{"id":"https://openalex.org/W4410771612","doi":"https://doi.org/10.1109/cacml64929.2025.11010956","title":"MC-YOLO: A Context Aware Down Sampling Algorithm for Construction Site Object Detection","display_name":"MC-YOLO: A Context Aware Down Sampling Algorithm for Construction Site Object Detection","publication_year":2025,"publication_date":"2025-03-28","ids":{"openalex":"https://openalex.org/W4410771612","doi":"https://doi.org/10.1109/cacml64929.2025.11010956"},"language":"en","primary_location":{"id":"doi:10.1109/cacml64929.2025.11010956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cacml64929.2025.11010956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 4th Asia Conference on Algorithms, Computing and Machine Learning (CACML)","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/A5112041197","display_name":"Dandan Qi","orcid":"https://orcid.org/0000-0001-8023-156X"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dandan Qi","raw_affiliation_strings":["South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113777665","display_name":"Weiyu Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiyu Yu","raw_affiliation_strings":["South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076517151","display_name":"Qimin Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qimin Chen","raw_affiliation_strings":["South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5112041197"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14066705,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9873999953269958,"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":0.9873999953269958,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9638000130653381,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9609000086784363,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7747691869735718},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5741848945617676},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5068183541297913},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4947151839733124},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4526776969432831},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41228196024894714},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3980570137500763},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37751176953315735},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.18207716941833496},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06590500473976135}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7747691869735718},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5741848945617676},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5068183541297913},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4947151839733124},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4526776969432831},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41228196024894714},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3980570137500763},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37751176953315735},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.18207716941833496},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06590500473976135},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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/cacml64929.2025.11010956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cacml64929.2025.11010956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 4th Asia Conference on Algorithms, Computing and Machine Learning (CACML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.46000000834465027,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2037364857","https://openalex.org/W2102605133","https://openalex.org/W2193145675","https://openalex.org/W2790722345","https://openalex.org/W2885020160","https://openalex.org/W2963037989","https://openalex.org/W3096100049","https://openalex.org/W3096609285","https://openalex.org/W4282919254","https://openalex.org/W4288987448","https://openalex.org/W4366577357","https://openalex.org/W4393066019","https://openalex.org/W4395067547","https://openalex.org/W4403865003"],"related_works":["https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W4409439182","https://openalex.org/W2053286651","https://openalex.org/W2181743346","https://openalex.org/W2187401768","https://openalex.org/W2181413294","https://openalex.org/W2052122378","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"Safety":[0],"accidents":[1],"at":[2,26],"construction":[3,27,40,62,69,95],"sites":[4,28],"not":[5,154],"only":[6,155],"pose":[7,78],"a":[8,109,192,197],"threat":[9],"to":[10,18,36,56,80,117,126],"the":[11,65,68,73,99,119,128,157,166,176,182,208,213],"lives":[12],"of":[13,67,75,101,133,150,168,194,199,215],"workers":[14],"but":[15,162],"also":[16,163],"lead":[17],"significant":[19],"economic":[20],"losses.":[21],"Therefore,":[22],"strengthening":[23],"safety":[24,42],"management":[25],"has":[29],"become":[30],"an":[31,85,135],"issue":[32],"that":[33,175],"urgently":[34],"needs":[35],"be":[37],"addressed.":[38],"Traditional":[39],"site":[41,70],"relies":[43],"on":[44,181],"manual":[45],"inspection":[46],"methods":[47],"with":[48,142],"low":[49],"efficiency.":[50],"Computer":[51],"vision":[52],"makes":[53],"it":[54],"possible":[55],"conduct":[57],"automatic":[58],"target":[59],"detection":[60,76,93,131],"in":[61,94,107,124],"site.":[63,96],"However,":[64],"complexity":[66],"environment":[71],"and":[72,184,196,204,212],"diversity":[74],"targets":[77],"challenges":[79],"detection.":[81],"This":[82],"paper":[83],"proposes":[84],"improved":[86,136],"YOLOv8":[87],"model":[88,169,178,190,216],"called":[89],"MC-YOLO":[90,177],"for":[91],"object":[92,130],"To":[97],"overcome":[98],"problem":[100],"information":[102],"loss":[103],"caused":[104],"by":[105,220],"down-sampling":[106,121],"YOLOv8,":[108,134],"CADS":[110],"(Content":[111],"Aware":[112],"Down-Sampling)":[113],"block":[114,138,145],"is":[115,146,218],"proposed":[116],"replace":[118],"original":[120,209],"block.":[122],"Meanwhile,":[123],"order":[125],"enhance":[127],"multi-scale":[129],"ability":[132,161],"C2f":[137],"named":[139],"C2f-MSC":[140],"(C2f":[141],"Multi-Scale":[143],"Convolution)":[144],"introduced.":[147],"The":[148,171,188],"addition":[149],"these":[151],"two":[152],"blocks":[153],"improves":[156],"model\u2019s":[158],"feature":[159],"extraction":[160],"significantly":[164],"reduces":[165],"number":[167,214],"parameters.":[170],"experimental":[172],"results":[173],"show":[174],"performs":[179],"well":[180],"CSS":[183],"SODA":[185],"datasets.":[186],"Specifically,":[187],"MC-YOLOs":[189],"achieves":[191],"mAP@0.5":[193],"80.8%":[195],"precision":[198],"91.9%,":[200],"which":[201],"are":[202],"1.5%":[203],"3.9%":[205],"higher":[206],"than":[207],"algorithm":[210],"respectively,":[211],"parameters":[217],"reduced":[219],"21.09%.":[221]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}