcid":"https://orcid.org/0009-0007-2934-8990"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Neema Jakisa Owor","raw_affiliation_strings":["University of Missouri,Department of Civil and Environmental Engineering,Columbia,MO,USA"],"affiliations":[{"raw_affiliation_string":"University of Missouri,Department of Civil and Environmental Engineering,Columbia,MO,USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069415522","display_name":"Yaw Adu\u2010Gyamfi","orcid":"https://orcid.org/0000-0002-1924-9792"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yaw Adu-Gyamfi","raw_affiliation_strings":["University of Missouri,Department of Civil and Environmental Engineering,Columbia,MO,USA"],"affiliations":[{"raw_affiliation_string":"University of Missouri,Department of Civil and Environmental Engineering,Columbia,MO,USA","institution_ids":["https://openalex.org/I76835614"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5044802071"],"corresponding_institution_ids":["https://openalex.org/I76835614"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29436554,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8439","last_page":"8446"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9939000010490417,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/hyperparameter","display_name":"Hyperparameter","score":0.8314264416694641},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7305989265441895},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5798077583312988},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.5235083699226379},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41026031970977783},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3664278984069824},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10380548238754272}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8314264416694641},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7305989265441895},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5798077583312988},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.5235083699226379},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41026031970977783},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3664278984069824},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10380548238754272},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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":39,"referenced_works":["https://openalex.org/W2913742577","https://openalex.org/W2944441395","https://openalex.org/W2963037989","https://openalex.org/W3034059856","https://openalex.org/W3040786507","https://openalex.org/W3093788378","https://openalex.org/W3099452838","https://openalex.org/W3136219530","https://openalex.org/W3136785150","https://openalex.org/W3138078591","https://openalex.org/W3161660388","https://openalex.org/W3189632814","https://openalex.org/W3200966602","https://openalex.org/W4282813695","https://openalex.org/W4296544680","https://openalex.org/W4309047390","https://openalex.org/W4318677156","https://openalex.org/W4320024041","https://openalex.org/W4320024077","https://openalex.org/W4320024091","https://openalex.org/W4376140744","https://openalex.org/W4377089756","https://openalex.org/W4384927866","https://openalex.org/W4386363076","https://openalex.org/W4387586878","https://openalex.org/W4387636022","https://openalex.org/W4387816198","https://openalex.org/W4390628502","https://openalex.org/W4392544261","https://openalex.org/W4394415098","https://openalex.org/W4398810114","https://openalex.org/W4400594501","https://openalex.org/W4401070086","https://openalex.org/W4402099957","https://openalex.org/W6676602567","https://openalex.org/W6838816276","https://openalex.org/W6843295864","https://openalex.org/W6857084072","https://openalex.org/W6868582632"],"related_works":["https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W2602382373","https://openalex.org/W3003615511","https://openalex.org/W4285827128","https://openalex.org/W3198113463","https://openalex.org/W2787698406","https://openalex.org/W2963844355","https://openalex.org/W4361251046","https://openalex.org/W98577079"],"abstract_inverted_index":{"Efficient":[0],"road":[1,16,57,116],"damage":[2,58],"detection":[3,50,59],"is":[4],"key":[5],"for":[6,14,32,89],"timely":[7],"maintenance":[8],"and":[9,52,92,102,112,141],"decreasing":[10],"long-term":[11],"costs,":[12],"especially":[13],"large-scale":[15],"networks.":[17],"While":[18],"existing":[19],"models":[20],"achieve":[21],"high":[22],"accuracy,":[23],"they":[24],"often":[25],"overlook":[26],"resource":[27],"optimization,":[28],"making":[29],"them":[30],"impractical":[31],"widespread":[33],"deployment.":[34],"In":[35],"this":[36],"study,":[37],"we":[38,134],"propose":[39],"an":[40,136,142],"optimized":[41],"approach":[42],"using":[43],"the":[44,109,125],"YOLOv10":[45],"model,":[46],"designed":[47],"to":[48,64,124],"balance":[49],"accuracy":[51],"inference":[53,143],"speed,":[54],"enabling":[55],"scalable":[56],"across":[60,83,114],"diverse":[61,78],"regions.":[62],"Key":[63],"our":[65],"success":[66],"were":[67],"several":[68],"optimization":[69],"techniques,":[70],"including":[71],"strategic":[72],"data":[73,94],"sampling":[74],"from":[75],"a":[76],"large,":[77],"dataset":[79],"of":[80,139,145],"47,420":[81],"images":[82],"six":[84],"countries,":[85],"automated":[86],"hyperparameter":[87],"tuning":[88],"10,000":[90],"epochs,":[91],"advanced":[93],"augmentation":[95],"methods":[96],"such":[97],"as":[98],"Bounding-Box-Safe":[99],"Random":[100],"Crop":[101],"Optimized":[103,126],"Test-Time":[104],"Augmentation.":[105],"These":[106],"strategies":[107],"enhanced":[108],"model\u2019s":[110],"robustness":[111],"generalization":[113],"varying":[115],"damages.":[117],"Our":[118],"model":[119],"was":[120],"developed":[121],"in":[122],"response":[123],"Road":[127],"Damage":[128],"Detection":[129],"Challenge":[130],"(ORDDC)":[131],"2024,":[132],"where":[133],"attained":[135],"F1":[137],"score":[138],"0.7073":[140],"speed":[144],"0.2886,":[146],"securing":[147],"5th":[148],"place.":[149]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
