{"id":"https://openalex.org/W4413205802","doi":"https://doi.org/10.1109/eaic66483.2025.11101424","title":"TR-YOLO: An Enhanced Model for PCB Mixed Defect Detection and Classification","display_name":"TR-YOLO: An Enhanced Model for PCB Mixed Defect Detection and Classification","publication_year":2025,"publication_date":"2025-06-05","ids":{"openalex":"https://openalex.org/W4413205802","doi":"https://doi.org/10.1109/eaic66483.2025.11101424"},"language":"en","primary_location":{"id":"doi:10.1109/eaic66483.2025.11101424","doi":"https://doi.org/10.1109/eaic66483.2025.11101424","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eaic66483.2025.11101424","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Electronics, AI and Computing (EAIC)","raw_type":"proceedings-article"},"volume":null,"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/A5099842865","display_name":"Vinod Kumar Ancha","orcid":"https://orcid.org/0009-0003-2661-1408","observed_orcids":["https://orcid.org/0009-0003-2661-1408"]},"institutions":[{"id":"https://openalex.org/I4210100463","display_name":"SRM University, Andhra Pradesh","ror":"https://ror.org/013vs5h31","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210100463"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vinod Kumar Ancha","raw_affiliation_strings":["SRM University,Department of Electronics and Communication Engineering,Andhra Pradesh,India,522503"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SRM University,Department of Electronics and Communication Engineering,Andhra Pradesh,India,522503","institution_ids":["https://openalex.org/I4210100463"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014560064","display_name":"Venkateswarlu Gonuguntla","orcid":"https://orcid.org/0000-0002-6857-2254","observed_orcids":["https://orcid.org/0000-0002-6857-2254"]},"institutions":[{"id":"https://openalex.org/I244572783","display_name":"Symbiosis International University","ror":"https://ror.org/005r2ww51","country_code":"IN","type":"education","lineage":["https://openalex.org/I244572783"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Venkateswarlu Gonuguntla","raw_affiliation_strings":["Symbiosis Centre for Medical Image Analysis, Symbiosis International (Deemed University),Pune,India,412115"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Symbiosis Centre for Medical Image Analysis, Symbiosis International (Deemed University),Pune,India,412115","institution_ids":["https://openalex.org/I244572783"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086107411","display_name":"Ramesh Vaddi","orcid":"https://orcid.org/0000-0003-3158-4013","observed_orcids":["https://orcid.org/0000-0003-3158-4013"]},"institutions":[{"id":"https://openalex.org/I4210100463","display_name":"SRM University, Andhra Pradesh","ror":"https://ror.org/013vs5h31","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210100463"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ramesh Vaddi","raw_affiliation_strings":["SRM University,Department of Electronics and Communication Engineering,Andhra Pradesh,India,522503"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SRM University,Department of Electronics and Communication Engineering,Andhra Pradesh,India,522503","institution_ids":["https://openalex.org/I4210100463"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4725,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.86302629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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":0.0,"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":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pcb-defect-detection","display_name":"PCB defect detection","score":0.8029999732971191},{"id":"https://openalex.org/keywords/yolov5","display_name":"YOLOv5","score":0.953000009059906},{"id":"https://openalex.org/keywords/multiscale-feature-extraction","display_name":"multi-scale feature extraction","score":0.8379999995231628},{"id":"https://openalex.org/keywords/lightweight-object-detection","display_name":"lightweight object detection","score":0.7680000066757202},{"id":"https://openalex.org/keywords/automated-optical-inspection","display_name":"automated optical inspection","score":0.9290000200271606},{"id":"https://openalex.org/keywords/real-time-inspection","display_name":"real-time inspection","score":0.6129999756813049},{"id":"https://openalex.org/keywords/printed-circuit-board-manufacturing","display_name":"printed circuit board manufacturing","score":0.8029999732971191}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5594547986984253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4456157684326172},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.422633558511734}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/eaic66483.2025.11101424","doi":"https://doi.org/10.1109/eaic66483.2025.11101424","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eaic66483.2025.11101424","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Electronics, AI and Computing (EAIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5378999710083008}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2044191976","https://openalex.org/W2089853160","https://openalex.org/W2120270858","https://openalex.org/W2565639579","https://openalex.org/W2914237854","https://openalex.org/W2922005503","https://openalex.org/W2949202705","https://openalex.org/W2963857746","https://openalex.org/W3013211776","https://openalex.org/W3033847194","https://openalex.org/W3035006120","https://openalex.org/W3048213795","https://openalex.org/W3113968199","https://openalex.org/W4226323522","https://openalex.org/W4286582003","https://openalex.org/W4297676427","https://openalex.org/W4313064927","https://openalex.org/W4378965951","https://openalex.org/W4386076325","https://openalex.org/W4400770913","https://openalex.org/W4415795831","https://openalex.org/W6862000706","https://openalex.org/W6868582632","https://openalex.org/W6872123822"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Accurate":[0],"detection":[1,140],"and":[2,18,83,108,123,142,165],"classification":[3],"of":[4,20,30,77],"mixed":[5],"defects":[6],"in":[7,157],"printed":[8],"circuit":[9],"board":[10],"(PCB)":[11],"are":[12],"critical":[13],"for":[14,37,151],"maintaining":[15],"the":[16,31,66,89,92,125],"quality":[17,164],"reliability":[19],"electronic":[21],"product.":[22],"This":[23],"paper":[24],"presents":[25],"TR-YOLO,":[26],"an":[27],"enhanced":[28],"version":[29],"YOLOv5":[32,90],"model,":[33,91],"which":[34],"is":[35],"developed":[36],"PCB":[38,158],"defect":[39],"inspection.":[40],"TR-YOLO":[41,70,94],"introduces":[42],"a":[43,48,72,136],"modified":[44],"backbone":[45],"structure":[46],"incorporating":[47],"C3TR":[49],"module,":[50],"enabling":[51],"effective":[52],"extracting":[53],"features":[54],"at":[55],"multiple":[56],"scales":[57],"while":[58],"minimizing":[59],"computational":[60,116],"resources":[61],"overhead.":[62],"The":[63,145],"findings":[64,130],"from":[65],"experiments":[67],"indicate":[68],"that":[69],"attains":[71],"mean":[73],"average":[74],"precision":[75,99],"(mAP)":[76],"96.4%,":[78],"with":[79],"4.1":[80],"giga":[81],"FLOPs":[82],"1.80":[84],"million":[85],"parameters.":[86],"Compared":[87],"to":[88,134,161],"proposed":[93,146],"improves":[95],"mAP":[96],"by":[97,100,103,106,111,118,127],"0.63%,":[98],"0.73%,":[101],"recall":[102],"1.04%,":[104],"F1-score":[105],"1.34%,":[107],"overall":[109],"accuracy":[110,141],"0.17%.":[112],"Moreover,":[113],"it":[114],"reduces":[115],"complexity":[117],"2.38%":[119],"(i.e.,":[120],"reduced":[121],"FLOPs)":[122],"enhances":[124],"speed":[126],"7.61%.":[128],"These":[129],"highlight":[131],"TR-YOLO\u2019s":[132],"ability":[133],"deliver":[135],"balanced":[137],"performance":[138],"between":[139],"processing":[143],"efficiency.":[144],"model":[147],"shows":[148],"strong":[149],"potential":[150],"real-time":[152],"automatic":[153],"optical":[154],"inspection":[155],"(AOI)":[156],"manufacturing,":[159],"contributing":[160],"improved":[162],"product":[163],"production":[166],"reliability.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-10-10T00:00:00"}
