Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2016: (UTC) administrator
Metadata Last Update2017: (UTC) administrator
Citation KeyRochaMeneOliv:2016:DeAuMi
TitleDetecção Automática de Microcomponentes SMT Ausentes em Placas de Circuito Impresso
Access Date2022, Jan. 19
Number of Files1
Size2778 KiB
Context area
Author1 Rocha, Cleandro de Souza
2 Menezes, Mathias Afonso Guedes de
3 Oliveira, Felipe Gomes de
Affiliation1 Federal University of Amazoas
2 Federal University of Amazonas
3 Federal University of Amazonas
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos
DateOct. 4-7, 2016
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeIndustry Application Paper
History (UTC)2016-09-02 21:35:23 :: -> administrator :: 2016
2017-08-10 23:44:47 :: administrator -> :: 2016
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
KeywordsVisão de Máquina
Aprendizado de Máquina
Inspeção Industrial
Controle de Qualidade
AbstractThis work presents a visual inspection approach to detect absence/presence of surface mount components (SMC) on printed circuit boards (PCB). We propose a methodology based on the combination of Machine Vision and Machine Learning to detect component absence, with more quality and precision, using noised digital images acquired directly from PCB industrial production line. The applicability of method was tested for automatic visual inspection in motherboards, where the demand of these components is high. The results obtained demonstrates the robustness of our methodology in images with high levels of gaussian and salt and pepper noise, obtaining 97.25% of hit rate. > SDLA > SIBGRAPI 2016 > Detecção Automática de...
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Target FileSibgrapi2016_CameraReady.pdf
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Next Higher Units8JMKD3MGPAW/3M2D4LP
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